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2024 International Conference on information and Communication technology for Development for Africa, ICT4DA 2024

作者： Desta, Abinet Bizuayehu Abetw, Abebe Alemu Werku, Kebad Nigussie Asfaw, Nega Agmas Belachew, Habtamu Molla Alemu, Wubie Belay Debark University Dept. Information Technology Debark Ethiopia University of Gondar Dept. Computer Science Gondar Ethiopia Woldia University Dept. Computer Science Woldia Ethiopia

ISBN: (纸本)9798331527792

This research focuses on the challenge of Sub-event Relation Classification (SERC) in Amharic text, with the objective of identifying relationships between event triggers or mentions in sentences. The aim is to predict sub-event relationships in Amharic sentences, benefiting applications such as information extraction, question answering, and document summarization. Amharic text requires a lot of effort to manually identify event relationships, and there isn't yet a model that specializes in extracting event relationships from Amharic language. Deep learning techniques, particularly LSTM and BiLSTM, are used in this work to categorize the relation type between event pairs in unstructured Amharic text in order to address this difficulty. The models are trained and assessed using a dataset of 6622 manually annotated Amharic texts. Experimental results showcase the effectiveness of the BiLSTM model, achieving a high accuracy of 93% for Amharic sub-event relation classification. The findings of this research hold significant implications for natural language processing applications in Amharic and other languages. Moreover, the developed model can be extended to other languages, making it a valuable contribution to the field of natural language processing. © 2024 IEEE.

关键词： Amharic Sub-event Relation Classification (A-SERC) Bidirectional Long Short-Term Memory (BiLSTM) Deep Learning (DL) Long Short-Term Memory (LSTM) Natural Language Processing (NLP) Word2Vec

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Improvising Early Detection of Diabetes through Deep Learning on Retinal Fundus images 3

Improvising Early Detection of Diabetes through Deep Learnin...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Balaji, Krishna Ganesh, Maniprabha Shankar Murali, Daneesh Pachiappan, Kalaivani Iyyanar, P. Jayakumar, Nithya Kongu Engineering College Dept. of Computer Science Erode India Sona College of Technology Dept. of Information Technology Salem India K.S Rangasamy College of Technology Dept. of Information Technology Tiruchengode India

ISBN: (纸本)9798331509675

Diabetes is a chronic disease whose timely and accurate diagnosis will prevent serious complications from health. This paper explores using iridology principles in a deep learning method to detect diabetes from retinal images in order to streamline the process and automate the procedure. The objective here is to create a non-invasive procedure for early and reliable detection of the disease. Deep learning in medical imaging is the new way to interfere with conventional modes of diagnostics. The retinal imaging is non-invasive and has, over the years, been put to use in medical examinations, and its potential is now absolutely being utilized in the early diagnosis of diabetes. A retinal analysis that is based on principles, integrating them into iridology, concentrating on the patterns and signs portrayed in the iris for possible reflections of a general health condition, is aimed at achieving earlier indications of diabetes in a more effortless and rapid way. Models such as ResNet50, ResNet101, and EfficientNetB7 were precious but actually, it was InceptionV3 that delivered a great accuracy of 97.14%. This result proves the feasibility application of InceptionV3 for diagnosing diabetes in the early stages, thus eventually providing an easy and non-invasive way of moving forward to better health. © 2025 IEEE.

关键词： Ophthalmology

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Negation Handling and Domain Generalization in Sentiment Analysis using Machine Learning Models

Negation Handling and Domain Generalization in Sentiment Ana...

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2024 International Conference on Knowledge Engineering and Communication Systems, ICKECS 2024

作者： Biradar, Shrnivas Raju, G.T. Divakar, K.M. SJC Institute of Technology Dept. of Computer Science and Engineering Chikkaballapur India SJC Institute of Technology Dept. of Information Science and Engineering Chikkaballapur India

ISBN: (纸本)9798350359688

Conventional lexicon-based approaches to sentiment analysis typically lack the necessary methods to properly identify the negation window, making it impossible to model negation. An enormous increase in sentiment-rich electronic and social media has been observed daily. Negation modifiers cause problems for Sentiment Classification techniques and have the power to entirely change the discourse's meaning. Therefore, it becomes essential to manage them well. Opinion mining or sentiment analysis is the study of people's attitudes, feelings, and views as they are expressed in written language. It is one of the busiest text mining and natural language processing research projects. Even though sentiment analysis research has gained popularity in the field of natural language processing, for this problem, the state-of-the-art machine learning approach is based on Bag of Words. But the BOW model pays little attention to polarity shift, which could have a distinct overall effect. One of the main issues with doing sentimental analysis on any given text or sentence is handling polarity shift, which is what this study attempts to address. Sentiment analysis use Natural Language Processing principles to identify negation in the text. Our goal is to identify the negation effect on customer reviews that, although appearing good, are actually negative. The suggested modified negation methodology helps to increase classification accuracy by providing a method for computing negation identification. In terms of review classification by accuracy, precision, and recall, this approach yielded a noteworthy outcome. When test and training data are from distinct domains, machine learning faces the challenge of domain generalization. Despite the large body of research on cross-domain text classification, the majority of current methods concentrate on one-to-one or many-to-one domain adaptation. Our domain generalization method regularly outperforms state-of-the-art domain adaption methods, a

关键词： Natural language processing systems

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A Review of Food Supply Chain Tracking Using Blockchain technology: Enabled Factors and Barriers 1

A Review of Food Supply Chain Tracking Using Blockchain Tech...

