Search results for: data loss

Authors: Beata Bilska, Marzena Tomaszewska, Danuta Kolozyn-Krajewska

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Food loss and food waste are a global problem of the modern economy. The research undertaken aimed to analyze how food is handled in catering establishments when it comes to food waste and to demonstrate the main ways of management with foods/dishes not served to consumers. A survey study was conducted from January to June 2019. The selection of catering establishments participating in the study was deliberate. The study included establishments located only in Mazowieckie Voivodeship (Poland). Forty-two completed questionnaires were collected. In some questions, answers were based on a 5-point scale of 1 to 5 (from "always" / "every day" to "never"). The survey also included closed questions with a suggested cafeteria of answers. The respondents stated that in their workplaces, dishes served cold and hot ready meals are discarded every day or almost every day (23.7% and 20.5% of answers respectively). A procedure most frequently used for dealing with dishes not served to consumers on a given day is their storage at a cool temperature until the following day. In the research, 1/5 of respondents admitted that consumers "always" or "usually" leave uneaten meals on their plates, and over 41% "sometimes" do so. It was found additionally that food not used in the foodservice sector is most often thrown into a public container for rubbish. Most often thrown into the public container (with communal trash) were: expired products (80.0%), plate waste (80.0%) and inedible products (fruit and vegetable peels, eggshells) (77.5%). Most frequently into the container dedicated only to food waste were thrown out used deep-frying oil (62.5%). 10% of respondents indicated that inedible products in their workplaces are allocated for animal feeds. Food waste in the foodservice sector remains an insufficiently studied issue, as owners of these objects are often unwilling to disclose data about the subject. Incorrect ways of management with foods not served to consumers were observed. There is a need to develop educational activities for employees and management in the context of food waste management in the foodservice sector.

Keywords: food waste, inedible products, plate waste, used deep-frying oil

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Authors: R. Gautam, P. Sastha Kanagasabai, G. N. Rathna

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The proposed healthcare system enables quadriplegic patients, people with severe motor disabilities to send commands to electronic devices and monitor their vitals. The growth of Brain-Computer-Interface (BCI) has led to rapid development in 'assistive systems' for the disabled called 'assistive domotics'. Brain-Computer-Interface is capable of reading the brainwaves of an individual and analyse it to obtain some meaningful data. This processed data can be used to assist people having speech disorders and sometimes people with limited locomotion to communicate. In this Project, Emotiv EPOC Headset is used to obtain the electroencephalogram (EEG). The obtained data is processed to communicate pre-defined commands over the internet to the desired mobile phone user. Other Vital Information like the heartbeat, blood pressure, ECG and body temperature are monitored and uploaded to the server. Data analytics enables physicians to scan databases for a specific illness. The Data is processed in Intel Edison, system on chip (SoC). Patient metrics are displayed via Intel IoT Analytics cloud service.

Keywords: brain computer interface, Intel Edison, Emotiv EPOC, IoT analytics, electroencephalogram

Procedia PDF Downloads 189

Authors: Ren Junn Hwang, Chung-Chien Lu, Jain-Shing Wu

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Cloud outsource storage is one of important services in cloud computing. Cloud users upload data to cloud servers to reduce the cost of managing data and maintaining hardware and software. To ensure data confidentiality, users can encrypt their files before uploading them to a cloud system. However, retrieving the target file from the encrypted files exactly is difficult for cloud server. This study proposes a protocol for performing multikeyword searches for encrypted cloud data by applying k-nearest neighbor technology. The protocol ranks the relevance scores of encrypted files and keywords, and prevents cloud servers from learning search keywords submitted by a cloud user. To reduce the costs of file transfer communication, the cloud server returns encrypted files in order of relevance. Moreover, when a cloud user inputs an incorrect keyword and the number of wrong alphabet does not exceed a given threshold; the user still can retrieve the target files from cloud server. In addition, the proposed scheme satisfies security requirements for outsourced data storage.

Keywords: fault-tolerance search, multi-keywords search, outsource storage, ranked search, searchable encryption

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Authors: Fatemah A. Alqallaf, Debasis Kundu

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The aim of this paper is to introduce a bivariate inverse generalized exponential distribution which has a singular component. The proposed bivariate distribution can be used when the marginals have heavy-tailed distributions, and they have non-monotone hazard functions. Due to the presence of the singular component, it can be used quite effectively when there are ties in the data. Since it has four parameters, it is a very flexible bivariate distribution, and it can be used quite effectively for analyzing various bivariate data sets. Several dependency properties and dependency measures have been obtained. The maximum likelihood estimators cannot be obtained in closed form, and it involves solving a four-dimensional optimization problem. To avoid that, we have proposed to use an EM algorithm, and it involves solving only one non-linear equation at each `E'-step. Hence, the implementation of the proposed EM algorithm is very straight forward in practice. Extensive simulation experiments and the analysis of one data set have been performed. We have observed that the proposed bivariate inverse generalized exponential distribution can be used for modeling dependent competing risks data. One data set has been analyzed to show the effectiveness of the proposed model.

