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18th EAI International Conference on Body Area Networks, BODYNETS 2024

作者： Hussain, Aamir ur Rehman, Attique Hussain, Altaf Li, Qimeng Gravina, Raffaele Fortino, Giancarlo Department of Computer Science Muhammad Nawaz Sharif Agriculture University Multan Multan Pakistan Department of Computer Engineering Modeling Electronics and Systems University of Calabria CS Rende87036 Italy

ISBN: (纸本)9783031725234

Type 2 diabetes (T2d), also known as diabetes mellitus or adult-onset diabetes, is a chronic condition that disrupts the body’s insulin regulation, a crucial hormone for controlling blood glucose levels. Glucose, a form of sugar, is the primary energy source for cells in the human bloodstream. Regulating glucose metabolism is vital for the body’s proper functioning. The primary challenges in managing T2d include blood sampling, timely analysis, risk of skin infections, and blood-borne diseases. This paper presents the design of an IoT-based, non-invasive system for monitoring blood glucose levels. Our system utilizes medical sensors to gather data, which is then cleaned and transformed using data mining algorithms. Subsequent analysis compares the accuracy and sensitivity of this data with results from pathology labs and glucometers. The system generates specific warning notifications and provides standard medical advice in response to these alerts, aiding T2d patients in painlessly and effortlessly monitoring and controlling their blood sugar levels. © ICST Institute for computer sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Glucose sensors

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Deduplicating and Ranking Solution Programs for Suggesting Reference Solutions 4

Deduplicating and Ranking Solution Programs for Suggesting R...

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4th Asia Service sciences and Software engineering Conference, ASSE 2023

作者： Shirafuji, Atsushi Watanobe, Yutaka Department of Computer and Information Systems University of Aizu Aizu-Wakamatsu Japan Department of Computer Science and Engineering University of Aizu Aizu-Wakamatsu Japan

ISBN: (纸本)9798400708534

Referring to solution programs written by other users is helpful for learners in programming education. However, current online judge systems just list all solution programs submitted by users for references, and the programs are sorted based on the submission date and time, execution time, or user rating, ignoring to what extent the programs can be helpful to be referenced. In addition, users struggle to refer to a variety of solution approaches since there are too many duplicated and near-duplicated programs. To motivate learners to refer to various solutions to learn better solution approaches, in this paper, we propose an approach to deduplicate and rank common solution programs in each programming problem. Inspired by the nature that the many-duplicated program adopts a more common approach and can be a general reference, we remove the near-duplicated solution programs and rank the unique programs based on the duplicate count. The experiments on the solution programs submitted to a real-world online judge system demonstrate that the number of programs is reduced by 60.20%, whereas the baseline only reduces by 29.59% after the deduplication, meaning that users only need to refer to 39.80% of programs on average. Furthermore, our analysis shows that top-10 ranked programs cover 29.95% of programs on average, indicating that users can grasp 29.95% of solution approaches by referring to only 10 programs. The proposed approach shows the potential of reducing the learners' burden of referring to too many solutions and motivating them to learn a variety of solution approaches. © 2023 ACM.

关键词： Online systems

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Image Caption Generation Related to Object Detection and Colour Recognition Using Transformer-Decoder 4

Image Caption Generation Related to Object Detection and Col...

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4th International Conference on Computing, Mathematics and engineering Technologies, iCoMET 2023

作者： Kamangar, Zainab Umair Shaikh, Ghulam Mutjaba Hassan, Saif Mughal, Nimra Kamangar, Umair Ayaz Sukkur Iba University Craib Computer Science Department Sukkur Pakistan Sukkur Iba University Computer Systems Engineering Department Sukkur Pakistan

ISBN: (纸本)9798350335316

The dependence on digital images is increasing in different fields. i.e, education, business, medicine, or defense, as they are shifting towards the online paradigm. So, there is a dire need for computers and other similar machines to interpret information related to these images and help the users understand the meaning of it. This has been achieved with the help of automatic Image captioning using different prediction models, such as machine learning and deep learning models. However, the problem with the traditional models, especially machine learning models, is that they may not generate a caption that accurately represents that Image. Although deep learning methods are better for generating captions of an image, it is still an open research area that requires a lot of work. Therefore, a model proposed in this research uses transformers with the help of attention layers to encode and decode the image token. Finally, it generates the image caption by identifying the objects along with their colours. The fliker8k and Conceptual Captions datasets are used to train this model, which contains images and captions. The fliker8k contains 8,092 images, each with five captions, and Conceptual Captions contains more than 3 million images, each with one caption. The contribution of this presented work is that it can be utilized by different companies, which require the interpretation of diverse images automatically and the naming of the images to describe some scenario or descriptions related to the images. In the future, the accuracy can be increased by increasing the number of images and captions or incorporating different deep-learning techniques. © 2023 IEEE.

