Search results for: vulnerability intelligence

Authors: S. Cobos-Guzman

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This paper presents the results of a mechatronic design based on a 4-wheel omnidirectional mobile robot that can be used in indoor logistic applications. The low-level control has been selected using two open-source hardware (Raspberry Pi 3 Model B+ and Arduino Mega 2560) that control four industrial motors, four ultrasound sensors, four optical encoders, a vision system of two cameras, and a Hokuyo URG-04LX-UG01 laser scanner. Moreover, the system is powered with a lithium battery that can supply 24 V DC and a maximum current-hour of 20Ah.The Robot Operating System (ROS) has been implemented in the Raspberry Pi and the performance is evaluated with the selection of the sensors and hardware selected. The mechatronic system is evaluated and proposed safe modes of power distribution for controlling all the electronic devices based on different tests. Therefore, based on different performance results, some recommendations are indicated for using the Raspberry Pi and Arduino in terms of power, communication, and distribution of control for different devices. According to these recommendations, the selection of sensors is distributed in both real-time controllers (Arduino and Raspberry Pi). On the other hand, the drivers of the cameras have been implemented in Linux and a python program has been implemented to access the cameras. These cameras will be used for implementing a deep learning algorithm to recognize people and objects. In this way, the level of intelligence can be increased in combination with the maps that can be obtained from the laser scanner.

Keywords: autonomous, indoor robot, mechatronic, omnidirectional robot

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Authors: Jui-Chien Hsieh

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Background:12-lead electrocardiography(ECG) is one of frequently-used tools to detect atrial fibrillation (AF), which might degenerate into life-threaten stroke, in clinical Practice. Based on this study, the AF detection by the clinically-used 12-lead ECG device has only 0.73~0.77 positive predictive value (ppv). Objective: It is on great demand to develop a new algorithm to improve the precision of AF detection using 12-lead ECG. Due to the progress on artificial intelligence (AI), we develop an ECG deep model that has the ability to recognize AF patterns and reduce false-positive errors. Methods: In this study, (1) 570-sample 12-lead ECG reports whose computer interpretation by the ECG device was AF were collected as the training dataset. The ECG reports were interpreted by 2 senior cardiologists, and confirmed that the precision of AF detection by the ECG device is 0.73.; (2) 88 12-lead ECG reports whose computer interpretation generated by the ECG device was AF were used as test dataset. Cardiologist confirmed that 68 cases of 88 reports were AF, and others were not AF. The precision of AF detection by ECG device is about 0.77; (3) A parallel 4-layer 1 dimensional convolutional neural network (CNN) was developed to identify AF based on limb-lead ECGs and chest-lead ECGs. Results: The results indicated that this model has better performance on AF detection than traditional computer interpretation of the ECG device in 88 test samples with 0.94 ppv, 0.98 sensitivity, 0.80 specificity. Conclusions: As compared to the clinical ECG device, this AI ECG model promotes the precision of AF detection from 0.77 to 0.94, and can generate impacts on clinical applications.

Keywords: 12-lead ECG, atrial fibrillation, deep learning, convolutional neural network

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Authors: Sean W. T. Bayly, Daniel Glover, Don Horrell, Simon Horrocks, Barnes Callum, Stuart Gibson, Mac Misuira

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We assess suitability of recent, ∼7B parameter, instruction-tuned Language Models for Generative Named Entity Recognition (GNER). Alongside Retrieval Augmented Generation (RAG), and supported by task-specific prompting, our proposed Multi-Level Information Retrieval method achieves notable improvements over finetuned entity-level and sentence-level methods. We conclude that language models directed toward this task are highly capable when distinguishing between positive classes (precision). However, smaller models seem to struggle to find all entities (recall). Poorly defined classes such as ”Miscellaneous” exhibit substantial declines in performance, likely due to the ambiguity it introduces to the prompt. This is partially resolved through a self-verification method using engineered prompts containing knowledge of the stricter class definitions, particularly in areas where their boundaries are in danger of overlapping, such as the conflation between the location ”Britain” and the nationality ”British”. Finally, we explore correlations between model performance on the GNER task with performance on relevant academic benchmarks.

Keywords: generative named entity recognition, information retrieval, lightweight artificial intelligence, prompt engineering, personal information identification, retrieval augmented generation, self verification

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Authors: Saroj Kumar Rath

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When insurgency erupted in Kashmir in 1989, it was quickly backed by Pakistan. Kashmir witnessed terrorism for more than a decade till 2004 when Indian forces decimated militancy. After the US pressure in 1992, terrorist training camps of Pakistan shifted to Afghanistan and al Qaeda and the Taliban had taken over training of Kashmiri militants in Afghanistan after 1997 as part of their global jihad. The Indo-Pak rivalry over Kashmir dispute had taken a new turn in the aftermath of 9/11 developments. Islamabad viewed its Afghan policy through the prism of denying India any advantage in Kabul. Pakistan was successful in refuting Indian presence in Kabul for a decade through the Taliban. After the 9/11 attacks the Inter Services Intelligence (ISI) saw Northern Alliance, supported by the Americans and all of Pakistan’s regional rivals – India, Iran, and Russia – as claiming victory in Kabul. For Pakistan’s military regime, this was a strategic disaster and prompted the ISI to give refuge to the escaping Taliban, while denying full support to Hamid Karzai. The new development in Afghanistan prompted India to establish a foothold it had lost nearly a decade earlier. India established diplomatic contacts with Afghanistan; supported the Karzai government and funded aid programs. Pakistan alleged that Indian agents are training Baloch and Sindhi dissidents in Pakistan through Afghanistan. Kabul had suddenly become the new Kashmir – the new battleground for India-Pakistan rivalry.