Keywords: Block and Basu bivariate distributions, competing risks, EM algorithm, Marshall-Olkin bivariate exponential distribution, maximum likelihood estimators

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Authors: Singara Singh Kasana, Pankaj Garg

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In this paper, a blind data hiding technique based on interpolation of sub sampled versions of a cover image is proposed. Sub sampled image is taken as a reference image and an interpolated image is generated from this reference image. Then difference between original cover image and interpolated image is used to embed secret data. Comparisons with the existing interpolation based techniques show that proposed technique provides higher embedding capacity and better visual quality marked images. Moreover, the performance of the proposed technique is more stable for different images.

Keywords: interpolation, image subsampling, PSNR, SIM

Procedia PDF Downloads 584

Authors: M. H. Rajikin, S. M. M. Syairah, A. R. Sharaniza

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Effects of nicotine on pre-partum body weight and preimplantation embryonic development has been reported previously. Present study was conducted to determine the effects of annatto (Bixa orellana)-derived delta-tocotrienol (TCT) (with presence of 10% gamma-TCT isomer) on the nicotine-induced reduction in body weight and 8-cell embryonic growth in mice. Twenty four 6-8 weeks old (23-25g) female balb/c mice were randomly divided into four groups (G1-G4; n=6). Those groups were subjected to the following treatments for 7 consecutive days: G1 (control) were gavaged with 0.1 ml tocopherol stripped corn oil, G2 was subcutaneously (s.c.) injected with 3 mg/kg/day of nicotine, G3 received concurrent treatment of nicotine (3 mg/kg/day) and 60 mg/kg/day of δ-TCT mixture (contains 90% delta & 10% gamma isomers) and G4 was given 60 mg/kg/day of δ-TCT mixture alone. Body weights were recorded daily during the treatment. On Day 8, females were superovulated with 5 IU Pregnant Mare’s Serum Gonadotropin (PMSG) for 48 hours followed with 5 IU human Chorionic Gonadotropin (hCG) before mated with males at the ratio of 1:1. Females were sacrificed by cervical dislocation for embryo collection 48 hours post-coitum. Collected embryos were cultured in vitro. Results showed that throughout Day 1 to Day 7, the body weight of nicotine treated group (G2) was significantly lower (p<0.05) than that of G1, G3 and G4. Intervention with δ-TCT mixture (G3) managed to increase the body weight close to the control group. This is also observed in the group treated with δ-TCT mixture alone (G4). The development of 8-cell embryos following in vitro culture (IVC) was totally inhibited in G2. Intervention with δ-TCT mixture (G3) resulted in the production of 8-cell embryos, although it was not up to that of the control group. Treatment with δ-TCT mixture alone (G4) caused significant increase in the average number of produced 8-cell embryo compared to G1. Present data indicated that δ-TCT mixture was able to reverse the body weight loss in nicotine treated mice and the development of 8-cell embryos was also improved.

Keywords: δ-tocotrienol, body weight, nicotine, preimplantation embryonic development

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Authors: Sajid Hussain

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The complex domain approach for image segmentation based on active contour has been designed, which deforms step by step to partition an image into numerous expedient regions. A novel region-based trigonometric complex pressure force function is proposed, which propagates around the region of interest using image forces. The signed trigonometric force function controls the propagation of the active contour and the active contour stops on the exact edges of the object accurately. The proposed model makes the level set function binary and uses Gaussian smoothing kernel to adjust and escape the re-initialization procedure. The working principle of the proposed model is as follows: The real image data is transformed into complex data by iota (i) times of image data and the average iota (i) times of horizontal and vertical components of the gradient of image data is inserted in the proposed model to catch complex gradient of the image data. A simple finite difference mathematical technique has been used to implement the proposed model. The efficiency and robustness of the proposed model have been verified and compared with other state-of-the-art models.

Keywords: image segmentation, active contour, level set, Mumford and Shah model

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Authors: Tadashi Watanabe, Jinya Katsuyama, Akihiro Mano

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The estimation of leak flow rates through narrow cracks in structures is of importance for nuclear reactor safety, since the leak flow could be detected before occurrence of loss-of-coolant accidents. The two-phase critical leak flow rates are calculated using the system analysis code, and two representative non-homogeneous critical flow models, Henry-Fauske model and Ransom-Trapp model, are compared. The pressure decrease and vapor generation in the crack, and the leak flow rates are found to be larger for the Henry-Fauske model. It is shown that the leak flow rates are not affected by the structural temperature, but affected largely by the roughness of crack surface.