关键词： Object detection

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Effective Visualization and Trend Forecasting of Crime Data Through Big Data Analytics and Mining 3

Effective Visualization and Trend Forecasting of Crime Data ...

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3rd International Conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Mahendhiran, Pd Apsara, S.K. Vasudevan, Harshitha Nithish, S. Sri Krishna College of Engineering and Technology Department of Computer Science and Business Systems Tamil Nadu Coimbatore India Sri Krishna College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798350322842

A crime is an act that merits public condemnation and penalties, which is usually in the form of a fine or jail. It is a widespread social issue that affects a country's life style, economic progress, and credibility. People are at a worrisome point in terms of reducing criminal activity at a greater rate, as criminality rates have risen dramatically. If people can evaluate and predict the crime pattern and implement preventive steps ahead of time, crime rates can be lowered. In this research, the author has present Crime Cast, a system that analyses crime reports and anticipates trends and crime statistics based on a specified data set using deep learning techniques such as LSTM and DNN at a faster rate and with greater accuracy. Within a huge percentage of crime data, Big Data Analytics is utilized to analyze and detect different crime patterns, their relationships, and trends. The information contains cases of kidnapping, murder, rape, stealing, and other crimes. With the help of the LSTM model and DNN several operations performed on the data. The expected outcomes will aid police and law enforcement agencies in clearer grasp crime trends, allowing them to track incidences and improve their decision-making processes. These results will allow the law enforcement and regulatory agencies to further immediately spot inappropriate behavior issues and provide information that will allow them to monitor activities and predict the likelihood of limiting the number of events, transporting property securely, and improving the dynamic cycle. © 2023 IEEE.

关键词： Crime

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Comparative Analysis of Word Embedding and Machine Learning Techniques for Classification of Software Developer Communications on Gitter 18

Comparative Analysis of Word Embedding and Machine Learning ...

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18th Conference on computer science and Intelligence systems, FedCSIS 2023

作者： Akshar, Tumu Kumar, Lov Yogita Murthy, Lalita Bhanu Department of Computer Science & Information Systems BITS Pilani Hyderabad Campus India National Institute of Technology Department of Computer Engineering Kurukshetra India

ISBN: (纸本)9788396744784

In recent times, software developers widely use instant messaging and collaboration platforms, as these plat-forms aid them in exploring new technologies, raising different development-related issues, and seeking solutions from their peers virtually. Gitter is one such platform that has a heavy userbase. It generates a tremendous volume of data, analysis of which is helpful to gain insights about trends in open-source software development and the developers' inclination toward various technologies. Analyzing these trends helps these platforms better cater to the needs of the developers, in turn increasing the usage of these platforms and promoting collaborations between more developers. The classification techniques can be deployed for this purpose. The selection of an apt word embedding for a given dataset of text messages plays a vital role in determining the performance of classification techniques. In the present work, the comparative analysis of nine-word embeddings in combination with seventeen classification techniques with onevsone and onevs-rest has been performed on the GitterCom dataset for categorizing text messages into one of the pre-determined classes based on their purpose. Further, two feature selection methods have been applied. The SMOTE technique has been used for handling data imbalance. It resulted in a total of 1836 classification pipelines for analysis. The objective is to analyze their performances to recommend efficient pipelines for the classification task at hand. The experimental results show that word2vect, GLOVE with 300 vector size, and GLOVE with 100 vector size are three top-performing word embeddings having performance values taken across different classification techniques. The models trained using ANOVA features performed similarly to those models trained using all features. Finally, using the SMOTE technique helps models to get a better prpediction ability. © 2023 Polish Information Processing Society.