Keywords: Afghan imbroglio, Kashmir conflict, Indo-Pak rivalry, US policy in South Asia

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Authors: Emmanuel Osaze Oshoiribhor, Adetokunbo MacGregor John-Otumu

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In recent years, there has been a desire to forecast student academic achievement prior to graduation. This is to help them improve their grades, particularly for individuals with poor performance. The goal of this study is to employ supervised learning techniques to construct a predictive model for student academic achievement. Many academics have already constructed models that predict student academic achievement based on factors such as smoking, demography, culture, social media, parent educational background, parent finances, and family background, to name a few. This feature and the model employed may not have correctly classified the students in terms of their academic performance. This model is built using a logistic regression classifier with basic features such as the previous semester's course score, attendance to class, class participation, and the total number of course materials or resources the student is able to cover per semester as a prerequisite to predict if the student will perform well in future on related courses. The model outperformed other classifiers such as Naive bayes, Support vector machine (SVM), Decision Tree, Random forest, and Adaboost, returning a 96.7% accuracy. This model is available as a desktop application, allowing both instructors and students to benefit from user-friendly interfaces for predicting student academic achievement. As a result, it is recommended that both students and professors use this tool to better forecast outcomes.

Keywords: artificial intelligence, ML, logistic regression, performance, prediction

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Authors: Leonie Laskowitz

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A self-confident appearance is a basic prerequisite for success in the world of work 4.0. Within a few seconds, people convey a first impression that usually lasts. Artificial intelligence is making it increasingly important how our virtual selves appear and communicate (nonverbally) in digital worlds such as the metaverse. In addition to the modified creation of an avatar, the field of photogrammetry is developing fast, creating exact likenesses of ourselves in virtual environments. Given the importance of self-representation in virtual space for future collaborations, it is important to investigate the impact of phenotype in virtual worlds and how an avatar type can profitably be used situationally. We analyzed the effect of self-similar versus desirable self-presentation as an avatar on one's self-awareness, considering various theoretical constructs in the area of self-awareness and stress stimuli. The avatars were arbitrarily created on the one hand and scanned on the other hand with the help of a lidar sensor, the state-of-the-art photogrammetry method. All subjects were exposed to the established Trier Social Stress Test. The results showed that especially insecure people prefer to create rather than be scanned when confronted with a stressful work situation. (1) If they are in a casual work environment and a relaxed situation, they prefer a 3D photorealistic avatar that reflects them in detail. (2) Confident people will give their avatar their true appearance in any situation, while insecure people would only do so for honesty and authenticity. (3) Thus, the choice of avatar type has considerable impact on self-confidence in different situations.

Keywords: avatar, virtual identity, self-presentation, metaverse, virtual reality, self-awareness

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Authors: Almutasim Billa A. Alanazi, Hal S. Tharp

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Safe performance and efficient energy consumption are essential factors for designing a control system. This paper presents a reinforcement learning (RL) model that can be applied to control applications to improve safety and reduce energy consumption. As hardware constraints and environmental disturbances are imprecise and unpredictable, conventional control methods may not always be effective in optimizing control designs. However, RL has demonstrated its value in several artificial intelligence (AI) applications, especially in the field of control systems. The proposed model intelligently monitors a system's success by observing the rewards from the environment, with positive rewards counting as a success when the controlled reference is within the desired operating zone. Thus, the model can determine whether the system is safe to continue operating based on the designer/user specifications, which can be adjusted as needed. Additionally, the controller keeps track of energy consumption to improve energy efficiency by enabling the idle mode when the controlled reference is within the desired operating zone, thus reducing the system energy consumption during the controlling operation. Water temperature control for a hydroponic system is taken as a case study for the RL model, adjusting the variance of disturbances to show the model’s robustness and efficiency. On average, the model showed safety improvement by up to 15% and energy efficiency improvements by 35%- 40% compared to a traditional RL model.

Keywords: control system, hydroponics, machine learning, reinforcement learning

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Authors: Raymond Xu, Cindy Jingru Wang

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Social media has become a globally connected and influencing platform. Social media data, such as tweets, can help predict the spread of pandemics and provide individuals and healthcare providers early warnings. Public psychological reactions and opinions can be efficiently monitored by AI models on the progression of dominant topics on Twitter. However, statistics show that as the coronavirus spreads, so does an infodemic of misinformation due to pandemic-related factors such as unemployment and lockdowns. Social media algorithms are often biased toward outrage by promoting content that people have an emotional reaction to and are likely to engage with. This can influence users’ attitudes and cause confusion. Therefore, social media is a double-edged sword. Combating fake news and biased content has become one of the essential tasks. This research analyzes the variety of methods used for fake news detection covering random forest, logistic regression, support vector machines, decision tree, naive Bayes, BoW, TF-IDF, LDA, CNN, RNN, LSTM, DeepFake, and hierarchical attention network. The performance of each method is analyzed. Based on these models’ achievements and limitations, a multi-dimensional AI framework is proposed to achieve higher accuracy in infodemic detection, especially pandemic-related news. The model is trained on contextual content, images, and news metadata.