Keywords: crack, critical flow, leak, roughness

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Authors: Mehran Asadi, Afrand Agah

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In data mining, partitioning is used as a fundamental tool for classification. With the help of partitioning, we study the structure of data, which allows us to envision decision rules, which can be applied to classification trees. In this research, we used online social network dataset and all of its attributes (e.g., Node features, labels, etc.) to determine what constitutes an above average chance of being a divergent node. We used the R statistical computing language to conduct the analyses in this report. The data were found on the UC Irvine Machine Learning Repository. This research introduces the basic concepts of classification in online social networks. In this work, we utilize overfitting and describe different approaches for evaluation and performance comparison of different classification methods. In classification, the main objective is to categorize different items and assign them into different groups based on their properties and similarities. In data mining, recursive partitioning is being utilized to probe the structure of a data set, which allow us to envision decision rules and apply them to classify data into several groups. Estimating densities is hard, especially in high dimensions, with limited data. Of course, we do not know the densities, but we could estimate them using classical techniques. First, we calculated the correlation matrix of the dataset to see if any predictors are highly correlated with one another. By calculating the correlation coefficients for the predictor variables, we see that density is strongly correlated with transitivity. We initialized a data frame to easily compare the quality of the result classification methods and utilized decision trees (with k-fold cross validation to prune the tree). The method performed on this dataset is decision trees. Decision tree is a non-parametric classification method, which uses a set of rules to predict that each observation belongs to the most commonly occurring class label of the training data. Our method aggregates many decision trees to create an optimized model that is not susceptible to overfitting. When using a decision tree, however, it is important to use cross-validation to prune the tree in order to narrow it down to the most important variables.

Keywords: online social networks, data mining, social cloud computing, interaction and collaboration

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Authors: Seyoung Kim, Jeongmin Kim, Kwang Ryel Ryu

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A database that records average traffic speeds measured at five-minute intervals for all the links in the traffic network of a metropolitan city. While learning from this data the models that can predict future traffic speed would be beneficial for the applications such as the car navigation system, building predictive models for every link becomes a nontrivial job if the number of links in a given network is huge. An advantage of adopting k-nearest neighbor (k-NN) as predictive models is that it does not require any explicit model building. Instead, k-NN takes a long time to make a prediction because it needs to search for the k-nearest neighbors in the database at prediction time. In this paper, we investigate how much we can speed up k-NN in making traffic speed predictions by reducing the amount of data to be searched for without a significant sacrifice of prediction accuracy. The rationale behind this is that we had a better look at only the recent data because the traffic patterns not only repeat daily or weekly but also change over time. In our experiments, we build several different k-NN models employing different sets of features which are the current and past traffic speeds of the target link and the neighbor links in its up/down-stream. The performances of these models are compared by measuring the average prediction accuracy and the average time taken to make a prediction using various amounts of data.

Keywords: big data, k-NN, machine learning, traffic speed prediction

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Authors: Gampel Alexander, Mazzuchi Thomas, Sarkani Shahram

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The cybersecurity landscape is constantly evolving, and organizations must adapt to the changing threat environment to protect their assets. The implementation of the NIST Risk Management Framework (RMF) has become critical in ensuring the security and safety of industrial control and automation systems. However, cybersecurity professionals are facing challenges in implementing RMF, leading to systems operating without authorization and being non-compliant with regulations. The current approach to RMF implementation based on business practices is limited and insufficient, leaving organizations vulnerable to cyberattacks resulting in the loss of personal consumer data and critical infrastructure details. To address these challenges, this research proposes a Model-Based Systems Engineering (MBSE) approach to implementing cybersecurity controls and assessing risk through the RMF process. The study emphasizes the need to shift to a modeling approach, which can streamline the RMF process and eliminate bloated structures that make it difficult to receive an Authorization-To-Operate (ATO). The study focuses on the practical application of MBSE in industrial control and automation systems to improve the security and safety of operations. It is concluded that MBSE can be used to solve the implementation challenges of the NIST RMF process and improve the security of industrial control and automation systems. The research suggests that MBSE provides a more effective and efficient method for implementing cybersecurity controls and assessing risk through the RMF process. The future work for this research involves exploring the broader applicability of MBSE in different industries and domains. The study suggests that the MBSE approach can be applied to other domains beyond industrial control and automation systems.

