Search results for: imbalance dataset

Authors: Indra Bahadur Chand

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This paper will focus on the need for development and application of global protocols and policy in planning, designing, development, and management of systems of eco-towns and eco-villages so that sustainable development will be assured from the perspective of environmental, economical, peace, and harmonized social dynamics. This perspective is essential for the development of civilized and eco-friendly human settlements in the town and rural areas of the nation that will be a milestone for developing a happy and sustainable lifestyle of rural and urban communities of the nation. The urban population of most of the town of developing economies has been tremendously increasing, whereas rural people have been tremendously migrating for the past three decades. Consequently, the urban lifestyle in most towns has stressed in terms of environmental pollution, water crisis, congested traffic, energy crisis, food crisis, and unemployment. Eco-towns and villages should be developed where lifestyle of all residents is sustainable and happy. Built up environment of settlement should reduce and minimize the problems of non ecological CO2 emissions, unbalanced utilization of natural resources, environmental degradation, natural calamities, ecological imbalance, energy crisis, water scarcity, waste management, food crisis, unemployment, deterioration of cultural heritage, social, the ratio among the public and private land ownership, ratio of land covered with vegetation and area of settlement, the ratio of people in the vehicles and foot, the ratio of people employed outside of town and village, ratio of resources recycling of waste materials, water consumption level, the ratio of people and vehicles, ratio of the length of the road network and area of town/villages, a ratio of renewable energy consumption with total energy, a ratio of religious/recreational area out of the total built-up area, the ratio of annual suicide case out of total people, a ratio of annual injured and death out of total people from a traffic accident, a ratio of production of agro foods within town out of total food consumption will be used to assist in designing and monitoring of each eco-towns and villages. An eco-town and villages should be planned and developed to offer sustainable infrastructure and utilities that maintain CO2 level in individual homes and settlements, home energy use, transport, food and consumer goods, water supply, waste management, conservation of historical heritages, healthy neighborhood, conservation of natural landscape, conserving bio-diversity and developing green infrastructures. Eco-towns and villages should be developed on the basis of master planning and architecture that affect and define the settlement and its form. Master planning and engineering should focus in delivering the sustainability criteria of eco towns and eco village. This will involve working with specific landscape and natural resources of locality.

Keywords: eco-town, ecological habitation, master plan, sustainable development

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Authors: Amir Shahab Shahabi, Mohsen Hasirian

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Modern societies, marked by expansive Internet connectivity and the rise of e-commerce, are now integrated with digital platforms at an unprecedented level. The efficiency, speed, and accessibility of e-commerce have garnered a substantial consumer base. Against this backdrop, electronic banking has undergone rapid proliferation within the realm of online activities. However, this growth has inadvertently given rise to an environment conducive to illicit activities, notably electronic payment fraud, posing a formidable challenge to the domain of electronic banking. A pivotal role in upholding the integrity of electronic commerce and business transactions is played by electronic fraud detection, particularly in the context of credit cards which underscores the imperative of comprehensive research in this field. To this end, our study introduces an Attentional Bi-Stream Sequence Learner (AttBiSeL) framework that leverages attention mechanisms and recurrent networks. By incorporating bidirectional recurrent layers, specifically bidirectional Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) layers, the proposed model adeptly extracts past and future transaction sequences while accounting for the temporal flow of information in both directions. Moreover, the integration of an attention mechanism accentuates specific transactions to varying degrees, as manifested in the output of the recurrent networks. The effectiveness of the proposed approach in automatic credit card fraud classification is evaluated on the European Cardholders' Fraud Dataset. Empirical results validate that the hybrid architectural paradigm presented in this study yields enhanced accuracy compared to previous studies.

Keywords: credit card fraud, deep learning, attention mechanism, recurrent neural networks

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Authors: Dmytro Zubov, Francesco Volponi

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In this paper, D-Wave quantum computing Ising model is used for the forecasting of positive extremes of daily mean air temperature. Forecast models are designed with two to five qubits, which represent 2-, 3-, 4-, and 5-day historical data respectively. Ising model’s real-valued weights and dimensionless coefficients are calculated using daily mean air temperatures from 119 places around the world, as well as sea level (Aburatsu, Japan). In comparison with current methods, this approach is better suited to predict heat wave values because it does not require the estimation of a probability distribution from scarce observations. Proposed forecast quantum computing algorithm is simulated based on traditional computer architecture and combinatorial optimization of Ising model parameters for the Ronald Reagan Washington National Airport dataset with 1-day lead-time on learning sample (1975-2010 yr). Analysis of the forecast accuracy (ratio of successful predictions to total number of predictions) on the validation sample (2011-2014 yr) shows that Ising model with three qubits has 100 % accuracy, which is quite significant as compared to other methods. However, number of identified heat waves is small (only one out of nineteen in this case). Other models with 2, 4, and 5 qubits have 20 %, 3.8 %, and 3.8 % accuracy respectively. Presented three-qubit forecast model is applied for prediction of heat waves at other five locations: Aurel Vlaicu, Romania – accuracy is 28.6 %; Bratislava, Slovakia – accuracy is 21.7 %; Brussels, Belgium – accuracy is 33.3 %; Sofia, Bulgaria – accuracy is 50 %; Akhisar, Turkey – accuracy is 21.4 %. These predictions are not ideal, but not zeros. They can be used independently or together with other predictions generated by different method(s). The loss of human life, as well as environmental, economic, and material damage, from extreme air temperatures could be reduced if some of heat waves are predicted. Even a small success rate implies a large socio-economic benefit.

Keywords: heat wave, D-wave, forecast, Ising model, quantum computing

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Authors: Abdulaziz Alsadhan, Naveed Khan

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In recent years intrusions on computer network are the major security threat. Hence, it is important to impede such intrusions. The hindrance of such intrusions entirely relies on its detection, which is primary concern of any security tool like Intrusion Detection System (IDS). Therefore, it is imperative to accurately detect network attack. Numerous intrusion detection techniques are available but the main issue is their performance. The performance of IDS can be improved by increasing the accurate detection rate and reducing false positive. The existing intrusion detection techniques have the limitation of usage of raw data set for classification. The classifier may get jumble due to redundancy, which results incorrect classification. To minimize this problem, Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Local Binary Pattern (LBP) can be applied to transform raw features into principle features space and select the features based on their sensitivity. Eigen values can be used to determine the sensitivity. To further classify, the selected features greedy search, back elimination, and Particle Swarm Optimization (PSO) can be used to obtain a subset of features with optimal sensitivity and highest discriminatory power. These optimal feature subset used to perform classification. For classification purpose, Support Vector Machine (SVM) and Multilayer Perceptron (MLP) used due to its proven ability in classification. The Knowledge Discovery and Data mining (KDD’99) cup dataset was considered as a benchmark for evaluating security detection mechanisms. The proposed approach can provide an optimal intrusion detection mechanism that outperforms the existing approaches and has the capability to minimize the number of features and maximize the detection rates.