Keywords: artificial intelligence, fake news detection, infodemic detection, image recognition, sentiment analysis

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Authors: Mohsen Noroozi, Maryam Khalili

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Creating cities, objects and spaces that are economically, environmentally and socially sustainable and which meet the challenge of social interaction and generation change will be one of the biggest tasks of designers. Social sustainability is about how individuals, communities and societies live with each other and set out to achieve the objectives of development model which they have chosen for themselves. Urban lightning as one of the most important elements of urban furniture that people constantly interact with it in public spaces; can be a significant object for designers. Using intelligence by internet of things for urban lighting makes it more interactive in public environments. It can encourage individuals to carry out appropriate behaviors and provides them the social awareness through new interactions. The greatest strength of this technology is its strong impact on many aspects of everyday life and users' behaviors. The analytical phase of the research is based on a multiple method survey strategy. Smart lighting proposed in this paper is an urban lighting designed on results obtained from a collective point of view about the social sustainability. In this paper, referring to behavioral design methods, the social behaviors of the people has been studied. Data show that people demands for a deeper experience of social participation, safety perception and energy saving with the meaningful use of interactive and colourful lighting effects. By using intelligent technology, some suggestions are provided in the field of future lighting to consider the new forms of social sustainability.

Keywords: behavior pattern, internet of things, social sustainability, urban lighting

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Authors: Sagri Sharma

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Analysis of diseases integrating multi-factors increases the complexity of the problem and therefore, development of frameworks for the analysis of diseases is an issue that is currently a topic of intense research. Due to the inter-dependence of the various parameters, the use of traditional methodologies has not been very effective. Consequently, newer methodologies are being sought to deal with the problem. Supervised Learning Algorithms are commonly used for performing the prediction on previously unseen data. These algorithms are commonly used for applications in fields ranging from image analysis to protein structure and function prediction and they get trained using a known dataset to come up with a predictor model that generates reasonable predictions for the response to new data. Gene expression profiles generated by DNA analysis experiments can be quite complex since these experiments can involve hypotheses involving entire genomes. The application of well-known machine learning algorithm - Support Vector Machine - to analyze the expression levels of thousands of genes simultaneously in a timely, automated and cost effective way is thus used. The objectives to undertake the presented work are development of a methodology to identify genes relevant to Hepatocellular Carcinoma (HCC) from gene expression dataset utilizing supervised learning algorithms and statistical evaluations along with development of a predictive framework that can perform classification tasks on new, unseen data.

Keywords: artificial intelligence, biomarker, gene expression datasets, hepatocellular carcinoma, machine learning, supervised learning algorithms, support vector machine

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Authors: Hsiao-Ting Huang, Chang Hsueh-Sheng

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Taiwan is an island country which is affected by the monsoon deeply. Under the climate change, the frequency of extreme rainstorm by typhoon becomes more and more often Since 2000. When the extreme rainstorm comes, it will cause serious damage in Taiwan, especially in urban area. It is suffered by the flooding and the government take it as the urgent issue. On the past, the land use of urban planning does not take flood-detention into consideration. With the development of the city, the impermeable surface increase and most of the people live in urban area. It means there is the highly vulnerability in the urban area, but it cannot deal with the surface runoff and the flooding. However, building the detention pond in hydraulic engineering way to solve the problem is not feasible in urban area. The land expropriation is the most expensive construction of the detention pond in the urban area, and the government cannot afford it. Therefore, the management strategy of flooding in urban area should use the existing resource, public facilities land. It can archive the performance of flood-detention through providing the public facilities land with the detention function. As multi-use public facilities land, it also can show the combination of the land use and water agency. To this purpose, this research generalizes the factors of multi-use for public facilities land as flood-detention space with literature review. The factors can be divided into two categories: environmental factors and conditions of public facilities. Environmental factors including three factors: the terrain elevation, the inundation potential and the distance from the drainage system. In the other hand, there are six factors for conditions of public facilities, including area, building rate, the maximum of available ratio etc. Each of them will be according to it characteristic to given the weight for the land use suitability analysis. This research selects the rules of combination from the logical combination. After this process, it can be classified into three suitability levels. Then, three suitability levels will input to the physiographic inundation model for simulating the evaluation of flood-detention respectively. This study tries to respond the urgent issue in urban area and establishes a model of multi-use for public facilities land as flood-detention through the systematic research process of this study. The result of this study can tell which combination of the suitability level is more efficacious. Besides, The model is not only standing on the side of urban planners but also add in the point of view from water agency. Those findings may serve as basis for land use indicators and decision-making references for concerned government agencies.

Keywords: flooding management strategy, land use suitability analysis, multi-use for public facilities land, physiographic inundation model

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Authors: Farid Eltom A. E., Mustafa Abdul-Halim, Abdalla Markaz, Sami Atta, Mohamed Azhari, Ahmed Rashed

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Smart environment sensors integrated with ‘Internet of Things’ (IoT) technology can provide a new concept in tracking, sensing, and monitoring objects in the environment. The aim of the study is to evaluate the farm environmental monitoring system based on (IoT) and to realize the automated management of agriculture and the implementation of precision production. Until now, irrigation monitoring operations in Sudan have been carried out using traditional methods, which is a very costly and unreliable mechanism. However, by utilizing soil moisture sensors, irrigation can be conducted only when needed without fear of plant water stress. The result showed that software application allows farmers to display current and historical data on soil moisture and nutrients in the form of line charts. Design measurements of the soil factors: moisture, electrical, humidity, conductivity, temperature, pH, phosphorus, and potassium; these factors, together with a timestamp, are sent to the data server using the Lora WAN interface. It is considered scientifically agreed upon in the modern era that artificial intelligence works to arrange the necessary procedures to take care of the terrain, predict the quality and quantity of production through deep analysis of the various operations in agricultural fields, and also support monitoring of weather conditions.