Keywords: authorization-to-operate (ATO), industrial control systems (ICS), model-based system’s engineering (MBSE), risk management framework (RMF)

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Authors: Dewi Juliah Ratnaningsih, Imas Sukaesih Sitanggang

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Classification is one of data mining techniques that aims to discover a model from training data that distinguishes records into the appropriate category or class. Data mining classification methods can be applied in education, for example, to determine the classification of non-active students in Indonesia Open University. This paper presents a comparison of three methods of classification: Naïve Bayes, Bagging, and C.45. The criteria used to evaluate the performance of three methods of classification are stratified cross-validation, confusion matrix, the value of the area under the ROC Curve (AUC), Recall, Precision, and F-measure. The data used for this paper are from the non-active Indonesia Open University students in registration period of 2004.1 to 2012.2. Target analysis requires that non-active students were divided into 3 groups: C1, C2, and C3. Data analyzed are as many as 4173 students. Results of the study show: (1) Bagging method gave a high degree of classification accuracy than Naïve Bayes and C.45, (2) the Bagging classification accuracy rate is 82.99 %, while the Naïve Bayes and C.45 are 80.04 % and 82.74 % respectively, (3) the result of Bagging classification tree method has a large number of nodes, so it is quite difficult in decision making, (4) classification of non-active Indonesia Open University student characteristics uses algorithms C.45, (5) based on the algorithm C.45, there are 5 interesting rules which can describe the characteristics of non-active Indonesia Open University students.

Keywords: comparative analysis, data mining, clasiffication, Bagging, Naïve Bayes, C.45, non-active students, Indonesia Open University

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Authors: Alemayehu Hailemicael Mezgebe, A. J. Solomon Raju

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Context: Lake Chamo is a significant lake in the Ethiopian Rift Valley, known for its diversity of wildlife and vegetation. However, the lake is facing various threats due to human activities and global effects. The poor management of resources could lead to food insecurity, ecological degradation, and loss of biodiversity. Research Aim: The aim of this study is to analyze the environmental implications of lake level changes using GIS and remote sensing. The research also aims to examine the floristic composition of the lakeside vegetation and propose spatially oriented environmental planning for the sustainable utilization of the biodiversity resources. Methodology: The study utilizes multi-temporal satellite images and aerial photographs to analyze the changes in the lake area over the past 45 years. Geospatial analysis techniques are employed to assess land use and land cover changes and change detection matrix. The composition and role of the lakeside vegetation in the ecological and hydrological functions are also examined. Findings: The analysis reveals that the lake has shrunk by 14.42% over the years, with significant modifications to its upstream segment. The study identifies various threats to the lake-wetland ecosystem, including changes in water chemistry, overfishing, and poor waste management. The study also highlights the impact of human activities on the lake's limnology, with an increase in conductivity, salinity, and alkalinity. Floristic composition analysis of the lake-wetland ecosystem showed definite pattern of the vegetation distribution. The vegetation composition can be generally categorized into three belts namely, the herbaceous belt, the legume belt and the bush-shrub-small trees belt. The vegetation belts collectively act as different-sized sieve screen system and calm down the pace of incoming foreign matter. This stratified vegetation provides vital information to decide the management interventions for the sustainability of lake-wetland ecosystem.Theoretical Importance: The study contributes to the understanding of the environmental changes and threats faced by Lake Chamo. It provides insights into the impact of human activities on the lake-wetland ecosystem and emphasizes the need for sustainable resource management. Data Collection and Analysis Procedures: The study utilizes aerial photographs, satellite imagery, and field observations to collect data. Geospatial analysis techniques are employed to process and analyze the data, including land use/land cover changes and change detection matrices. Floristic composition analysis is conducted to assess the vegetation patterns Question Addressed: The study addresses the question of how lake level changes and human activities impact the environmental health and biodiversity of Lake Chamo. It also explores the potential opportunities and threats related to water utilization and waste management. Conclusion: The study recommends the implementation of spatially oriented environmental planning to ensure the sustainable utilization and maintenance of Lake Chamo's biodiversity resources. It emphasizes the need for proper waste management, improved irrigation facilities, and a buffer zone with specific vegetation patterns to restore and protect the lake outskirt.

Keywords: buffer zone, geo-spatial, lake chamo, lake level changes, sustainable utilization

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Authors: Yajing Jiang, Jing Wu

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Residential area construction links many problems such as population resources, ecology, social values, public services and transportation in the city. After Chinese housing reform, a large number of residential area development accompanied by the loss of agricultural and ecological land. To explore the future of Chinese urban residential area, this article concentrates on how the 'Compact Settlement' behaves in improving the living environment and saving the resources. Through the research of residential area in Hangzhou, there are some determines that increasing the development intensity of the area can indeed bring some improvement in the overall environment. In conclusion, possible design alternatives are discussed for leading Chinese urban development towards a more sustainable path.