Keywords: Particle Swarm Optimization (PSO), Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA), Local Binary Pattern (LBP), Support Vector Machine (SVM), Multilayer Perceptron (MLP)

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Authors: A. M. John-Otumu, M. M. Rahman, O. C. Nwokonkwo, M. C. Onuoha

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Online social media networks have long served as a primary arena for group conversations, gossip, text-based information sharing and distribution. The use of natural language processing techniques for text classification and unbiased decision-making has not been far-fetched. Proper classification of this textual information in a given context has also been very difficult. As a result, we decided to conduct a systematic review of previous literature on sentiment classification and AI-based techniques that have been used in order to gain a better understanding of the process of designing and developing a robust and more accurate sentiment classifier that can correctly classify social media textual information of a given context between hate speech and inverted compliments with a high level of accuracy by assessing different artificial intelligence techniques. We evaluated over 250 articles from digital sources like ScienceDirect, ACM, Google Scholar, and IEEE Xplore and whittled down the number of research to 31. Findings revealed that Deep learning approaches such as CNN, RNN, BERT, and LSTM outperformed various machine learning techniques in terms of performance accuracy. A large dataset is also necessary for developing a robust sentiment classifier and can be obtained from places like Twitter, movie reviews, Kaggle, SST, and SemEval Task4. Hybrid Deep Learning techniques like CNN+LSTM, CNN+GRU, CNN+BERT outperformed single Deep Learning techniques and machine learning techniques. Python programming language outperformed Java programming language in terms of sentiment analyzer development due to its simplicity and AI-based library functionalities. Based on some of the important findings from this study, we made a recommendation for future research.

Keywords: artificial intelligence, natural language processing, sentiment analysis, social network, text

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Authors: Mathew Wakefield, Matthew Mitchell, Lisa Wise, Christopher McCarthy

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The properties of memory representations in artificial neural networks have cognitive implications. Distributed representations that encode instances as a pattern of activity across layers of nodes afford memory compression and enforce the selection of a single point in instance space. These encoding schemes also appear to distort the representational space, as well as trading off the ability to validate that input information is within the bounds of past experience. In contrast, a localist representation which encodes some meaningful information into individual nodes in a network layer affords less memory compression while retaining the integrity of the representational space. This allows the validity of an input to be determined. The validity (or familiarity) of input along with the capacity of localist representation for multiple instance selections affords a memory sampling approach that dynamically balances the bias-variance trade-off. When the input is familiar, bias may be high by referring only to the most similar instances in memory. When the input is less familiar, variance can be increased by referring to more instances that capture a broader range of features. Using this approach in a localist instance memory network, an experiment demonstrates a relationship between representational conflict, generalization performance, and memorization demand. Relatively small sampling ranges produce the best performance on a classic machine learning dataset of visual objects. Combining memory validity with conflict detection produces a reliable confidence judgement that can separate responses with high and low error rates. Confidence can also be used to signal the need for supervisory input. Using this judgement, the need for supervised learning as well as memory encoding can be substantially reduced with only a trivial detriment to classification performance.

Keywords: artificial neural networks, representation, memory, conflict monitoring, confidence

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Authors: Taoues Hadour

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Twitter has become a popular means of communication used in a variety of fields, such as politics, journalism, and academia. This widely used online platform has an impact on the way people express themselves and is changing language usage worldwide at an unprecedented pace. The language used online reflects the linguistic battle that has been going on for several decades in French society. This study enables a deeper understanding of users' linguistic behavior online. The implications are important and allow for a rise in awareness of intercultural and cross-language exchanges. This project investigates the mixing of French-English language usage among French users of Twitter using a topic analysis approach. This analysis draws on Gumperz's theory of conversational switching. In order to collect tweets at a large scale, the data was collected in R using the rtweet package to access and retrieve French tweets data through Twitter’s REST and stream APIs (Application Program Interface) using the software RStudio, the integrated development environment for R. The dataset was filtered manually and certain repetitions of themes were observed. A total of nine topic categories were identified and analyzed in this study: entertainment, internet/social media, events/community, politics/news, sports, sex/pornography, innovation/technology, fashion/make up, and business. The study reveals that entertainment is the most frequent topic discussed on Twitter. Entertainment includes movies, music, games, and books. Anglicisms such as trailer, spoil, and live are identified in the data. Change in language usage is inevitable and is a natural result of linguistic interactions. The use of different languages online is just an example of what the real world would look like without linguistic regulations. Social media reveals a multicultural and multilinguistic richness which can deepen and expand our understanding of contemporary human attitudes.

Keywords: code-switching, French, sociolinguistics, Twitter

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Authors: Michael A. Unbehauen, Gregory D. Sloan, Alberto J. Squatrito

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Missile proliferation and global stability are intrinsically linked. Missile threats continually appear at the forefront of global security issues. North Korea’s recently demonstrated nuclear and intercontinental ballistic missile (ICBM) capabilities, for the first time since the Cold War, renewed public interest in strategic missile defense capabilities. To protect from limited ICBM attacks from so-called rogue actors, the United States developed the Ground-based Midcourse Defense (GMD) system. This study examines if the GMD missile defense shield has contributed to a safer world or triggered a new arms race. Based upon increased missile-related developments and the lack of adherence to international missile treaties, it is generally perceived that the GMD system is a destabilizing factor for global security. By examining the current state of arms control treaties as well as existing missile arsenals and ongoing efforts in technologies to overcome U.S. missile defenses, this study seeks to analyze the contribution of GMD to global stability. A thorough investigation cannot ignore that, through the establishment of this limited capability, the U.S. violated longstanding, successful weapons treaties and caused concern among states that possess ICBMs. GMD capability contributes to the perception that ICBM arsenals could become ineffective, creating an imbalance in favor of the United States, leading to increased global instability and tension. While blame for the deterioration of global stability and non-adherence to arms control treaties is often placed on U.S. missile defense, the facts do not necessarily support this view. The notion of a renewed arms race due to GMD is supported neither by current missile arsenals nor by the inevitable development of new and enhanced missile technology, to include multiple independently targeted reentry vehicles (MIRVs), maneuverable reentry vehicles (MaRVs), and hypersonic glide vehicles (HGVs). The methodology in this study encapsulates a period of time, pre- and post-GMD introduction, while analyzing international treaty adherence, missile counts and types, and research in new missile technologies. The decline in international treaty adherence, coupled with a measurable increase in the number and types of missiles or research in new missile technologies during the period after the introduction of GMD, could be perceived as a clear indicator of GMD contributing to global instability. However, research into improved technology (MIRV, MaRV and HGV) prior to GMD, as well as a decline of various global missile inventories and testing of systems during this same period, would seem to invalidate this theory. U.S. adversaries have exploited the perception of the U.S. missile defense shield as a destabilizing factor as a pretext to strengthen and modernize their militaries and justify their policies. As a result, it can be concluded that global stability has not significantly decreased due to GMD; but rather, the natural progression of technological and missile development would inherently include innovative and dynamic approaches to target engagement, deterrence, and national defense.