Keywords: smart environment, monitoring systems, IoT, LoRa Gateway, center pivot

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Authors: Sukrita Mukherjee, Anindita Chaudhuri

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Soft skills are a crucial aspect for employees, and these skills are not confined to any particular field rather, it guarantees further career growth and job opportunities for employees who are seeking growth. Soft skills are also regarded as personality-specific skills that are observable and are qualitative in nature, which determines an employee’s strengths as a leader. When an employee intends to hold his job, then the person must make effective use of his personal resources, that, in turn, impacts his employability in a positive manner. An employee at his workplace is expected to make effective use of his personal resources. The resources that are to be used by the employee are generally of two types. First type of resources are occupation related, which is related with the educational background of the employee, and the second type of resources are the psychological resources of the employee, such as self-knowledge, career orientation awareness, sense of purpose and emotional literacy, that are considered crucial for an employee in his workplace. The present study is a qualitative study which includes 10 individuals working in IT Sector and Service Industry, respectively. For IT sector, graduate people are considered, and for the Service Industry, individuals who have done a Professional course in order to get into the industry are considered. The emerging themes from the findings after thematic analysis reveal that different aspect of Soft skills such as communication, decision making, constant learning, keeping oneself updated with the latest technological advancement, emotional intelligence are some of the important factors that helps an employee not only to sustain his job, but also grow in his workplace.

Keywords: employabiliy, soft skils, employees, resources, workplace

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Authors: Peien Ma

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Bilingual effect/advantage theorized the positive effect of being bilingual on general cognitive abilities, but it was unknown which factors tend to modulate these bilingualism effects on working memory capacity. This study imposed empirical field research on a group of low-SES emerging bilinguals, Miao people, in the hill tribes of rural China to investigate whether bilingualism affected their verbal working memory performance. 20 Miao-Chinese bilinguals (13 girls and 7 boys with a mean age of 11.45, SD=1.67) and 20 Chinese monolingual peers (13 girls and 7 boys with a mean age of 11.6, SD=0.68) were recruited. These bilingual and monolingual juveniles, matched on age, sex, socioeconomic status, and educational status, completed a language background questionnaire and a standard forward and backward digit span test adapted from Wechsler Adult Intelligence Scale-Revised (WAIS-R). The results showed that bilinguals earned a significantly higher overall mean score of the task, suggesting the superiority of working memory ability over the monolinguals. And bilingual cognitive benefits were independent of proficiency levels in learners’ two languages. The results suggested that bilingualism enhances working memory in sequential bilinguals from low SES backgrounds and shed light on our understanding of the bilingual advantage from a psychological and social perspective.

Keywords: bilingual effects, heritage language, Miao/Hmong language Mandarin, working memory

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Authors: Catharina Lindberg, Cecilia Fagerstrom, Ania Willman

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Patients in high-tech care environments are usually dependent on both formal/informal caregivers and technology, highlighting their vulnerability and challenging their autonomy. Autonomy presumes that a person has education, experience, self-discipline and decision-making capacity. Reference to autonomy in relation to patients in high-tech care environments could, therefore, be considered paradoxical, as in most cases these persons have impaired physical and/or metacognitive capacity. Therefore, to understand the prerequisites for patients to experience autonomy in high-tech care environments and to support them, there is a need to enhance knowledge and understanding of the concept of patient autonomy in this care context. The development of concepts and theories in a practice discipline such as nursing helps to improve both nursing care and nursing education. Theoretical development is important when clarifying a discipline, hence, a theoretical framework could be of use to nurses in high-tech care environments to support and defend the patient’s autonomy. A meta-synthesis was performed with the intention to be interpretative and not aggregative in nature. An amalgamation was made of the results from three previous studies, carried out by members of the same research group, focusing on the phenomenon of patient autonomy from a patient perspective within a caring context. Three basic approaches to theory development: derivation, synthesis, and analysis provided an operational structure that permitted the researchers to move back and forth between these approaches during their work in developing a theoretical framework. The results from the synthesis delineated that patient autonomy in a high-tech care context is: To be in control though trust, co-determination, and transition in everyday life. The theoretical framework contains several components creating the prerequisites for patient autonomy. Assumptions and propositional statements that guide theory development was also outlined, as were guiding principles for use in day-to-day nursing care. Four strategies used by patients to remain or obtain patient autonomy in high-tech care environments were revealed: the strategy of control, the strategy of partnership, the strategy of trust, and the strategy of transition. This study suggests an extended knowledge base founded on theoretical reasoning about patient autonomy, providing an understanding of the strategies used by patients to achieve autonomy in the role of patient, in high-tech care environments. When possessing knowledge about the patient perspective of autonomy, the nurse/carer can avoid adopting a paternalistic or maternalistic approach. Instead, the patient can be considered to be a partner in care, allowing care to be provided that supports him/her in remaining/becoming an autonomous person in the role of patient.

Keywords: autonomy, caring, concept development, high-tech care, theory development

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Authors: Jill Stephenson, Marie Vaganay, Robert Cameron, Caoimhe McGurk, Neil Hewitt

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Purpose: The key aim of the research was to identify the secondary stressors experienced by businesses affected by single or repeated flooding and to determine to what extent businesses were affected by these stressors, along with any resulting impact on health. Additionally, the research aimed to establish the likelihood of businesses being re-exposed to the secondary stressors through assessing awareness of flood risk, implementation of property protection measures and level of community resilience. Design/methodology/approach: The chosen research method involved the distribution of a questionnaire survey to businesses affected by either single or repeated flood events. The questionnaire included the Impact of Event Scale (a 15-item self-report measure which assesses subjective distress caused by traumatic events). Findings: 55 completed questionnaires were returned by flood impacted businesses. 89% of the businesses had sustained internal flooding while 11% had experienced external flooding. The results established that the key secondary stressors experienced by businesses, in order of priority, were: flood damage, fear of reoccurring flooding, prevention of access to the premise/closure, loss of income, repair works, length of closure and insurance issues. There was a lack of preparedness for potential future floods and consequent vulnerability to the emergence of secondary stressors among flood affected businesses, as flood resistance or flood resilience measures had only been implemented by 11% and 13% respectively. In relation to the psychological repercussions, the Impact of Event scores suggested that potential prevalence of post-traumatic stress disorder (PTSD) was noted among 8 out of 55 respondents (l5%). Originality/value: The results improve understanding of the enduring repercussions of flood events on businesses, indicating that not only residents may be susceptible to the detrimental health impacts of flood events and single flood events may be just as likely as reoccurring flooding to contribute to ongoing stress. Lack of financial resources is a possible explanation for the lack of implementation of property protection measures among businesses, despite 49% experiencing flooding on multiple occasions. Therefore it is recommended that policymakers should consider potential sources of financial support or grants towards flood defences for flood impacted businesses. Any form of assistance should be made available to businesses at the earliest opportunity as there was no significant association between the time of the last flood event and the likelihood of experiencing PTSD symptoms.

