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Sankalpa - Interactive Digital Learning Platform to Enrich Students with Special Needs 5

Sankalpa - Interactive Digital Learning Platform to Enrich S...

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Basnayaka, A.I. Epitakaduwa, E.K.G.D. Jayawardena, C.P.U. Zoysa, Ashan Dharmakeerthi, Uditha Jayawardena, Sanath Sri Lanka Institute of Information Technology Dept of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Dept of Computer Science and Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Dept of Computer Science and Network Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

The term special needs mean the special educational needs that the students who suffer from learning disabilities must have due to several reasons. Learning impairment is a condition of the neurological system that impairs one's capacity to efficiently acquire and process knowledge. Failures in diagnosing the people with learning disabilities have been repeatedly highlighted and remain unpopular among people in the developing countries like Sir Lanka. Dyscalculia (difficulties in math), dysgraphia (difficulties in writing) and dyslexia (difficulties in reading) can be considered as some of the major learning disability issues. The 'mobile application based' solution 'Sankalpa Math Master' has been created to offer a game based all-in-one solution including screening, and intervention to identify one of the above-mentioned learning disabilities called 'Dyscalculia'. Dyscalculia is generally distinguished by challenges in learning math, including organizing problems, multi-step calculations, transposing numbers, distinguishing directions, and understanding mathematical signs, leading to confusion with basic operations and facts. The provided solution has been accurate with the use of machine learning and deep learning techniques. In the process of screening with the use of Support Vector Machine (SVM) machine learning algorithm 99.77% of accuracy level has been obtained. The skill level prediction process has acquired an accuracy level of 97% with the use of Random Forest machine learning algorithm. Sri Lankan traditional education system will be vastly benefited by the high level of accuracy in screening, skill predicting, and other game and voice chat-based activities. The students with special needs will gain more effective and personalized learning experience which will improve their learning abilities and help to close the technology gap in education. © 2023 IEEE.

关键词： Forecasting

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Efficiency Measurement of k-Partitioning of Mobile WSN 4

Efficiency Measurement of k-Partitioning of Mobile WSN

引用

4th IEEE International Conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Hossain, Azad Rehena, Zeenat Murshidabad Institute of Technology Lecturer in Computer Science and Technology Berhampore India Aliah University Dept. of Computer Science and Engineering Kolkata India

ISBN: (纸本)9798350306118

Network partitioning improves the energy efficiency of the network. In this work, the algorithm (KP-MWSN) can determine the no. of partitions (k) required based on the delay tolerance limit of each node of MWSN. It can be fruitful for the events where a small amount of delay can be accepted but efficiency has not been calculated in respect of a metric. So, we have measured the efficiency of KP-MWSN algorithm and the results are compared with the Topology Partitioning Scheme (TPS). The simulation result shows that in every randomly created event the efficiency of KP-MWSN is always better than other related state-of-The-Art work. © 2023 IEEE.

关键词： Energy efficiency

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Secure EHR Storage System using Solana Blockchain and IPFS 3

Secure EHR Storage System using Solana Blockchain and IPFS

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3rd International Conference for Innovation in technology, INOCON 2024

作者： Thejaswini, S. Abhishek, S. Managavi, Chetan Maruthi, R. Harikant, Chaya Nagesh Siddaganga Institute of Technology Dept. of Computer Science & Engineering Karnataka Tumakuru India Karnataka Tumakuru India

ISBN: (纸本)9798350381931

Electronic Health Records (EHRs) are crucial for effective patient care and informed decision-making. Proving the authenticity of Electronic Health Records (EHRs) is important for ensuring the quality and reliability of patient care and health insurance services. However, conventional EHR storage systems face issues such as data breaches and privacy violations. To address these challenges, the proposed system presents an innovative solution that combines blockchain technology and the InterPlanetary File System (IPFS). Our solution leverages the decentralized and secure data management features of IPFS to improve the security and accessibility of sensitive health information. Moreover, our solution employs solana blockchain technology to ensure that EHRs are securely stored and accessible only to authorized parties, creating a transparent and tamperproof environment. By incorporating solana blockchain technology, our solution enables health insurance providers to validate the authenticity of EHRs without relying on time-consuming and error-prone methods that are vulnerable to manipulation. Thus, our solution enhances the trustworthiness and accountability of the health insurance system. In summary, the proposed system aims to transform EHR management, providing a secure, decentralized, and efficient ecosystem that protects patient privacy while enhancing the overall integrity and accessibility of Electronic Health Records. © 2024 IEEE.

关键词： Blockchain

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Face Recognition Using openCV- use case on student attendance system 15

Face Recognition Using openCV- use case on student attendanc...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Kamalapur, Anjan Kumar Srikanth, Begari Yadav, Asari Shivadeep Yashwanth, Bachhanaboni Duraivelu, Vivek B V Raju Institute of Technology Dept.of Computer Science Engineering Telangana Narsapur502313 India

ISBN: (纸本)9798350370249

This is a modern approach to student attendance management using face recognition technology, powered by advanced techniques including Convolutional Neural Network (CNN) and Haar Cascade frontalface default algorithm. By leveraging OpenCV and these cutting-edge face recognition methods, the system offers a reliable and efficient solution for tracking attendance in educational institutions. Real-time face detection and recognition, support for multiple face recognition, and smooth interaction with current attendance management systems are some of the key characteristics. Additionally, the system incorporates deep learning algorithms to enhance its accuracy and robustness in identifying students' faces. Through practical implementation, the project demonstrates the effectiveness of using OpenCV and deep learning for automated student attendance management. By providing accurate and efficient attendance tracking, while improving operational efficiency in educational settings, this innovative solution marks a significant advancement in modernizing attendance management practices. © 2024 IEEE.