Keywords: arms control, arms race, global security, GMD, ICBM, missile defense, proliferation

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Authors: Joseph F. Cabrera, Rachael Dela Cruz

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This study specifically examines patterns that define ethnoracially diverse neighborhoods. Ethnoracial diversity is important as it facilitates cross-racial interactions within neighborhoods which have been theorized to be associated with such outcomes as intergroup harmony, the reduction of racial and ethnic prejudice and discrimination, and increases in racial tolerance. Los Angeles (LA) is an ideal location to study ethnoracial spatial patterns as it is one of the most ethnoracially diverse cities in the world. A large influx of Latinos, as well as Asians, have contributed to LA’s urban landscape becoming increasingly diverse over several decades. Our dataset contains all census tracts in Los Angeles County in 2016 and incorporates Census and ACS demographic and spatial data. We quantify ethnoracial diversity using a derivative of Simpson’s Diversity Index and utilize this measure to test previous literature that suggests Latinos are one of the key drivers of changing ethnoracial spatial patterns in Los Angeles. Preliminary results suggest that there has been an overall increase in ethnoracial diversity in Los Angeles neighborhoods over the past sixteen years. Patterns associated with this trend include decreases in predominantly white and black neighborhoods, increases in predominantly Latino and Asian neighborhoods, and a general decrease in the white populations of the most diverse neighborhoods. A similar pattern is seen in neighborhoods with large Latino increases- a decrease in white population, but with an increase in Asian and black populations. We also found support for previous research that suggests increases in Latino and Asian populations act as a buffer, allowing for black population increases without a sizeable decrease in the white population. Future research is needed to understand the underlying causes involved in many of the patterns and trends highlighted in this study.

Keywords: race, race and interaction, racial harmony, social interaction

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Authors: Farah Naja, Nahla Hwalla, Leila Itani, Shirine Baalbaki, Abla Sibai, Lara Nasreddine

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Purpose: To propose an index for assessing adherence to a Middle-Eastern version of the Mediterranean diet as represented by the traditional Lebanese Mediterranean diet (LMD), to evaluate the association between the LMD and selected European Mediterranean diets (EMD); to examine socio-demographic and lifestyle correlates of adherence to Mediterranean diet (MD) among Lebanese adults. Methods: Using nationally representative dietary intake data of Lebanese adults, an index to measure adherence to the LMD was derived. The choice of food groups used for calculating the LMD score was based on results of previous factor analyses conducted on the same dataset. These food groups included fruits, vegetables, legumes, olive oil, burghol, dairy products, starchy vegetables, dried fruits, and eggs. Using Pearson’s correlation and scores tertiles distributions agreement, the derived LMD index was compared to previously published EMD indexes from Greece, Spain, Italy, France, and EPIC. Results: Fruits, vegetables and olive oil were common denominators to all MD scores. Food groups, specific to the LMD, included burghol and dried fruits. The LMD score significantly correlated with the EMD scores, while being closest to the Italian (r=0.57) and farthest from the French (r=0.21). Percent agreement between scores’ tertile distributions and Kappa statistics confirmed these findings. Multivariate linear regression showed that older age, higher educational, female gender, and healthy lifestyle characteristics were associated with increased adherence to all MD studied. Conclusion: A novel LMD index was proposed to characterize Mediterranean diet in Lebanon, complementing international efforts to characterize the MD and its association with disease risk.

Keywords: mediterranean diet, adherence, Middle-East, Lebanon, Europe

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Authors: Khairani Binti Supyan, Fatimah Khalid, Mas Rina Mustaffa, Azreen Bin Azman, Amirul Azuani Romle

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Perennial plant classification plays a significant role in various agricultural and environmental applications, assisting in plant identification, disease detection, and biodiversity monitoring. Nevertheless, attaining high accuracy in perennial plant image classification remains challenging due to the complex variations in plant appearance, the diverse range of environmental conditions under which images are captured, and the inherent variability in image quality stemming from various factors such as lighting conditions, camera settings, and focus. This paper proposes an adaptation approach to optimize perennial plant image classification by fine-tuning the pre-trained DNNs model. This paper explores the efficacy of fine-tuning prevalent architectures, namely VGG16, ResNet50, and InceptionV3, leveraging transfer learning to tailor the models to the specific characteristics of perennial plant datasets. A subset of the MYLPHerbs dataset consisted of 6 perennial plant species of 13481 images under various environmental conditions that were used in the experiments. Different strategies for fine-tuning, including adjusting learning rates, training set sizes, data augmentation, and architectural modifications, were investigated. The experimental outcomes underscore the effectiveness of fine-tuning deep neural networks for perennial plant image classification, with ResNet50 showcasing the highest accuracy of 99.78%. Despite ResNet50's superior performance, both VGG16 and InceptionV3 achieved commendable accuracy of 99.67% and 99.37%, respectively. The overall outcomes reaffirm the robustness of the fine-tuning approach across different deep neural network architectures, offering insights into strategies for optimizing model performance in the domain of perennial plant image classification.

Keywords: perennial plants, image classification, deep neural networks, fine-tuning, transfer learning, VGG16, ResNet50, InceptionV3

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Authors: Rajkumar Ghosh

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Water scarcity is a significant challenge in urban areas, even in smart cities (Lucknow, Bangalore, Jaipur, etc.) where efficient resource management is prioritized. The depletion of groundwater resources in Gomti Nagar, Lucknow, Uttar Pradesh, India is particularly severe, posing a significant challenge for sustainable development in the region. This study focuses on addressing the water shortage by investigating the effectiveness of rooftop rainwater harvesting systems (RTRWHs) as a sustainable approach to bridge the gap between groundwater recharge and extraction. The aim of this study is to assess the effectiveness of RTRWHs in reducing aquifer depletion and addressing the water scarcity issue in the Gomti Nagar region. The research methodology involves the utilization of RTRWHs as the primary method for collecting rainwater. RTRWHs will be implemented in residential and commercial buildings to maximize the collection of rainwater. Data for this study were collected through various sources such as government reports, surveys, and existing groundwater abstraction patterns. Statistical analysis and modelling techniques were employed to assess the current water situation, groundwater depletion rate, and the potential impact of implementing RTRWHs. The study reveals that the installation of RTRWHs in the Gomti Nagar region has a positive impact on addressing the water scarcity issue. Currently, RTRWHs cover only a small percentage of the total rainfall collected in the region. However, when RTRWHs are installed in all buildings, their influence on increasing water availability and reducing aquifer depletion will be significantly greater. The study also highlights the significant water imbalance in the region, emphasizing the urgent need for sustainable water management practices. This research contributes to the theoretical understanding of sustainable water management systems in smart cities. By highlighting the effectiveness of RTRWHs in reducing aquifer depletion, it emphasizes the importance of implementing such systems in urban areas. Data for this study were collected through various sources such as government reports, surveys, and existing groundwater abstraction patterns. The collected data were then analysed using statistical analysis and modelling techniques to assess the current water situation, groundwater depletion rate, and the potential impact of implementing RTRWHs. The findings of this study demonstrate that the implementation of RTRWHs can effectively mitigate the water scarcity crisis in Gomti Nagar. By reducing aquifer depletion and bridging the gap between groundwater recharge and extraction, RTRWHs offer a sustainable solution to the region's water scarcity challenges. Widespread adoption of RTRWHs in all buildings and integration into urban planning and development processes are crucial for efficient water management in smart cities like Gomti Nagar. These findings can serve as a basis for policymakers, urban planners, and developers to prioritize and incentivize the installation of RTRWHs as a potential solution to the water shortage crisis.