Keywords: flood event, flood resilience, flood resistance, PTSD, secondary stressors

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Authors: Nancy Ayman Kamal Mohamed Mehrz

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As the economies of other countries in the Mediterranean Basin, the tourism sector in our country has a high denominator in economics. Tourism businesses, which are building blocks of tourism, sector faces with a variety of problems during their activities. These problems faced make business efficiency and competition conditions of the businesses difficult. Most of the problems faced by the tourism businesses and the information of consumers about consumers’ rights were used in this study, which is conducted to determine the problems of tourism businesses in the Central Anatolia Region. It is aimed to contribute the awareness of staff and executives working at tourism sector and to attract attention of businesses active concurrently with tourism sector and legislators. E-tourism is among the issues that have recently been entered into the field of tourism. In order to achieve this type of tourism, Information and Communications Technology (or ICT) infrastructures as well as Co-governmental organizations and tourism resources are important. In this study, the opinions of managers and tourism officials about the e-tourism in Leman city were measured; it also surveyed the impact of level of digital literacy of managers and tourism officials on attracting tourists. This study was conducted. One of the environs of the Esfahan province. This study is a documentary – survey and the sources include library resources and also questionnaires. The results obtained indicate that if managers use ICT, it may help e-tourism to be developed in the region, and increasing managers’ beliefs on e-tourism and upgrading their level of digital literacy may affect e-tourism development.

Keywords: financial problems, the problems of tourism businesses, tourism businesses, internet, marketing, tourism, tourism management economic competitiveness, enhancing competitiveness

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Authors: Rommila Chandra, Harshika Choudhary

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The present-day tourism sector is globally developing at a fast pace, searching for new ideas and new venues. In the Indian Himalayan Region (IHR), tourism has experienced a vast growth and continuous diversification over the last few years, thus becoming one of the fastest-growing economic sectors in India. With its majestic landscape, high peaks, rich floral and faunal diversity, and cultural history, the IHR has continuously attracted tourists and pilgrims from across the globe. The IHR has attracted a vast range of visitors who seek adventure sports, natural and spiritual solace, peace, cultural assets, food, and festivals, etc. Thus, the multi-functionality of the region has turned tourism into a key component of economic growth for the rural communities in the hills. For the local mountain people, it means valuable economic opportunity for income generation, and for the government and entrepreneurs, it brings profits. As the urban cities gain attention and investment in India, efforts have to be made to protect, safeguard, and strengthen the cultural, spiritual, and natural heritage of IHR for sustainable livelihood development. Furthermore, the socio-economic and environmental insecurities, along with geographical isolation, adds to the challenging survival in the tough terrains of IHR, creating a major threat of outmigration, land abandonment, and degradation. The question the paper intends to answer is: whether the rural community of IHR is aware of the new global trends in rural tourism and the extent of their willingness to adapt to the evolving tourism industry, which impacts the rural economy, including sustainable livelihood opportunity. The objective of the paper is to discuss the integrated nature of rural tourism, which widely depends upon natural resources, cultural heritage, agriculture/horticulture, infrastructural development, education, social awareness, and willingness of the locals. The sustainable management of all these different rural activities can lead to long-term livelihood development and social upliftment. It highlights some gap areas and recommends fewcommunity-based coping measures which the local people can adopt amidst the disorganized sector of rural tourism. Lastly, the main contribution is the exploratory research of the rural tourism vulnerability in the IHR, which would further help in studying the resilience of the tourism sector in the rural parts of a developing nation.

Keywords: community-based approach, sustainable livelihood development, Indian Himalayan region, rural tourism

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Authors: Lilian Mbinya Muasa

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Climate change is inevitable and a global challenge with long term implications to the sustainable development of many countries today. The negative impacts of climate change are creating far reaching social, economic and environmental problems threatening lives and livelihoods of millions of people in the world. Developing countries especially sub-Saharan countries are more vulnerable to climate change due to their weak ecosystem, low adaptive capacity and high dependency on rain fed agriculture. Countries in Sub-Saharan Africa are more vulnerable to climate change impacts due to their weak adaptive capacity and over-reliance on rain fed agriculture. In Kenya, 78% of the rural communities are poor farmers who heavily rely on rain fed agriculture thus are directly affected by climate change impacts.Currently, many parts of Kenya are experiencing successive droughts which are contributing to persistently unstable and declining agricultural productivity especially in semi arid eastern Kenya. As a result, thousands of rural communities repeatedly experience food insecurity which plunge them to an ever over-reliance on relief food from the government and Non-Governmental Organization In addition, they have adopted poverty coping strategies to diversify their income, for instance, deforestation to burn charcoal, sand harvesting and overgrazing which instead contribute to environmental degradation.This research was conducted in Makueni County which is classified as one of the most food insecure counties in Kenya and experiencing acute environmental degradation. The study aimed at analyzing the adaptive capacity to climate change across farming households of Makueni County in Kenya by, 1) analyzing adaptive capacity to climate change and agricultural productivity across farming households, 2) identifying factors that contribute to differences in adaptive capacity across farming households, and 3) understanding the relationship between climate change, agricultural productivity and adaptive capacity. Analytical Hierarchy Process (AHP) was applied to determine adaptive capacity and Total Factor Productivity (TFP) to determine Agricultural productivity per household. Increase in frequency of prolonged droughts and scanty rainfall. Preliminary findings indicate a magnanimous decline in agricultural production in the last 10 years in Makueni County. In addition, there is an over reliance of households on indigenous knowledge which is no longer reliable because of the unpredictability nature of climate change impacts. These findings on adaptive capacity across farming households provide the first step of developing and implementing action-oriented climate change policies in Makueni County and Kenya.