Keywords: compact city development, environmental sustainability, residential area, Hangzhou

Procedia PDF Downloads 321

Authors: Abdulmagid A. Khattabi, Ahmed A. Hablous, Mofied M. Elnemry

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The carbon steel is of one of the most common mineral materials used in engineering and industrial applications in order to have access to the required mechanical properties, especially after the change of carbon ratio, but this may lead to stimulate corrosion. It has been used in models of solids with different carbon ratios such as 0.05% C, 0.2% C, 0.35% C, 0.5% C, and 0.65% C and have been studied using three testing durations which are 4 weeks, 6 weeks, and 8 weeks and among different corrosion environments such as atmosphere, fresh water, and salt water. This research is for the purpose of finding the effect of the carbon content on the corrosion resistance of steels in different corrosion medium by using the weight loss technique as a function of the corrosion resistance. The results that have been obtained through this research shows that a correlation can be made between corrosion rates and steel's carbon content, and the corrosion resistance decreases with the increase in carbon content.

Keywords: proportion of carbon in the steel, corrosion rate, erosion, corrosion resistance in carbon-steel

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Authors: Wann-Ming Wey

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In today's popularity and progress of information technology, the big data set and its analysis are no longer a major conundrum. Now, we could not only use the relevant big data to analysis and emulate the possible status of urban development in the near future, but also provide more comprehensive and reasonable policy implementation basis for government units or decision-makers via the analysis and emulation results as mentioned above. In this research, we set Taipei City as the research scope, and use the relevant big data variables (e.g., population, facility utilization and related social policy ratings) and Analytic Network Process (ANP) approach to implement in-depth research and discussion for the possible reduction of land use in primary and secondary schools of Taipei City. In addition to enhance the prosperous urban activities for the urban public facility utilization, the final results of this research could help improve the efficiency of urban land use in the future. Furthermore, the assessment model and research framework established in this research also provide a good reference for schools or other public facilities land use and adaptive reuse strategies in the future.

Keywords: adaptive reuse, analytic network process, big data, land use strategy

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Authors: Anshika Kankane, Dongshik Kang

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Marine debris has a significant influence on coastal environments, damaging biodiversity, and causing loss and damage to marine and ocean sector. A functional cost-effective and automatic approach has been used to look up at this problem. Computer vision combined with a deep learning-based model is being proposed to identify and categorize marine debris of seven kinds on different beach locations of Japan. This research compares state-of-the-art deep learning models with a suggested model architecture that is utilized as a feature extractor for debris categorization. The model is being proposed to detect seven categories of litter using a manually constructed debris dataset, with the help of Mask R-CNN for instance segmentation and a shape matching network called HOGShape, which can then be cleaned on time by clean-up organizations using warning notifications of the system. The manually constructed dataset for this system is created by annotating the images taken by fixed KaKaXi camera using CVAT annotation tool with seven kinds of category labels. A pre-trained HOG feature extractor on LIBSVM is being used along with multiple templates matching on HOG maps of images and HOG maps of templates to improve the predicted masked images obtained via Mask R-CNN training. This system intends to timely alert the cleanup organizations with the warning notifications using live recorded beach debris data. The suggested network results in the improvement of misclassified debris masks of debris objects with different illuminations, shapes, viewpoints and litter with occlusions which have vague visibility.

Keywords: computer vision, debris, deep learning, fixed live camera images, histogram of gradients feature extractor, instance segmentation, manually annotated dataset, multiple template matching

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Authors: Giovana Nunes Inocêncio

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The professional and technological education (EPT) plays a pivotal role in equipping students for specialized careers, and it is imperative to establish a framework for efficient data exchange among educational institutions. The primary focus of this article is to address the pressing need for document interoperability within the context of EPT. The challenges, motivations, and benefits of implementing interoperability standards for digital educational documents are thoroughly explored. These documents include EPT completion certificates, academic records, and curricula. In conjunction with the prior abstract, it is evident that the intersection of IT governance and interoperability standards holds the key to transforming the landscape of technical education in Brazil. IT governance provides the strategic framework for effective data management, aligning with educational objectives, ensuring compliance, and managing risks. By adopting interoperability standards, the technical education sector in Brazil can facilitate data exchange, enhance data security, and promote international recognition of qualifications. The utilization of the XML (Extensible Markup Language) standard further strengthens the foundation for structured data exchange, fostering efficient communication, standardization of curricula, and enhancing educational materials. The IT governance, interoperability standards, and data management critical role in driving the quality, efficiency, and security of technical education. The adoption of these standards fosters transparency, stakeholder coordination, and regulatory compliance, ultimately empowering the technical education sector to meet the dynamic demands of the 21st century.

Keywords: interoperability, education, standards, governance

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Authors: Z. Bouattou, R. Laurini, H. Belbachir

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This paper describes a new approach for the automatic generation of the visual summaries dealing with cartographic visualization methods and sensors real time data modeling. Hence, the concept of chorems seems an interesting candidate to visualize real time geographic database summaries. Chorems have been defined by Roger Brunet (1980) as schematized visual representations of territories. However, the time information is not yet handled in existing chorematic map approaches, issue has been discussed in this paper. Our approach is based on spatial analysis by interpolating the values recorded at the same time, by sensors available, so we have a number of distributed observations on study areas and used spatial interpolation methods to find the concentration fields, from these fields and by using some spatial data mining procedures on the fly, it is possible to extract important patterns as geographic rules. Then, those patterns are visualized as chorems.