Keywords: water scarcity, urban areas, smart cities, resource management, groundwater depletion, rooftop rainwater harvesting systems, sustainable development, sustainable water management, mitigating water scarcity

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Authors: Christina Tran, Lu Khoo, Andrea Waylen

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Introduction: The evolution of the dental consultation from paternalism to partnership has been well documented in developed Western countries. Great emphasis is now placed on the importance of empowering patients to make decisions regarding their care, obtaining informed consent, and maintaining patient privacy and confidentiality. With the majority of communication occurring non-verbally, clinicians often adopt behaviours which suggest an approachable and positive attitude. However, evidence indicates that in Asia, a paternalistic model may be favored in medicine. The power imbalance occurring in doctor-patient relationships worldwide may be exacerbated by various factors in Southeast Asia: the strong hierarchical culture, and the large education gap between doctor and patient. Further insight into this matter can be gained by observing patient-dentist communication in Cambodia. The dentist:population ratio in Cambodia is approximately 1:33,000, with rural areas remaining extremely underserviced. We have carried out an observational study of communication in a voluntary dental clinic in Cambodia with the aim of describing whether the patient-dentist relationship follows a paternalistic or patient-centred model. Method: Over a period of two weeks, two clinicians provided dental care as part of a voluntary program in two Cambodian settings: a temporary, rural clinic and a permanent clinic in Phnom Penh. The clinicians independently recorded their experiences in diaries, making observations on the verbal and non-verbal communication between patients and staff. General observations such as the clinic environment were also made. The diaries were then compared and analyzed using a thematic approach. Results: The overall themes that emerged were regarding the clinic environment, verbal communication, and non-verbal communication. Regarding the clinic environment, the rural clinic was arranged in order to easily direct patients from one dentist to another, with little emphasis on continuous patient care. There was also little consideration for patient privacy: patients were often treated in the presence of many observers, including other waiting patients. However, the permanent clinic was structured to allow greater patient privacy, with continuous patient care occurring throughout the appointment. Regarding verbal communication, there was a strongly paternalistic approach to gaining consent and giving instruction. Patients rarely asked questions regarding their treatment, with dentists doing little to encourage patient involvement. Non-verbal communication between patients and dentists was generally paternalistic, with the dentist often addressing the supine patient from above. Patients often avoided making eye-contact, which may have indicated discomfort or lack of engagement. Both adult and paediatric patients rarely raised verbal concerns regarding pain during treatment, despite displaying non-verbal signs of experiencing pain. Anxious paediatric patients were sometimes managed with physical restraint by their mothers to facilitate treatment. Conclusion: Patient-professional communication in the Cambodian dental setting was observed to be generally paternalistic in nature, although more patient-centred aspects were observed in the established, urban setting. However, it should be noted that these observations are subjective in nature, and that the patients’ actual perceptions of their communication experience were unexplored. Further observations in variety of dental settings in Cambodia are needed before any definitive conclusions can be made.

Keywords: patient-dentist communication, paternalism, patient-centered, non-verbal communication

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Authors: Briony Gray, Glenn Simpson, Hajira Dambha-Miller, Andrew Farmer

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Multimorbidity may be strongly affected by a variety of conditions, factors, and variables requiring higher demands on health and social care services, infrastructure, and expenses. Holding one or more conditions increases one’s risk for development of future conditions; with patients over 65 years old at highest risk. Psychosocial factors such as anxiety and depression are rising exponentially globally, which has been amplified by the COVID19 pandemic. These are highly correlated and predict poorer outcomes when held in coexistence and increase the likelihood of comorbid physical health conditions. While possible future reform of social and healthcare systems may help to alleviate some of these mounting pressures, there remains an urgent need to better understand the potential role health behaviours and psychosocial conditions - such as anxiety and depression – may have on aging populations. Using the UK healthcare scene as a lens for analysis, this study uses big data collected in the UK Longitudinal Study of Aging (ELSA) to examine the role of anxiety and depression in ageing populations (65yrs+). Using logistic regression modelling, results identify the 10 most significant variables correlated with both anxiety and depression from data categorised into the areas of health behaviour, psychosocial, socioeconomic, and life satisfaction (each demonstrated through literature review to be of significance). These are compared with wider global research findings with the aim of better understanding the areas in which social and healthcare reform can support multimorbidity interventions, making suggestions for improved patient-centred care. Scope of future research is outlined, which includes analysis of 59 total multimorbidity variables from the ELSA dataset, going beyond anxiety and depression.

Keywords: multimorbidity, health behaviours, patient centred care, psychosocial factors

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Authors: Joseph Lillington, Tom Gout, Mike Harrison, Ian Farnan

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The vitrification of high-level nuclear waste within borosilicate glass and its incorporation within a multi-barrier repository deep underground is widely accepted as the preferred disposal method. However, for this to happen, any safety case will require validation that the initially localized radionuclides will not be considerably released into the near/far-field. Therefore, accurate mechanistic models are necessary to predict glass dissolution, and these should be robust to a variety of incorporated waste species and leaching test conditions, particularly given substantial variations across international waste-streams. Here, machine learning is used to predict glass material properties (viscosity, density) and glass leaching model parameters from large-scale industrial data. A variety of different machine learning algorithms have been compared to assess performance. Density was predicted solely from composition, whereas viscosity additionally considered temperature. To predict suitable glass leaching model parameters, a large simulated dataset was created by coupling MATLAB and the chemical reactive-transport code HYTEC, considering the state-of-the-art GRAAL model (glass reactivity in allowance of the alteration layer). The trained models were then subsequently applied to the large-scale industrial, experimental data to identify potentially appropriate model parameters. Results indicate that ensemble methods can accurately predict viscosity as a function of temperature and composition across all three industrial datasets. Glass density prediction shows reliable learning performance with predictions primarily being within the experimental uncertainty of the test data. Furthermore, machine learning can predict glass dissolution model parameters behavior, demonstrating potential value in GRAAL model development and in assessing suitable model parameters for large-scale industrial glass dissolution data.