Keywords: adaptive capacity, agricultural productivity, climate change, vulnerability

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Authors: Reza Mohammadzadeh

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The major challenges in geotechnical engineering in underground spaces arise from uncertainties and different probabilities. The collection, collation, and collaboration of existing data to incorporate them in analysis and design for given prospect evaluation would be a reliable, practical problem solving method under uncertainty. Machine learning (ML) is a subfield of artificial intelligence in statistical science which applies different techniques (e.g., Regression, neural networks, support vector machines, decision trees, random forests, genetic programming, etc.) on data to automatically learn and improve from them without being explicitly programmed and make decisions and predictions. In this paper, a conceptual database schema of geotechnical risks in underground coal mining based on a cloud system architecture has been designed. A new approach of risk assessment using a three-dimensional risk matrix supported by the level of knowledge (LoK) has been proposed in this model. Subsequently, the model workflow methodology stages have been described. In order to train data and LoK models deployment, an ML platform has been implemented. IBM Watson Studio, as a leading data science tool and data-driven cloud integration ML platform, is employed in this study. As a Use case, a data set of geotechnical hazards and risk assessment in underground coal mining were prepared to demonstrate the performance of the model, and accordingly, the results have been outlined.

Keywords: data model, geotechnical risks, machine learning, underground coal mining

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Authors: Sagir M. Yusuf, Chris Baber

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In this paper, we describe how Bayesian inferential reasoning will contributes in obtaining a well-satisfied prediction for Distributed Constraint Optimization Problems (DCOPs) with uncertainties. We also demonstrate how DCOPs could be merged to multi-agent knowledge understand and prediction (i.e. Situation Awareness). The DCOPs functions were merged with Bayesian Belief Network (BBN) in the form of situation, awareness, and utility nodes. We describe how the uncertainties can be represented to the BBN and make an effective prediction using the expectation-maximization algorithm or conjugate gradient descent algorithm. The idea of variable prediction using Bayesian inference may reduce the number of variables in agents’ sampling domain and also allow missing variables estimations. Experiment results proved that the BBN perform compelling predictions with samples containing uncertainties than the perfect samples. That is, Bayesian inference can help in handling uncertainties and dynamism of DCOPs, which is the current issue in the DCOPs community. We show how Bayesian inference could be formalized with Distributed Situation Awareness (DSA) using uncertain and missing agents’ data. The whole framework was tested on multi-UAV mission for forest fire searching. Future work focuses on augmenting existing architecture to deal with dynamic DCOPs algorithms and multi-agent information merging.

Keywords: DCOP, multi-agent reasoning, Bayesian reasoning, swarm intelligence

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Authors: Basak Ozkendirci

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The discovery of chemical dyes, the development of regenerated fibers, and warp knitting technology have enormous effects on the fashion world. The trends created by the information obtained in the context of various studies today shape the fashion world. Trend analysts must follow scientific developments as well as sociological events, political developments and artwork to obtain healthy data on trends. Digital printing technologies have changed the dynamics of textile printing production and also the style of printed designs. Fashion designers already have started design 3D printed accessories and garments. The research fields like the internet of things, artificial intelligence, hologram technologies, mechatronics, energy storage systems, nanotechnology are seen as the technologies that will change the social life and economy of the future. It is clear that research carried out in these areas will affect the textiles of the future and whereat the trends of fashion. The article aims to create a future vision for trend researchers and designers by giving clues about the changes to be experienced in the fashion world. In the first part of the article, information about the scientific studies that are thought to shape the future is given, and the forecasting about how the inventions that can be obtained from these studies can be adapted at the textile are presented. In the second part of the article, examples of how the new generation of innovative textiles will affect the daily life experience of the user are given.

Keywords: biotextiles, fashion trends, nanotextiles, new materials, smart textiles, techno textiles

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Authors: Rupam Saikia, Ashim Kanti Dey