Keywords: geovisualization, spatial analytics, real-time, geographic data streams, sensors, chorems

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Authors: Amrashaa Singh

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In the digital age and the large cyberspace, Data Protection and Privacy have become major issues in this technological era. There was a time when social media and online shopping websites were treated as a blessing for the people. But now the tables have turned, and the people have started to look at them with suspicion. They are getting aware of the privacy implications, and they do not feel as safe as they used to initially. When Edward Snowden informed the world about the snooping United States Security Agencies had been doing, that is when the picture became clear for the people. After the Cambridge Analytica case where the data of Facebook users were stored without their consent, the doubts arose in the minds of people about how safe they actually are. In India, the case of spyware Pegasus also raised a lot of concerns. It was used to snoop on a lot of human right activists and lawyers and the company which invented the spyware claims that it only sells it to the government. The paper will be dealing with the privacy concerns in the Indian perspective with an analytical methodology. The Supreme Court here had recently declared a right to privacy a Fundamental Right under Article 21 of the Constitution of India. Further, the Government is also working on the Data Protection Bill. The point to note is that India is still a developing country, and with the bill, the government aims at data localization. But there are doubts in the minds of many people that the Government would actually be snooping on the data of the individuals. It looks more like an attempt to curb dissenters ‘lawfully’. The focus of the paper would be on these issues in India in light of the European Union (EU) General Data Protection Regulation (GDPR). The Indian Data Protection Bill is also said to be loosely based on EU GDPR. But how helpful would these laws actually be is another concern since the economic and social conditions in both countries are very different? The paper aims at discussing these concerns, how good or bad is the intention of the government behind the bill, and how the nations can act together and draft common regulations so that there is some uniformity in the laws and their application.

Keywords: Article 21, data protection, dissent, fundamental right, India, privacy

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Authors: Jiankang Wang, Hongyang Yu

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This paper proposes a portable online 3D modeling method. The method first utilizes a depth camera to collect data and compresses the depth data using a frame-by-frame lossless data compression method. The color image is encoded using the H.264 encoding format. After the cloud obtains the color image and depth image, a 3D modeling method based on bundlefusion is used to complete the 3D modeling. The results of this study indicate that this method has the characteristics of portability, online, and high efficiency and has a wide range of application prospects.

Keywords: 3D reconstruction, bundlefusion, lossless compression, depth image

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Authors: Chang-Ho Lee, Seung-Hoon Lee, Myeong-Jin Park, Oh-Min Kwon

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This paper investigates improved stability criteria for sampled-data control of linear systems with disturbances and time-varying delays. Based on Lyapunov-Krasovskii stability theory, delay-dependent conditions sufficient to ensure H∞ stability for the system are derived in the form of linear matrix inequalities(LMI). The effectiveness of the proposed method will be shown in numerical examples.

Keywords: sampled-data control system, Lyapunov-Krasovskii functional, time delay-dependent, LMI, H∞ control

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Authors: Shohoni Mahabub

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Health Information Systems (HIS) are essential to contemporary healthcare; nonetheless, they frequently encounter challenges such as data fragmentation, inefficiencies, and an absence of real-time analytics. The advent of machine learning (ML) and artificial intelligence (AI) provides a revolutionary potential to address these difficulties via AI-driven data fusion ecosystems. These ecosystems integrate many health data sources, including electronic health records (EHRs), wearable devices, and genetic data, with sophisticated machine learning techniques such as natural language processing (NLP) and predictive analytics to produce actionable insights. Through the integration of strong data intake layers, secure interoperability protocols, and privacy-preserving models, these ecosystems provide individualized treatment, early illness diagnosis, and enhanced operational efficiency. This paradigm change enhances clinical decision-making and rectifies systemic inefficiencies in healthcare delivery. Nonetheless, adoption presents problems such as data privacy concerns, ethical considerations, and scalability constraints. The study examines options such as federated learning for safe, decentralized data sharing, explainable AI for transparency, and cloud-based infrastructure for scalability to address these issues. These ecosystems aim to address health equity disparities, particularly in resource-limited environments, and improve public health surveillance, notably in pandemic response initiatives. This article emphasizes the revolutionary potential of AI-driven data fusion ecosystems in redefining Health Information Systems by providing an implementation roadmap and showcasing successful deployment case studies. The suggested method promotes a cooperative initiative among legislators, healthcare professionals, and technology to establish a cohesive, efficient, and patient-centric healthcare model.