Keywords: machine learning, predictive modelling, pattern recognition, radioactive waste glass

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Authors: Fatemeh Zeinali Sehrig

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Background: miRNAs play an important role in the post-transcriptional regulation of genes, including genes involved in DNA methylation (DNMTs), and are also important regulators of oncogenic pathways. The study of microRNAs and DNMTs in breast cancer allows the development of targeted treatments and early detection of this cancer. Methods and Materials: Clinical Patients and Samples: Institutional guidelines, including ethical approval and informed consent, were followed by the Ethics Committee (Ethics code: IR.IAU.TABRIZ.REC.1401.063) of Tabriz Azad University, Tabriz, Iran. In this study, tissues of 100 patients with breast cancer and tissues of 100 healthy women were collected from Noor Nejat Hospital in Tabriz. The basic characteristics of the patients with breast cancer included: 1)Tumor grade(Grade 3 = 5%, Grade 2 = 87.5%, Grade 1 = 7.5%), 2)Lymph node(Yes = 87.5%, No = 12.5%), 3)Family cancer history(Yes = 47.5%, No = 41.3%, Unknown = 11.2%), 4) Abortion history(Yes = 36.2%).In silico methods (data gathering, process, and build networks): Gene Expression Omnibus (GEO), a high-throughput genomic database, was queried for miRNAs expression profiles in breast cancer. For Experimental protocol Tissue Processing, Total RNA isolation, complementary DNA(cDNA) synthesis, and quantitative real time PCR (QRT-PCR) analysis were performed. Results: In the present study, we found significant (p.value<0.05) changes in the expression level of miRNAs and DNMTs in patients with breast cancer. In bioinformatics studies, the GEO microarray data set, similar to qPCR results, showed a decreased expression of miRNAs and increased expression of DNMTs in breast cancer. Conclusion: According to the results of the present study, which showed a decrease in the expression of miRNAs and DNMTs in breast cancer, it can be said that these genes can be used as important diagnostic and therapeutic biomarkers in breast cancer.

Keywords: gene expression omnibus, microarray dataset, breast cancer, miRNA, DNMT (DNA methyltransferases)

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Authors: El Harraj Abdeslam, Raissouni Naoufal

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The detection of moving objects from a video image sequences is very important for object tracking, activity recognition, and behavior understanding in video surveillance. The most used approach for moving objects detection / tracking is background subtraction algorithms. Many approaches have been suggested for background subtraction. But, these are illumination change sensitive and the solutions proposed to bypass this problem are time consuming. In this paper, we propose a robust yet computationally efficient background subtraction approach and, mainly, focus on the ability to detect moving objects on dynamic scenes, for possible applications in complex and restricted access areas monitoring, where moving and motionless persons must be reliably detected. It consists of three main phases, establishing illumination changes in variance, background/foreground modeling and morphological analysis for noise removing. We handle illumination changes using Contrast Limited Histogram Equalization (CLAHE), which limits the intensity of each pixel to user determined maximum. Thus, it mitigates the degradation due to scene illumination changes and improves the visibility of the video signal. Initially, the background and foreground images are extracted from the video sequence. Then, the background and foreground images are separately enhanced by applying CLAHE. In order to form multi-modal backgrounds we model each channel of a pixel as a mixture of K Gaussians (K=5) using Gaussian Mixture Model (GMM). Finally, we post process the resulting binary foreground mask using morphological erosion and dilation transformations to remove possible noise. For experimental test, we used a standard dataset to challenge the efficiency and accuracy of the proposed method on a diverse set of dynamic scenes.

Keywords: video surveillance, background subtraction, contrast limited histogram equalization, illumination invariance, object tracking, object detection, behavior understanding, dynamic scenes

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Authors: Dona Ghosh, Zakir Husain

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In India, the increase in share of aged has generated many social and economic issues, of which health concerns is a major challenge that society must confront in coming years. Self-reported health (SRH) is a popular health measure in this regard but has been questioned in recent years due to its heavy dependence on the socioeconomic status. So, the validity of SRH, as a measure of health status during old age, is needed to be verified. This paper emphasizes on the self-reported health and related inconsistent responses among elderly in India. The objective of the study is bifurcated into two parts: firstly, to identify the socioeconomic determinants of subjective health status and its change over time; and secondly, to analyse the role of the socioeconomic components in providing inconsistent responses regarding the health status of elderly. Inconsistency in response can rise in two ways: positive response bias (if an individual has a health problem but reports his/her health as good) and negative response bias (if bad health is reported even if there is no health problem). However, in the present study, we focus only on the negative response bias of elderly individuals. To measure the inconsistencies in responses, self-reported health is compared with two types of physical health conditions – existence of chronicle ailment and physical immobility. Using NSS dataset of 60th and 71st rounds, the study found that subjective health has worsened over time in both rural and urban areas. Findings suggest that inconsistency in responses, related to chronic ailment, vary across social classes, living environments, geographical regions, age groups and education levels. On the contrary, variation in inconsistent responses regarding physical mobility is quite rare and difficult to explain by socioeconomic characteristics because most of the indicators are found to be insignificant in this regard. The findings indicate that in case of chronicle ailment, inconsistency between objective and subjective health status largely depends on socioeconomic conditions but the importance of such factors disappears for physical immobility.

Keywords: India, aging, self-reported health, inconsistent responses

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Authors: Mohamed Saad, Huda Ismail

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The purpose of this research is to examine the impact of cryptocurrency classification on money laundering crimes and to analyze how the preferences of criminals differ according to the type of digital currency used. Specifically, we aim to explore the roles of stablecoins, utility coins, and privacy tokens in facilitating or hindering money laundering activities and to identify the key factors that influence the choices of criminals in using these cryptocurrencies. To achieve our research objectives, we used a dataset for the most highly traded cryptocurrencies (32 currencies) that were published on the coin market cap for 2022. In addition to conducting a comprehensive review of the existing literature on cryptocurrency and money laundering, with a focus on stablecoins, utility coins, and privacy tokens, Furthermore, we conducted several Multivariate analyses. Our study reveals that the classification of cryptocurrency plays a significant role in money laundering activities, as criminals tend to prefer certain types of digital currencies over others, depending on their specific needs and goals. Specifically, we found that stablecoins are more commonly used in money laundering due to their relatively stable value and low volatility, which makes them less risky to hold and transfer. Utility coins, on the other hand, are less frequently used in money laundering due to their lack of anonymity and limited liquidity. Finally, privacy tokens, such as Monero and Zcash, are increasingly becoming a preferred choice among criminals due to their high degree of privacy and untraceability. In summary, our study highlights the importance of understanding the nuances of cryptocurrency classification in the context of money laundering and provides insights into the preferences of criminals in using digital currencies for illegal activities. Based on our findings, our recommendation to the policymakers is to address the potential misuse of cryptocurrencies for money laundering. By implementing measures to regulate stable coins, strengthening cross-border cooperation, fostering public-private partnerships, and increasing cooperation, policymakers can help prevent and detect money laundering activities involving digital currencies.