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Sonapur is an intense hilly region on the border of Assam and Meghalaya lying in North-East India and is very near to a seismic fault named as Dauki besides which makes the region seismically active. Besides, these recently two earthquakes of magnitude 6.7 and 6.9 have struck North-East India in January and April 2016. Also, the slope concerned for this study is adjacent to NH 44 which for a long time has been a sole important connecting link to the states of Manipur and Mizoram along with some parts of Assam and so has been a cause of considerable loss to life and property since past decades as there has been several recorded incidents of landslide, road-blocks, etc. mostly during the rainy season which comes into news. Based on this issue this paper reports a static and dynamic slope stability analysis of Sonapur which has been carried out in MIDAS GTS NX. The slope being highly unreachable due to terrain and thick vegetation in-situ test was not feasible considering the current scope available so disturbed soil sample was collected from the site for the determination of strength parameters. The strength parameters were so determined for varying relative density with further variation in water content. The slopes were analyzed considering plane strain condition for three slope heights of 5 m, 10 m and 20 m which were then further categorized based on slope angles 30, 40, 50, 60, and 70 considering the possible extent of steepness. Initially static analysis under dry state was performed then considering the worst case that can develop during rainy season the slopes were analyzed for fully saturated condition along with partial degree of saturation with an increase in the waterfront. Furthermore, dynamic analysis was performed considering the El-Centro Earthquake which had a magnitude of 6.7 and peak ground acceleration of 0.3569g at 2.14 sec for the slope which were found to be safe during static analysis under both dry and fully saturated condition. Some of the conclusions were slopes with inclination above 40 onwards were found to be highly vulnerable for slopes of height 10 m and above even under dry static condition. Maximum horizontal displacement showed an exponential increase with an increase in inclination from 30 to 70. The vulnerability of the slopes was seen to be further increased during rainy season as even slopes of minimal steepness of 30 for height 20 m was seen to be on the verge of failure. Also, during dynamic analysis slopes safe during static analysis were found to be highly vulnerable. Lastly, as a part of the study a comparative study on Strength Reduction Method (SRM) versus Limit Equilibrium Method (LEM) was also carried out and some of the advantages and disadvantages were figured out.

Keywords: dynamic analysis, factor of safety, slope stability, strength reduction method

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Authors: Katie E. Jennings

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The interactions between family dynamics and environmental factors with mental health vulnerability in individuals are well known and are a theme for on-going research and debate. The impact upon mental health issues and forensic issues on family dynamics, experience, and emotional wellbeing cannot be over-Emphasised. For forensic patients with diagnosed mental disorders, these relationships and environments may have also been functionally linked to the development and maintenance of those disorders; with significant adverse childhood experiences being a common feature of many Patient’s histories. Mental health hospitals remove the patient from their home environments and provide treatment outside of these relationships and often outside of the home area. There is, therefore, a major focus on Services ensuring that patients are able to build and maintain relationships with family and friends, requiring services to involve families in Patients' care and treatment wherever possible. There are standards set by Government and clinical bodies that require absolute demonstration of the inclusion of family and friends in all aspects of the care and treatment of forensic patients. For some patients and family members, this push to take on a “role” in care can be unhelpful, extremely stressful, and has constant implications for the potential delicate reparation of relationships. Based on work undertaken for over 20 years in forensic mental health settings, this paper explores the positive psychology approach to a dimensional model to family inclusion in mental health care that learns from family court work and allows for the maintenance of relationships to be at both proximal and Distil levels; to prevent the replication of abuse, decrease the need for falsehoods and assist the recovery of all. The model is based on allowing families to choose to not be involved or be involved in different ways if this is seen to be more helpful. It also allows patients to choose the level of potential involvement that they would find helpful, and for this to be reviewed at a timeframe agreed by all parties, rather than when the next survey is due or the patient has a significant care meeting. This paper is significant as there is a lack of research to support services to use a positive psychology approach to work in this area, the assumption that being asked to be involved must be positive for all seems naïve at best for this patient group. Work relating to the psychology of family can significantly contribute to the development of knowledge in this area. The development of a dimensional model will support choice within families and assist in the development of more honest and open relationships.

Keywords: family dynamics, forensic, mental disorder, positive psychology

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Authors: Pooja Rani, G. S. Mahapatra, S. K. Pandey

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In this paper, we propose a novel approach of Neural Network and Particle Swarm Optimization methods for software reliability prediction. We first explain how to apply compound function in neural network so that we can derive a Flexible Logistic (S-shaped) Growth Curve (FLGC) model. This model mathematically represents software failure as a random process and can be used to evaluate software development status during testing. To avoid trapping in local minima, we have applied Particle Swarm Optimization method to train proposed model using failure test data sets. We drive our proposed model using computational based intelligence modeling. Thus, proposed model becomes Neuro-Particle Swarm Optimization (NPSO) model. We do test result with different inertia weight to update particle and update velocity. We obtain result based on best inertia weight compare along with Personal based oriented PSO (pPSO) help to choose local best in network neighborhood. The applicability of proposed model is demonstrated through real time test data failure set. The results obtained from experiments show that the proposed model has a fairly accurate prediction capability in software reliability.

Keywords: software reliability, flexible logistic growth curve model, software cumulative failure prediction, neural network, particle swarm optimization

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Authors: Simon Arunga, Teddy Kwaga, Rita Kageni, Michael Gichangi, Nyawira Mwangi, Fred Kagwa, Rogers Mwavu, Amos Baryashaba, Luis F. Nakayama, Katharine Morley, Michael Morley, Leo A. Celi, Jessica Haberer, Celestino Obua

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Purpose: The main aim for creating the Multimodal Database of Retinal Images for Africa (MoDRIA) was to provide a publicly available repository of retinal images for responsible researchers to conduct algorithm development in a bid to curb the challenges of ophthalmic artificial intelligence (AI) in Africa. Methods: Data and retina images were ethically sourced from sites in Uganda and Kenya. Data on medical history, visual acuity, ocular examination, blood pressure, and blood sugar were collected. Retina images were captured using fundus cameras (Foru3-nethra and Canon CR-Mark-1). Images were stored on a secure online database. Results: The database consists of 7,859 retinal images in portable network graphics format from 1,988 participants. Images from patients with human immunodeficiency virus were 18.9%, 18.2% of images were from hypertensive patients, 12.8% from diabetic patients, and the rest from normal’ participants. Conclusion: Publicly available data repositories are a valuable asset in the development of AI technology. Therefore, is a need for the expansion of MoDRIA so as to provide larger datasets that are more representative of Sub-Saharan data.