Keywords: AI-powered healthcare systems, data fusion ecosystem, predictive analytics, digital health interoperability

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Authors: Cornelius Femi Popoola

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This study investigated logistics information systems in the distribution of flour in Nigeria. A case study design was used and 50 staff of Honeywell Flour Mill was sampled for the study. Data generated through a questionnaire were analysed using correlation and regression analysis. The findings of the study revealed that logistic information systems such as e-commerce, interactive telephone systems and electronic data interchange positively correlated with the distribution of flour in Honeywell Flour Mill. Finding also deduced that e-commerce, interactive telephone systems and electronic data interchange jointly and positively contribute to the distribution of flour in Honeywell Flour Mill in Nigeria (R = .935; Adj. R2 = .642; F (3,47) = 14.739; p < .05). The study therefore recommended that Honeywell Flour Mill should upgrade their logistic information systems to computer-to-computer communication of business transactions and documents, as well adopt new technology such as, tracking-and-tracing systems (barcode scanning for packages and palettes), tracking vehicles with Global Positioning System (GPS), measuring vehicle performance with ‘black boxes’ (containing logistic data), and Automatic Equipment Identification (AEI) into their systems.

Keywords: e-commerce, electronic data interchange, flour distribution, information system, interactive telephone systems

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Authors: Hidir S. Nogay

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In this study, two systems were created to predict interior temperature in induction motor. One of them consisted of a simple ANN model which has two layers, ten input parameters and one output parameter. The other one consisted of eight ANN models connected each other as cascaded. Cascaded ANN system has 17 inputs. Main reason of cascaded system being used in this study is to accomplish more accurate estimation by increasing inputs in the ANN system. Cascaded ANN system is compared with simple conventional ANN model to prove mentioned advantages. Dataset was obtained from experimental applications. Small part of the dataset was used to obtain more understandable graphs. Number of data is 329. 30% of the data was used for testing and validation. Test data and validation data were determined for each ANN model separately and reliability of each model was tested. As a result of this study, it has been understood that the cascaded ANN system produced more accurate estimates than conventional ANN model.

Keywords: cascaded neural network, internal temperature, inverter, three-phase induction motor

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Authors: James Semmens, James Boyd, Anna Ferrante, Katrina Spilsbury, Sean Randall, Adrian Brown

Abstract:

‘Big data’ is a relatively new concept that describes data so large and complex that it exceeds the storage or computing capacity of most systems to perform timely and accurate analyses. Health services generate large amounts of data from a wide variety of sources such as administrative records, electronic health records, health insurance claims, and even smart phone health applications. Health data is viewed in Australia and internationally as highly sensitive. Strict ethical requirements must be met for the use of health data to support health research. These requirements differ markedly from those imposed on data use from industry or other government sectors and may have the impact of reducing the capacity of health data to be incorporated into the real time demands of the Big Data environment. This ‘big data revolution’ is increasingly supported by national governments, who have invested significant funds into initiatives designed to develop and capitalize on big data and methods for data integration using record linkage. The benefits to health following research using linked administrative data are recognised internationally and by the Australian Government through the National Collaborative Research Infrastructure Strategy Roadmap, which outlined a multi-million dollar investment strategy to develop national record linkage capabilities. This led to the establishment of the Population Health Research Network (PHRN) to coordinate and champion this initiative. The purpose of the PHRN was to establish record linkage units in all Australian states, to support the implementation of secure data delivery and remote access laboratories for researchers, and to develop the Centre for Data Linkage for the linkage of national and cross-jurisdictional data. The Centre for Data Linkage has been established within Curtin University in Western Australia; it provides essential record linkage infrastructure necessary for large-scale, cross-jurisdictional linkage of health related data in Australia and uses a best practice ‘separation principle’ to support data privacy and security. Privacy preserving record linkage technology is also being developed to link records without the use of names to overcome important legal and privacy constraint. This paper will present the findings of the first ‘Proof of Concept’ project selected to demonstrate the effectiveness of increased record linkage capacity in supporting nationally significant health research. This project explored how cross-jurisdictional linkage can inform the nature and extent of cross-border hospital use and hospital-related deaths. The technical challenges associated with national record linkage, and the extent of cross-border population movements, were explored as part of this pioneering research project. Access to person-level data linked across jurisdictions identified geographical hot spots of cross border hospital use and hospital-related deaths in Australia. This has implications for planning of health service delivery and for longitudinal follow-up studies, particularly those involving mobile populations.