Keywords: crime, cryptocurrency, money laundering, tokens.

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Authors: Rishabh Nagpal, Riya Uchagaonkar, Venkata Naga Narasimha Ashish Mernedi, Ahmed Hambaba

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The rapid evolution of technology in the communication sector continually seeks to bridge the gap between different communities, notably between the deaf community and the hearing world. This project develops a comprehensive American Sign Language (ASL) recognition system, leveraging the advanced capabilities of convolutional neural networks (CNNs) and vision transformers (ViTs) to interpret and translate ASL in real-time. The primary objective of this system is to provide an effective communication tool that enables seamless interaction through accurate sign language interpretation. The architecture of the proposed system integrates dual networks -VGG16 for precise spatial feature extraction and vision transformers for contextual understanding of the sign language gestures. The system processes live input, extracting critical features through these sophisticated neural network models, and combines them to enhance gesture recognition accuracy. This integration facilitates a robust understanding of ASL by capturing detailed nuances and broader gesture dynamics. The system is evaluated through a series of tests that measure its efficiency and accuracy in real-world scenarios. Results indicate a high level of precision in recognizing diverse ASL signs, substantiating the potential of this technology in practical applications. Challenges such as enhancing the system’s ability to operate in varied environmental conditions and further expanding the dataset for training were identified and discussed. Future work will refine the model’s adaptability and incorporate haptic feedback to enhance the interactivity and richness of the user experience. This project demonstrates the feasibility of an advanced ASL recognition system and lays the groundwork for future innovations in assistive communication technologies.

Keywords: sign language, computer vision, vision transformer, VGG16, CNN

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Authors: Sergey V. Vinogradov

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Coastal communities are presently facing increased vulnerabilities due to rising sea levels and shifts in global climate patterns, a trend expected to escalate in the long run. To address the needs of government entities, the public sector, and private enterprises, there is an urgent need to thoroughly investigate, assess, and manage the present and projected risks associated with coastal flooding, including storm surges, sea level rise, and nuisance flooding. In response to these challenges, a practical approach to evaluating storm surge inundation risks has been developed. This methodology offers an integrated assessment of potential flood risk in targeted coastal areas. The physical modeling framework involves simulating synthetic storms and utilizing hydrodynamic models that align with projected future climate and ocean conditions. Both publicly available and site-specific data form the basis for a risk assessment methodology designed to translate inundation model outputs into statistically significant projections of expected financial and operational consequences. This integrated approach produces measurable indicators of impacts stemming from floods, encompassing economic and other dimensions. By establishing connections between the frequency of modeled flood events and their consequences across a spectrum of potential future climate conditions, our methodology generates probabilistic risk assessments. These assessments not only account for future uncertainty but also yield comparable metrics, such as expected annual losses for each inundation event. These metrics furnish stakeholders with a dependable dataset to guide strategic planning and inform investments in mitigation. Importantly, the model's adaptability ensures its relevance across diverse coastal environments, even in instances where site-specific data for analysis may be limited.

Keywords: climate, coastal, surge, risk

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Authors: Ankit Kamboj, Shikha Talwar, Nilesh Powar

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To successfully deliver our products in the market, the employees need to be in a safe environment, especially in an industrial and manufacturing environment. The consequences of delinquency in wearing safety glasses while working in industrial plants could be high risk to employees, hence the need to develop a real-time automatic detection system which detects the persons (violators) not wearing safety glasses. In this study a convolutional neural network (CNN) algorithm called faster region based CNN (Faster RCNN) with rotated bounding box has been used for detecting safety glasses on persons; the algorithm has an advantage of detecting safety glasses with different orientation angles on the persons. The proposed method of rotational bounding boxes with a convolutional neural network first detects a person from the images, and then the method detects whether the person is wearing safety glasses or not. The video data is captured at the entrance of restricted zones of the industrial environment (manufacturing plant), which is further converted into images at 2 frames per second. In the first step, the CNN with pre-trained weights on COCO dataset is used for person detection where the detections are cropped as images. Then the safety goggles are labelled on the cropped images using the image labelling tool called roLabelImg, which is used to annotate the ground truth values of rotated objects more accurately, and the annotations obtained are further modified to depict four coordinates of the rectangular bounding box. Next, the faster RCNN with rotated bounding box is used to detect safety goggles, which is then compared with traditional bounding box faster RCNN in terms of detection accuracy (average precision), which shows the effectiveness of the proposed method for detection of rotatory objects. The deep learning benchmarking is done on a Dell workstation with a 16GB Nvidia GPU.

Keywords: CNN, deep learning, faster RCNN, roLabelImg rotated bounding box, safety goggle detection

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Authors: Pinna Beatrice., Laini Alex, Negro Giovanni, Burgazzi Gemma, Viaroli Pierluigi, Vezza Paolo

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Habitat models rarely consider macroinvertebrates as ecological targets in rivers. Available approaches mainly focus on single macroinvertebrate species, not addressing the ecological needs and functionality of the entire community. This research aimed to provide an approach to model the habitat of the macroinvertebrate community. The approach is based on the recently developed Flow-T index, together with a Random Forest (RF) regression, which is employed to apply the Flow-T index at the meso-habitat scale. Using different datasets gathered from both field data collection and 2D hydrodynamic simulations, the model has been calibrated in the Trebbia river (2019 campaign), and then validated in the Trebbia, Taro, and Enza rivers (2020 campaign). The three rivers are characterized by a braiding morphology, gravel riverbeds, and summer low flows. The RF model selected 12 mesohabitat descriptors as important for the macroinvertebrate community. These descriptors belong to different frequency classes of water depth, flow velocity, substrate grain size, and connectivity to the main river channel. The cross-validation R² coefficient (R²𝒸ᵥ) of the training dataset is 0.71 for the Trebbia River (2019), whereas the R² coefficient for the validation datasets (Trebbia, Taro, and Enza Rivers 2020) is 0.63. The agreement between the simulated results and the experimental data shows sufficient accuracy and reliability. The outcomes of the study reveal that the model can identify the ecological response of the macroinvertebrate community to possible flow regime alterations and to possible river morphological modifications. Lastly, the proposed approach allows extending the MesoHABSIM methodology, widely used for the fish habitat assessment, to a different ecological target community. Further applications of the approach can be related to flow design in both perennial and non-perennial rivers, including river reaches in which fish fauna is absent.