Keywords: retina images, MoDRIA, image repository, African database

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Authors: Rasha Shoumar

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Background: Since the onset of the Syria crisis in 2011, Jordan has hosted many Syrian refugees, many of which are residing in urban and rural areas. Vulnerability of refugees has increased due to the COVID-19 pandemic, adding to their already existing challenge in access to medical services, rendering them vulnerable to the complications of untreated medical conditions and amplifying their risk for severe COVID-19 disease. To improve health outcomes and access to health care services in a COVID-19 context, IOM (The International Organization for Migration) provided health services including awareness raising, direct primary health care through mobile teams and referrals to secondary services were extended to the vulnerable populations of refugees. Method: 6 community health volunteers were trained and deployed to different governorates to provide COVID-19 and non-communicable disease awareness and collect data rated to non-communicable disease and access to medical health services. Primary health care services were extended to 7 governorates through a mobile medical team, providing medical management. The collected Data was reviewed and analyzed. Results: 2150 refugees in rural areas were reached out by community health volunteers, out of which 78 received their medications through the Ministry of Health, 121 received their medications through different non-governmental organizations, 665 patients couldn’t afford buying any medications, 1286 patients were occasionally buying their medications when they were able to afford it. 853 patients received medications and follow up through IOM mobile clinics, the most common conditions were hypertension, diabetes, hyperlipidemia, anemia, heart disease, thyroid disease, asthma, seizures, and psychiatric conditions. 709 of these patients had more than 3 of the comorbidities. Multiple cases were referred for secondary and tertiary lifesaving interventions. Conclusion: Non communicable diseases are highly prevalent among refugee population in Jordan, access to medical services have proven to be a challenge in rural areas especially during the COVID-19 era, many of the patients have multiple uncontrolled medical conditions placing them at risk for complications and risk for severe COVID-19 disease. Deployment of mobile clinics to rural areas plays an essential role in managing such medical conditions, thus improving the continuum of health care approach, physical and mental wellbeing of refugees and reducing the risk for severe COVID-19 disease among this group, taking us one step forward toward universal health access.

Keywords: COVID-19, refugees, mobile clinics, primary health care

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Authors: Houda Najeh, Mahendra Pratap Singh, Stéphane Ploix, Antoine Caucheteux, Karim Chabir, Mohamed Naceur Abdelkrim

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Building system is highly vulnerable to different kinds of faults and human misbehaviors. Energy efficiency and user comfort are directly targeted due to abnormalities in building operation. The available fault diagnosis tools and methodologies particularly rely on rules or pure model-based approaches. It is assumed that model or rule-based test could be applied to any situation without taking into account actual testing contexts. Contextual tests with validity domain could reduce a lot of the design of detection tests. The main objective of this paper is to consider fault validity when validate the test model considering the non-modeled events such as occupancy, weather conditions, door and window openings and the integration of the knowledge of the expert on the state of the system. The concept of heterogeneous tests is combined with test validity to generate fault diagnoses. A combination of rules, range and model-based tests known as heterogeneous tests are proposed to reduce the modeling complexity. Calculation of logical diagnoses coming from artificial intelligence provides a global explanation consistent with the test result. An application example shows the efficiency of the proposed technique: an office setting at Grenoble Institute of Technology.

Keywords: heterogeneous tests, validity, building system, sensor grids, sensor fault, diagnosis, fault detection and isolation

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Authors: Muhammad Saad Aslam

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In 2021, our organization has launched our proprietary mobile App i.e. Farm Intelligence platform, an industrial-first precision agriculture solution, to Pakistan. It was piloted at 47 locations (spanning around 1,200 hectares of land), addressing growers’ pain points by bringing the benefits of precision agriculture to their doorsteps. This year, we have extended its reach by more than 10 times (nearly 130,000 hectares of land) in almost 600 locations across the country. The project team selected highly infested areas to set up traps, which then enabled the sales team to initiate evidence-based conversations with the grower community about preventive crop protection products that includes pesticides and insecticides. Mega farmer meeting field visits and demonstrations plots coupled with extensive marketing activities, were setup to include farmer community. With the help of App real-time pest monitoring (using heat maps and infestation prediction through predictive analytics) we have equipped our growers with on spot insights that will help them optimize pesticide applications. Heat maps allow growers to identify infestation hot spots to fine-tune pesticide delivery, while predictive analytics enable preventive application of pesticides before the situation escalates. Ultimately, they empower growers to keep their crops safe for a healthy harvest.

Keywords: precision pest management, precision agriculture, real time pest tracking, pest forecasting

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Authors: Krishna Mohan Bathula, Fatou Bintou Loucoubar, FNU Kaleemunnisa, Christelle Scharff, Mark Anthony De Castro

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Voice recognition algorithms such as automatic speech recognition and text-to-speech systems with African languages can play an important role in bridging the digital divide of Artificial Intelligence in Africa, contributing to the establishment of a fully inclusive information society. This paper proposes a Deep Learning model that can classify the user responses as inputs for an interactive voice response system. A dataset with Wolof language words ‘yes’ and ‘no’ is collected as audio recordings. A two stage Data Augmentation approach is adopted for enhancing the dataset size required by the deep neural network. Data preprocessing and feature engineering with Mel-Frequency Cepstral Coefficients are implemented. Convolutional Neural Networks (CNNs) have proven to be very powerful in image classification and are promising for audio processing when sounds are transformed into spectra. For performing voice response classification, the recordings are transformed into sound frequency feature spectra and then applied image classification methodology using a deep CNN model. The inference model of this trained and reusable Wolof voice response recognition system can be integrated with many applications associated with both web and mobile platforms.

Keywords: automatic speech recognition, interactive voice response, voice response recognition, wolof word classification

Procedia PDF Downloads 118