Keywords: data integration, data linkage, health planning, health services research

Procedia PDF Downloads 218

Authors: Hemant Kumar

Abstract:

Brahmaputra River is known according to the Hindu mythology the son of the Lord Brahma. According to this name, the river Brahmaputra creates mass destruction during the monsoon season in Assam, India. It is a state situated in North-East part of India. This is one of the essential states out of the seven countries of eastern India, where almost all entire Brahmaputra flow carried out. The other states carry their tributaries. In the present case study, the spatial analysis performed in this specific case the number of MODIS data are acquired. In the method of detecting the change, the spray content was found during heavy rainfall and in the flooded monsoon season. By this method, particularly the analysis over the Brahmaputra outflow determines the flooded season. The charged particle-associated in aerosol content genuinely verifies the heavy water content below the ground surface, which is validated by trend analysis through rainfall spectrum data. This is confirmed by in-situ sampled view data from a different position of Brahmaputra River. Further, a Hyperion Hyperspectral 30 m resolution data were used to scan the sediment deposits, which is also confirmed by in-situ sampled view data from a different position.

Keywords: aerosol, change detection, spatial analysis, trend analysis

Procedia PDF Downloads 149

Authors: Ermias A. Tegegn, Million Meshesha

Abstract:

Human Immunodeficiency Virus and Acquired Immunodeficiency Syndrome (HIV/AIDS) is a major cause of death for most African countries. Ethiopia is one of the seriously affected countries in sub Saharan Africa. Previously in Ethiopia, having HIV/AIDS was almost equivalent to a death sentence. With the introduction of Antiretroviral Therapy (ART), HIV/AIDS has become chronic, but manageable disease. The study focused on a data mining technique to predict future living status of HIV/AIDS patients at the time of drug regimen change when the patients become toxic to the currently taking ART drug combination. The data is taken from University of Gondar Hospital ART program database. Hybrid methodology is followed to explore the application of data mining on ART program dataset. Data cleaning, handling missing values and data transformation were used for preprocessing the data. WEKA 3.7.9 data mining tools, classification algorithms, and expertise are utilized as means to address the research problem. By using four different classification algorithms, (i.e., J48 Classifier, PART rule induction, Naïve Bayes and Neural network) and by adjusting their parameters thirty-two models were built on the pre-processed University of Gondar ART program dataset. The performances of the models were evaluated using the standard metrics of accuracy, precision, recall, and F-measure. The most effective model to predict the status of HIV patients with drug regimen substitution is pruned J48 decision tree with a classification accuracy of 98.01%. This study extracts interesting attributes such as Ever taking Cotrim, Ever taking TbRx, CD4 count, Age, Weight, and Gender so as to predict the status of drug regimen substitution. The outcome of this study can be used as an assistant tool for the clinician to help them make more appropriate drug regimen substitution. Future research directions are forwarded to come up with an applicable system in the area of the study.

Keywords: HIV drug regimen, data mining, hybrid methodology, predictive model

Procedia PDF Downloads 143

Authors: Salif Koné

Abstract:

We analyze hydrometric data at the Koulikoro station on the Niger River; this basin drains 120600 km2 and covers three countries in West Africa, Guinea, Mali, and Ivory Coast. Two subsequent decadal cycles are highlighted (1925-1936 and 1929-1939) instead of the presumed single decadal one from literature. Moreover, the observed hydrometric data shows a multidecadal 40-year period that is confirmed when graphing a spatial coefficient of variation of runoff over decades (starting at 1901-1910). Spatial runoff data are produced on 48 grids (0.5 degree by 0.5 degree) and through semi-distributed versions of both SimulHyd model and GR2M model - variants of a French Hydrologic model – standing for Genie Rural of 2 parameters at monthly time step. Both extremal decades in terms of runoff coefficient of variation are confronted: 1951-1960 has minimal coefficient of variation, and 1981-1990 shows the maximal value of it during the three months of high-water level (August, September, and October). The mapping of the relative variation of these two decadal situations allows hypothesizing as following: the scale of variation between both extremal situations could serve to fix boundary conditions for further simulations using data from climate scenario.

Keywords: internal cycles, hydrometric data, niger river, gr2m and simulhyd framework, runoff coefficient of variation

Procedia PDF Downloads 101

Authors: A. Abdullah, S. Abushalmat, A. Bakshwain, A. Basuhail, A. Aslam

Abstract:

GIS (Geographic Information System) applications require geo-referenced data, this data could be available as databases or in the form of digital or hard-copy agro-meteorological maps. These parameter maps are color-coded with different regions corresponding to different parameter values, converting these maps into a database is not very difficult. However, text and different planimetric elements overlaid on these maps makes an accurate image to database conversion a challenging problem. The reason being, it is almost impossible to exactly replace what was underneath the text or icons; thus, pointing to the need for inpainting. In this paper, we propose a probabilistic inpainting approach that uses the probability of spatial locality of colors in the map for replacing overlaid elements with underlying color. We tested the limits of our proposed technique using non-textual simulated data and compared text removing results with a popular image editing tool using public domain data with promising results.

Keywords: noise, image, GIS, digital map, inpainting

Procedia PDF Downloads 356