Keywords: ecological flows, macroinvertebrate community, mesohabitat, river habitat modeling

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Authors: Michael Picard

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This proposal offers to shed light on the changing legal geography of the global waste economy. Global waste recycling has become a multi-billion-dollar industry. NASDAQ predicts the emergence of a worldwide 1,296G$ waste management market between 2017 and 2022. Underlining this evolution, a new generation of preferential waste-trade agreements has emerged in the Pacific. In the last decade, Japan has concluded a series of bilateral treaties with Asian countries, and most recently with China. An agreement between Tokyo and Beijing was formalized on 7 May 2008, which forged an economic partnership on waste transfer and mining. The agreement set up International Recycling Zones, where certified recycling plants in China process industrial waste imported from Japan. Under the joint venture, Chinese companies salvage the embedded value from Japanese industrial discards, reprocess them and send them back to Japanese manufacturers, such as Mitsubishi and Panasonic. This circular economy is designed to convert surplus garbage into surplus value. Ever since the opening of Sino-Japanese eco-parks, millions of tons of plastic and e-waste have been exported from Japan to China every year. Yet, quite unexpectedly, China has recently closed its waste market to imports, jeopardizing Japan’s billion-dollar exports to China. China notified the WTO that, by the end of 2017, it would no longer accept imports of plastics and certain metals. Given China’s share of Japanese waste exports, a complete closure of China’s market would require Japan to find new uses for its recyclable industrial trash generated domestically every year. It remains to be seen how China will effectively implement its ban on waste imports, considering the economic interests at stake. At this stage, what remains to be clarified is whether China's ban on waste imports will negatively affect the recycling trade between Japan and China. What is clear, though, is the rapid transformation in the legal geography of waste mining in East-Asia. For decades, East-Asian waste trade had been tied up in an ‘ecologically unequal exchange’ between the Japanese core and the Chinese periphery. This global unequal waste distribution could be measured by the Environmental Stringency Index, which revealed that waste regulation was 39% weaker in the Global South than in Japan. This explains why Japan could legally export its hazardous plastic and electronic discards to China. The asymmetric flow of hazardous waste between Japan and China carried the colonial heritage of international law. The legal geography of waste distribution was closely associated to the imperial construction of an ecological trade imbalance between the Japanese source and the Chinese sink. Thus, China’s recent decision to ban hazardous waste imports is a sign of a broader ecological shift. As a global economic superpower, China announced to the world it would no longer be the planet’s junkyard. The policy change will have profound consequences on the global circulation of waste, re-routing global waste towards countries south of China, such as Vietnam and Malaysia. By the time the Berlin Conference takes place in May 2018, the presentation will be able to assess more accurately the effect of the Chinese ban on the transboundary movement of waste in Asia.

Keywords: Asia, ecological unequal exchange, global waste trade, legal geography

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Authors: Parvaneh Mahmoudi, Ahmad Moeni, Seyed Mojtaba Khayam Nekoei, Mohsen Mardi, Mehrshad Zeinolabedini, Ghasem Hosseini Salekdeh

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Saffron (Crocus sativus L.) is one of the most important spice and medicinal plants. The three-branch style of C. sativus flowers are the most important economic part of the plant and known as saffron, which has several medicinal properties. Despite the economic and biological significance of this plant, knowledge about its molecular characteristics is very limited. In the present study, we, for the first time, constructed a comprehensive dataset for C. sativus stigma through de novo transcriptome sequencing. We performed de novo transcriptome sequencing of C. sativus stigma using the Illumina paired-end sequencing technology. A total of 52075128 reads were generated and assembled into 118075 unigenes, with an average length of 629 bp and an N50 of 951 bp. A total of 66171unigenes were identified, among them, 66171 (56%) were annotated in the non-redundant National Center for Biotechnology Information (NCBI) database, 30938 (26%) were annotated in the Swiss-Prot database, 10273 (8.7%) unigenes were mapped to 141 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database, while 52560 (44%) and 40756 (34%) unigenes were assigned to Gen Ontology (GO) categories and Eukaryotic Orthologous Groups of proteins (KOG), respectively. In addition, 65 candidate genes involved in three stages of crocin biosynthesis were identified. Finally, transcriptome sequencing of saffron stigma was used to identify 6779 potential microsatellites (SSRs) molecular markers. High-throughput de novo transcriptome sequencing provided a valuable resource of transcript sequences of C. sativus in public databases. In addition, most of candidate genes potentially involved in crocin biosynthesis were identified which could be further utilized in functional genomics studies. Furthermore, numerous obtained SSRs might contribute to address open questions about the origin of this amphiploid spices with probable little genetic diversity.

Keywords: saffron, transcriptome, NGS, bioinformatic

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Authors: Massimo Miccoli, Luca Marangoni, Alberto Aniello Scaringi, Alessandro Marceddu, Alessandro Amicone

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The susceptibility of deep neural networks (DNNs) to adversarial manipulations is a recognized challenge within the computer vision domain. Adversarial examples, crafted by adding subtle yet malicious alterations to benign images, exploit this vulnerability. Various defense strategies have been proposed to safeguard DNNs against such attacks, stemming from diverse research hypotheses. Building upon prior work, our approach involves the utilization of autoencoder models. Autoencoders, a type of neural network, are trained to learn representations of training data and reconstruct inputs from these representations, typically minimizing reconstruction errors like mean squared error (MSE). Our autoencoder was trained on a dataset of benign examples; learning features specific to them. Consequently, when presented with significantly perturbed adversarial examples, the autoencoder exhibited high reconstruction errors. The architecture of the autoencoder was tailored to the dimensions of the images under evaluation. We considered various image sizes, constructing models differently for 256x256 and 512x512 images. Moreover, the choice of the computer vision model is crucial, as most adversarial attacks are designed with specific AI structures in mind. To mitigate this, we proposed a method to replace image-specific dimensions with a structure independent of both dimensions and neural network models, thereby enhancing robustness. Our multi-modal autoencoder reconstructs the spectral representation of images across the red-green-blue (RGB) color channels. To validate our approach, we conducted experiments using diverse datasets and subjected them to adversarial attacks using models such as ResNet50 and ViT_L_16 from the torch vision library. The autoencoder extracted features used in a classification model, resulting in an MSE (RGB) of 0.014, a classification accuracy of 97.33%, and a precision of 99%.