关键词： Classification (of information)

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Multi-channel CNN to classify Nepali COVID-19 related tweets using hybrid features

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Journal of Ambient Intelligence and Humanized Computing 2024年第3期15卷 2047-2056页

作者： Sitaula, Chiranjibi Shahi, Tej Bahadur Department of Electrical and Computer Systems Engineering Monash University Wellington Rd ClaytonVIC3800 Australia Earth Observation and AI Research Group Department of Infrastructure Engineering The University of Melbourne ParkvilleVIC3010 Australia School of Engineering and Technology Central Queensland University Rockhampton Australia Central Department of Computer Science and Information Technology Tribhuvan University TU Rd Kathmandu Kirtipur44618 Nepal

Because of the current COVID-19 pandemic’s increasing fears among people, it has triggered several health complications such as depression and anxiety. Such complications have not only affected developed countries but also developing countries such as Nepal. These complications can be understood from peoples’ tweets and comments posted online after their felicitous analysis and sentiment classification. Nevertheless, owing to the limited number of tokens and words in each tweet, it is crucial to capture multiple pieces of information associated with them for better understanding. In this study, we, first, represent each tweet by combining both syntactic and semantic information, called hybrid features. The syntactic information is generated from the bag-of-words method, whereas the semantic information is generated from the combination of the fastText-based (ft) and domain-specific (ds) methods. Second, we design a novel multi-channel CNN (MCNN) model, which ensembles the multiple CNNs, to capture multi-scale information for better classification. Last, we evaluate the efficacy of both the proposed feature extraction method and the MCNN model classifying tweets into three sentiment classes (positive, neutral, and negative) on the NepCOV19Tweets dataset, which is the only public COVID-19 tweets dataset in the Nepali language. The evaluation results show that the proposed hybrid features outperform individual feature extraction methods with the highest classification accuracy of 69.7% and the MCNN model outperforms the existing methods with the highest classification accuracy of 71.3% during classification. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023.

关键词： COVID-19

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Machine learning and deep learning algorithms in detecting COVID-19 utilizing medical images: a comprehensive review

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Iran Journal of computer science 2024年第3期7卷 699-721页

作者： Nurjahan Mahbub-Or-Rashid, Md. Satu, Md. Shahriare Tammim, Sanjana Ruhani Sunny, Farhana Akter Moni, Mohammad Ali Department of IoT and Robotics Engineering Bangabandhu Sheikh Mujibur Rahman Digital University Kaliakair Bangladesh Department of Computer Science and Engineering Bangladesh University of Business and Technology Mirpur Bangladesh Department of Management Information Systems Noakhali Science and Technology University Noakhali Bangladesh Department of Computer Science and Software Engineering Auburn University Auburn AL United States Department of Computer Science and Engineering Green University of Bangladesh Dhaka Bangladesh Faculty of Health and Behavioural Sciences School of Health and Rehabilitation Sciences The University of Queensland St Lucia Australia

The public’s health is seriously at risk from the coronavirus pandemic. Millions of people have already died as a result of this devastating illness, which affects countless people daily worldwide. Unfortunately, no specific therapeutic drugs or vaccines are available to cure patients completely. Therefore, early identification of infected individuals and their isolation can help reduce community transmission of COVID-19. Despite being commonly used, the reverse transcription polymerase chain reaction (RT-PCR) test is costly, time-consuming, and requires suitable kits, which are not always readily available. An alternative solution for the traditional RT-PCR test is machine learning and deep learning-based COVID-19 detection that utilizes clinical features of chest X-rays and computed tomography (CT) images. In this study, we conducted a detailed review of more than 100 recently published works to detect COVID-19-infected patients. Thus, different data preprocessing, data augmentation, image enhancement, feature extraction, machine learning, deep learning, Explainable Artificial Intelligence (AI) methods, etc. were explored to understand how these techniques were used to process images for identifying COVID-19. Additionally, we identified the current challenges in this field and suggested further research directions. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Chest X-ray Computed tomography COVID-19 Deep learning Machine learning

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Deep Learning-Based Estimation of Arterial Stiffness from PPG Spectrograms: A Novel Approach for Non-Invasive Cardiovascular Diagnostics 46

Deep Learning-Based Estimation of Arterial Stiffness from PP...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Abrisham, Kiana Pilevar Alipour, Khalil Tarvirdizadeh, Bahram Ghamari, Mohammad Laboratory Department of Mechatronics Engineering School of Intelligent Systems Engineering College of Interdisciplinary Science and Technology Tehran Iran Kettering University Department of Electrical and Computer Engineering FlintMI United States