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1st International Conference on Emerging Technologies for Dependable Internet of Things, ICETI 2024

作者： Hashem, Buthaina Algaphary, Ghaleb Almutawkil, Adnan Dept. Information Technology Faculty of Computer and IT University of Sana’a Sana’a Yemen Dept. Computer Science Faculty of Computer and IT University of Sana’a Sana’a Yemen

ISBN: (纸本)9798331533557

The food supply chain (FSC) deals with issues like reliability, security, and traceability due to information contradiction. While blockchain technology (BCT) shows promise in improving FSC operations, its adoption in this sector remains early. This study systematically reviews BCT's applications in FSC tracking, focusing on its layers, types, platforms, and features. Through a methodical literature review spanning 2019 to 2023, 50 relevant articles were examined to identify barriers to BCT adoption, such as scalability and costs, and enabling factors like data integrity and decentralization. The review aims to identify gaps in the present research, setting the basis for offering a structured classification for blockchain-based FSC tracking and developing a conceptual framework for future BCT adoption in FSC. This framework aims to present strategic guidance for applying blockchain technology in FSC tracking applications, enhancing operational efficiency and transparency in the food supply chain sector. The finding of this study could help the government sector focus on building mechanisms to encourage the agriculture sector to invest in supply chain tracking. © 2024 IEEE.

关键词： Barriers Blockchain technology Enabling Factors Food Supply Chains Tracking

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MELANOMA AUTOMATED DETECTION SYSTEM INTEGRATED WITH AN EHR PLATFORM

UPB Scientific Bulletin, Series C: Electrical Engineering an...

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UPB Scientific Bulletin, Series C: Electrical Engineering and computer science 2024年第1期86卷 57-68页

作者： El-Khatib, Hassan Ștefan, Ana-Maria Popescu, Dan Dept. of Automatic Control and Industrial Informatics National University of Science and Technology POLITEHNICA Bucharest Romania Dept. of Electronics Telecommunications and Information Technology National University of Science and Technology POLITEHNICA Bucharest Romania

Early melanoma detection is vital for improved treatment outcomes and reduced mortality. This paper proposes integrating an automated melanoma detection system into the Electronic Health Record, offering benefits like seamless screening during routine medical visits, enhancing early lesion detection. The system's efficiency allows providers to focus on patient care, while real-time alerts ensure timely follow-up. Patient engagement promotes proactive skin health. Interoperability within the EHR facilitates comprehensive care, resulting in better outcomes, reduced costs, and proactive prevention. © 2024, Politechnica University of Bucharest. All rights reserved.

关键词： Automation

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Device Identification using Network Traces in a Smart Home IoT Network 17

Device Identification using Network Traces in a Smart Home I...

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17th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2023

作者： Brahma, Jwngfu Matam, Rakesh Choudhury, Nikumani Indian Institute of Information Technology Dept. of Computer Science & Engineering Guwahati India Birla Institute of Technology & Science Dept. of Computer Science & Information Systems Hyderabad India

ISBN: (纸本)9798350307672

Device identification is a crucial aspect of securing networks, particularly in the context of the Internet of Things (IoT), where a vast variety of devices are interconnected. Recently, there has been significant research on developing techniques for identifying IoT devices based on their unique characteristics, called as device fingerprints. These techniques use machine learning algorithms, which can effectively learn and classify devices based on their features. However, device identification remains a challenging task due to the diversity of IoT devices and the constant evolution of their characteristics. This paper presents our attempt at device identification by analyzing distinct device characteristics observed during network communication and using different machine learning techniques. © 2023 IEEE.

关键词： Internet of things

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Detection and segmentation of oral lesion using Mask R-CNN

Detection and segmentation of oral lesion using Mask R-CNN

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2024 Asian Conference on Intelligent Technologies, ACOIT 2024

作者： Kavyashree, C. Vimala, H.S. Shreyas, J. Dept of Computer Science UVCE Bangalore India Dept. of Information Technology MAHE Bangalore India

ISBN: (纸本)9798350374933

Early detection of oral cancer is essential for enhancing patient outcomes and preserving lives. Nevertheless, inaccurate and inappropriate diagnosis may impede the effectiveness of treatment. In recent years, deep learning techniques have assumed greater significance with the advancement of artificial intelligence, establishing themselves as a fundamental technology in the field of medical diagnosis. It automates image classification process and enhances accuracy. The study proposes Mask R-CNN (Region-based Convolutional Neural Network) for identifying the cancerous part or lesion in an image. The Photographic images are annotated and trained using mask R-CNN. With backbone as ResNet-50 extract features and used to obtain the bounding box along with the classification and segmentation mask. ROI (Region of Interest) alignment aids in enhancing the localization of objects by fine-tuning the feature maps and ROI, thereby delivering highly accurate segmentation masks. ROI align also ensures the prevention of data loss. The model provides the mean average precision (mAP) of 75.34%, precicsion of 76.45% and a recall of 73.25%. This technique enables the automated identification of cancerous regions within an image, assisting medical professionals in early detection. © 2024 IEEE.