Keywords: adversarial attacks, malicious images detector, binary classifier, multimodal transformer autoencoder

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Authors: Ebipatei Victoria Tunyan, T. A. Cao, Cheol Young Ock

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Detecting subjectively biased statements is a vital task. This is because this kind of bias, when present in the text or other forms of information dissemination media such as news, social media, scientific texts, and encyclopedias, can weaken trust in the information and stir conflicts amongst consumers. Subjective bias detection is also critical for many Natural Language Processing (NLP) tasks like sentiment analysis, opinion identification, and bias neutralization. Having a system that can adequately detect subjectivity in text will boost research in the above-mentioned areas significantly. It can also come in handy for platforms like Wikipedia, where the use of neutral language is of importance. The goal of this work is to identify the subjectively biased language in text on a sentence level. With machine learning, we can solve complex AI problems, making it a good fit for the problem of subjective bias detection. A key step in this approach is to train a classifier based on BERT (Bidirectional Encoder Representations from Transformers) as upstream model. BERT by itself can be used as a classifier; however, in this study, we use BERT as data preprocessor as well as an embedding generator for a Bi-LSTM (Bidirectional Long Short-Term Memory) network incorporated with attention mechanism. This approach produces a deeper and better classifier. We evaluate the effectiveness of our model using the Wiki Neutrality Corpus (WNC), which was compiled from Wikipedia edits that removed various biased instances from sentences as a benchmark dataset, with which we also compare our model to existing approaches. Experimental analysis indicates an improved performance, as our model achieved state-of-the-art accuracy in detecting subjective bias. This study focuses on the English language, but the model can be fine-tuned to accommodate other languages.

Keywords: subjective bias detection, machine learning, BERT–BiLSTM–Attention, text classification, natural language processing

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Authors: Qudsiya Baig

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Empathy yields an unparalleled therapeutic value within patient physician interactions. Medical research is inundated with evidence to support that a physician’s ability to empathise with patients leads to a greater willingness to report symptoms, an improvement in diagnostic accuracy and safety, and a better adherence and satisfaction with treatment plans. Furthermore, the Institute of Medicine states that empathy leads to a more patient-centred care, which is one of the six main goals of a 21st century health system. However, there is a paradox between the theoretical significance of empathy and its presence, or lack thereof, in clinical practice. Recent studies have reported that empathy declines amongst students and physicians over time. The three most impactful contributors to this decline are: (1) disagreements over the definitions of empathy making it difficult to implement it into practice (2) poor consideration or regulation of empathy leading to burnout and thus, abandonment altogether, and (3) the lack of diversity in the curriculum and the influence of medical culture, which prioritises science over patient experience, limiting some physicians from using ‘too much’ empathy in the fear of losing clinical objectivity. These issues were investigated by conducting a fully inductive thematic narrative analysis of patient narratives in the UK to evaluate the behaviours and attitudes that patients associate with empathy. The principal enquiries underpinning this study included uncovering the factors that affected experience of empathy within provider-patient interactions and to analyse their effects on patient care. This research contributes uniquely to this discourse by examining the phenomenon of empathy directly from patients’ experiences, which were systematically extracted from a repository of online patient narratives of care titled ‘CareOpinion UK’. Narrative analysis was specifically chosen as the methodology to examine narratives from a phenomenological lens to focus on the particularity and context of each story. By enquiring beyond the superficial who-whatwhere, the study of narratives prescribed meaning to illness by highlighting the everyday reality of patients who face the exigent life circumstances created by suffering, disability, and the threat of life. The following six themes were found to be the most impactful in influencing the experience of empathy: dismissive behaviours, judgmental attitudes, undermining patients’ pain or concerns, holistic care and failures and successes of communication or language. For each theme there were overarching themes relating to either a failure to understand the patient’s perspective or a success in taking a person-centred approach. An in-depth analysis revealed that a lack of empathy was greatly associated with an emotive-cognitive imbalance, which disengaged physicians with their patients’ emotions. This study hereby concludes that competent providers require a combination of knowledge, skills, and more importantly empathic attitudes to help create a context for effective care. The crucial elements of that context involve (a) identifying empathy clues within interactions to engage with patients’ situations, (b) attributing a perspective to the patient through perspective-taking and (c) adapting behaviour and communication according to patient’s individual needs. Empathy underpins that context, as does an appreciation of narrative, and the two are interrelated.

Keywords: empathy, narratives, person-centred, perspective, perspective-taking

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Authors: Ghufran Ahmed, Muhammad Osama Siddiqui, Shahbaz Siddiqui, Rauf Ahmad Shams Malick, Faisal Khan, Mubashir Khan

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In this paper, a machine learning-based approach for early prediction of diseases in cows is proposed. Different ML algos are applied to extract useful patterns from the available dataset. Technology has changed today’s world in every aspect of life. Similarly, advanced technologies have been developed in livestock and dairy farming to monitor dairy cows in various aspects. Dairy cattle monitoring is crucial as it plays a significant role in milk production around the globe. Moreover, it has become necessary for farmers to adopt the latest early prediction technologies as the food demand is increasing with population growth. This highlight the importance of state-ofthe-art technologies in analyzing how important technology is in analyzing dairy cows’ activities. It is not easy to predict the activities of a large number of cows on the farm, so, the system has made it very convenient for the farmers., as it provides all the solutions under one roof. The cattle industry’s productivity is boosted as the early diagnosis of any disease on a cattle farm is detected and hence it is treated early. It is done on behalf of the machine learning output received. The learning models are already set which interpret the data collected in a centralized system. Basically, we will run different algorithms on behalf of the data set received to analyze milk quality, and track cows’ health, location, and safety. This deep learning algorithm draws patterns from the data, which makes it easier for farmers to study any animal’s behavioral changes. With the emergence of machine learning algorithms and the Internet of Things, accurate tracking of animals is possible as the rate of error is minimized. As a result, milk productivity is increased. IoT with ML capability has given a new phase to the cattle farming industry by increasing the yield in the most cost-effective and time-saving manner.

Keywords: IoT, machine learning, health care, dairy cows

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Authors: Bliss Singhal

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Epilepsy is a prevalent neurological disorder affecting approximately 50 million individuals worldwide and 1.2 million Americans. There exist millions of pediatric patients with intractable epilepsy, a condition in which seizures fail to come under control. The occurrence of seizures can result in physical injury, disorientation, unconsciousness, and additional symptoms that could impede children's ability to participate in everyday tasks. Predicting seizures can help parents and healthcare providers take precautions, prevent risky situations, and mentally prepare children to minimize anxiety and nervousness associated with the uncertainty of a seizure. This research proposes a comprehensive framework to predict seizures in pediatric patients by evaluating machine learning algorithms on unimodal neuroimaging data consisting of electroencephalogram signals. The bandpass filtering and independent component analysis proved to be effective in reducing the noise and artifacts from the dataset. Various machine learning algorithms’ performance is evaluated on important metrics such as accuracy, precision, specificity, sensitivity, F1 score and MCC. The results show that the deep learning algorithms are more successful in predicting seizures than logistic Regression, and k nearest neighbors. The recurrent neural network (RNN) gave the highest precision and F1 Score, long short-term memory (LSTM) outperformed RNN in accuracy and convolutional neural network (CNN) resulted in the highest Specificity. This research has significant implications for healthcare providers in proactively managing seizure occurrence in pediatric patients, potentially transforming clinical practices, and improving pediatric care.

Keywords: intractable epilepsy, seizure, deep learning, prediction, electroencephalogram channels

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