ISBN: (纸本)9798350371499

Cardiovascular diseases (CVDs), a leading cause of global mortality, are intricately linked to arterial stiffness, a key factor in cardiovascular health. Non-invasive assessment of arterial stiffness, particularly through Carotid-to-femoral Pulse Wave Velocity (cf-PWV) - the gold standard in this field - is vital for early detection and management of CVDs. This study introduces a novel approach, utilizing photoplethysmogram (PPG) signal spectrograms as inputs for deep learning models to estimate cf-PWV, a significant advancement over traditional methods. Employing a modified ResNet-18 architecture, we analyze PPG signals from digital, radial, and brachial arteries of a simulated dataset of 4374 healthy adults. Our methodology's innovation lies in its direct use of finely tuned spectrogram images, bypassing the complex feature extraction processes. This approach achieved R2 (correlation coefficient) values of up to 0.9902 for the digital artery, 0.9898 for the radial artery, and 0.9825 for the brachial artery, coupled with significantly lower Mean Absolute Percentage Errors (MAPE) of approximately 1.61% for the digital, 1.87% for the radial, and 2.08% for the brachial artery. These findings highlight the efficacy of PPG spectrograms, especially from the digital artery, in providing an accurate, user-friendly, and non-invasive method for cf-PWV estimation, thereby enhancing the capabilities of non-invasive cardiovascular diagnostics. © 2024 IEEE.

关键词： Photoplethysmography

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Convolutional Neural Network-based image tamper detection with Error Level Analysis 2

Convolutional Neural Network-based image tamper detection wi...

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2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Manjunatha, S. Swetha, M.D. Rashmi, S. Subramanian, Ananda Kumar Vinoth Kumar, V. Mallikarjuna Swamy, S. Global Academy of Technology Department of Information Science and Engineering Bengaluru India BNM Institute of Technology Department of Computer Science and Engineering Bengaluru India Bengaluru India Vit - University School of Computer Science Engineering Dept of Computational Intelligence Vellore India Vit - University School of Computer Science Engineering and Information Systems Vellore India Jss Academy of Technical Education Dept of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350306415

Photography is the most important, powerful, and reliable means of expression. Today, digital images not only provide disinformation but also act as agents for secret communication. Users and editing professionals work with digital images for a variety of purposes. Images are often regarded as facts or proof of reality, so they are misleading and fake news or publications of any form that use images manipulated in a highly misleading way. To recognize image tampering needs multiple image data and a model that can handle all the pixels in the image. Furthermore, training the data more efficiently and needed flexibility support everyday use. Models based on Deep learning such as Convolutional Neural Networks with error level analysis (ELA) are the perfect solution. © 2024 IEEE.

关键词： Convolutional Neural Network Deep Learning Efficiency Error Level Analysis Image Forgery Detection

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A Novel Hybrid CNN-LSTM Framework with Ant Colony Optimization for Robust Fake News Detection

A Novel Hybrid CNN-LSTM Framework with Ant Colony Optimizati...

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2025 International Conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Santosh Kumar, B. Viji, M. Gowtham, T. Sekar, G. Sharmila, S. Saravanan, M. New Horizon College of Engineering Department of Computer Science and Engineering Bengaluru560103 India E.G.S. Pillay Engineering Engineering Department of Computer Science and Business Systems Nagapattinam611002 India P.A. College of Engineering and Technology Department of Electronics and Communication Engineering Pollachi642002 India Vsb College Of Engineering Department of Electronics and Communication Engineering Technical Campus Coimbatore642109 India Sir Issac Newton College of Engineering and Technology Department of Computer Science and Engineering Nagapattinam6111002 India Sri Eshwar college of engineering Department of Electronics and Communication Engineering Coimbatore641202 India

ISBN: (纸本)9798331509828

In the digital age, the spread of fake news has become ubiquitous: it weakens public speech and weakens public trust. In this paper we propose a novel method of detecting fake news using a deep learning framework. To do text analysis, we propose a hybrid model that incorporates Convolutional Neural Network and Long Short-Term Memory Network by utilizing on the strengths of each for effective text analysis. However, the LSTM layer is good in learning the context and sequence of the text, and the CNN component beats in extracting local and spatial details in textual data. The effectiveness of the proposed work is tested on a comprehensive data set of news sources (covering different sources, from different countries, and labeled for authenticity). We trained and evaluated the model on this dataset which mainly included features of linguistic and semantic nature to differentiate between genuine and fake news. We show that our results exhibit the biggest accuracy, precision, recall, and Fl-score improvement over old models. Automated fake news detection is a growing field, and this research proposes a robust and scalable solution to cut off the spread of misinformation. © 2025 IEEE.

关键词： Convolutional neural networks

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