关键词： Convolutional neural networks

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A Comparative Analysis of Machine Learning Classification Algorithms for Cardiovascular Disease Prediction 9

A Comparative Analysis of Machine Learning Classification Al...

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9th IEEE International Conference on Adaptive science and technology, ICAST 2024

作者： Attipoe, Elliot Kojo Yussiff, Alimatu Saadia Asante-Mensah, Maame Gyamfua University of Cape Coast Dept. of Computer Science and Information Technology Cape Coast Ghana

ISBN: (纸本)9798350385403

Cardiovascular disease is a life-threatening condition accounting for over 17 million deaths globally every year. Several studies have revealed the advancement of machine learning algorithms in predictive modeling and risk assessment, leading to improved chronic disease management and preventive measures. The algorithms analyze complex medical datasets to uncover hidden patterns, trends, and correlations that might be difficult for the medical practitioner, thereby improving the efficiency and accuracy of the medical decision-making process. This study utilizes a cardiovascular disease dataset comprising 70,000 instances to compare the performance of ten classification algorithms in predicting cardiovascular diseases. A new machine learning model is proposed, which performs better than all existing models using the same dataset. The results show the XGBoost classifier as the best model for predicting cardiovascular diseases, with an accuracy of 73.3% and an AUC score of 80%. © 2024 IEEE.

关键词： Contrastive Learning

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Leveraging XAI and Breakthrough Machine Learning Techniques for Trigeminal Neuralgia Severity Classification

Leveraging XAI and Breakthrough Machine Learning Techniques ...

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2024 IEEE Region 10 Symposium, TENSYMP 2024

作者： Abhijna, M.B. Akul, P. Kodipalli, Ashwini Global Academy of Technology Dept. of Information Science and Engineering India Global Academy of Technology Dept. of Computer Science and Engineering India Global Academy of Technology Dept. of Artificial Intelligence and Data Science India

ISBN: (纸本)9798350364866

Trigeminal Neuralgia (TN) is a debilitating chronic pain disorder that significantly diminishes overall well-being, making diagnosis and therapy more challenging. The quick and precise categorization of TN severity is critical to therapy effectiveness. To assess the severity of TN, this study makes use of advanced machine learning techniques and explainable AI (XAI) approaches. We use a range of bagging and boosting strategies, including AdaBoost, Random Forest, Decision Tree, K-Nearest Neighbours (KNN), Gradient Boosting, and Logistic Regression. Random Forest was 95.2% accurate, whereas AdaBoost and Gradient Boosting were 92% and 97.2% in terms of their accuracy. The Decision Tree classifier obtained an accuracy of 84.62%, while K-Nearest Neighbours and Logistic Regression also achieved 92.31% accuracy. F1- score, precision, and recall metrics were used to assess performance on a dataset that included patient demographics and medical histories. XAI techniques, such as LIME and SHAP values, were used to improve the models' readability and transparency, which helped to progress the creation of cutting-edge diagnostic tools and increase clinician and researcher confidence. © 2024 IEEE.

关键词： Logistic regression

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Deep Learning Approach with a Convolutional Neural Network for Landslide Detection Using Remotely Sensed Images

Deep Learning Approach with a Convolutional Neural Network f...

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2024 International Conference on science, Engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Pucoe, Gloria Obagbuwa, Ibidun Christiana Sol Plaatje University Dept. Computer Science and Information Technology Kimberley South Africa

ISBN: (纸本)9798350358155

A landslide is a natural hazard that has become prevalent, especially in these times of climate change. Detecting a natural hazard is critical as it can prevent costs and fatalities. In this study, we investigate the efficiency of algorithms in detecting landslides. We implement this investigation using convolutional neural networks to classify extracted features from satellite images to detect landslides. This study investigates the use of convolutional neural networks to classify, segment, detect and landslides in an area. Two experiments were conducted;the first was to evaluate the performance of the deep learning model when the learning rate changes along with the number of epochs, and the second was to evaluate the accuracy of the deep learning model. For experiment 1, performance was better due to a higher epoch number per learning rate by monitoring the loss, and it was then used for experiment 2. Adam optimizer was used to build a deep learning CNN model, and after 20 epochs of training, the model gave an F1-score area of 65.09%, recall area of 81.13%, and precision of 57.44%. The model can detect potential landslide areas from LiDAR image input data. © 2024 IEEE.

关键词： Convolutional neural networks

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