关键词： Convolutional Neural Network (CNN) Deep Learning Face Detection Haar Cascade Open CV

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Text Filtering Insignificant Word (TFIW) Readability Framework Model to Explain NLP in Sentiment Analysis Using Reinforcement Machine Learning 2

Text Filtering Insignificant Word (TFIW) Readability Framewo...

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2nd International Conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Kandakatla, Rajesh Kumar, V Dhilip Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Dept. Computer Science and Engineering Chennai India BVRIT HYDERABAD College of Engineering for Women Dept. Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350366570

Reading of a text in a given sentence have lot of parameters to consider for expressing an opinion on a given text to conclude nature of the text. The data which need to be formed in the form of data sets based on specific constrains. The Natural Language Processing (NLP) will always presume that Text readability made as the considerable measure for better round spread of any newspaper. The data related to these documents must be collected and store them in data sets. During this process of measuring text readability at the granular level of the text reading is a cumbersome task. This will always implicate a problem of machine readability of the text and explainability of the text taken from the documents mentioned above. In this context human readability will act an important role in predicting the text reading at granular level where the text-based sentiment analysis have been predicted through a machine learning model. The text reading has been evaluated in order to improve the readability through explainable system. The explainable systems will capture and explain the predictions of text reading. In this technical work an explainable system Text Filtering Insignificant Word (TFIW) is introduced where it improves the textual reading at the granule level based on sentiments by prediction the words which need to be filtered. This will improve the explainability of text and text reading and improve the fast text and explainability of the document. © 2024 IEEE.

关键词： Reinforcement learning

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An Evaluation and Comparison for Phishing Attack Detection using Machine Learning Approaches 3

An Evaluation and Comparison for Phishing Attack Detection u...

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3rd International Conference on Power Electronics and IoT Applications in Renewable Energy and its Control, PARC 2024

作者： Sharma, Ajeet Kumar Anushree Rakesh, Nitin Verma, Pawan Kumar Sharda University School of Engineering and Technology Dept. of Computer Science & Engineering Uttar Pradesh Greater Noida India GLA University Dept. of Computer Engineering & Applications Uttar Pradesh Mathura India Nagpur Campus Pune India

ISBN: (纸本)9798350309409

The persistent and evolving threat of phishing attacks demands effective and adaptive detection techniques. This research paper presents a comprehensive evaluation and comparison of various machine learning approaches to detect phishing attacks. We investigated five prominent algorithms: Logistic Regression, Support Vector Machine (SVM), K-Nearest Neighbors (K-NN), Naive Bayes, and Extreme Gradient Boosting (XGBoost), to determine their efficacy in identifying phishing activities. Our methodology involved a systematic analysis using a large dataset of phishing and legitimate URLs, where each model was trained, tested, and validated to ensure robustness and reliability. The performance of each algorithm was assessed based on accuracy, precision, recall, and F1 score. Among the evaluated models, XGBoost demonstrated superior performance, achieving an exceptional accuracy of 99.75%. This result underscores the potential of XGBoost in phishing attack detection, offering a promising tool for cybersecurity applications. © 2024 IEEE.

关键词： Cybersecurity

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Analysis of data from UAVs for surveillance and threat identification in maritime areas 8

Analysis of data from UAVs for surveillance and threat ident...

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8th IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science, BCD 2023

作者： Lampropoulos, Fotis Daramouskas, Ioannis Perikos, Isidoros Paraskevas, Michael University of Peloponnese Electrical and Computer Engineering Dept. Patras Greece Hellenic Coast Guard Patras Greece University of Patras Computer Technology Institute and Press "diophantus" Computer Engineering and Informatics Department Patras Greece University of Peloponnese Computer Technology Institute and Press "diophantus" Electrical and Computer Engineering Dept. Patras Greece

ISBN: (纸本)9798350373615

In this paper, we present a methodology for drones for recognizing different types of objects in maritime areas. The concept and the aim is to assist the national maritime surveillance authorities in the identification of treats and the recognition of the exact type of the treat. The methodology relies on the use of deep learning networks and YOLO framework as well as regression models. The YOLO model detects where each object is and which label should be applied. In this way, object detection is subject to the analysis of Machine Learning-based approaches and Deep Learning-based approaches providing more information about the video or an image than traditional approaches to recognition. These approaches are used to identify groups of pixels that may individually belong to an object. This then feeds a regression model with the help of a convolutional neural network and more specifically, R-CNN, Fast R-CNN, Faster R-CNN and after that object detection algorithm approximates the location of the object and gives its label at the same time. The purpose of the application is to detect objects with an emphasis on identifying one or more targets of interest from data of a video or an image capture. An experimental study was conducted in real-world conditions and revealed quite interesting findings. © 2023 IEEE.

关键词： computer vision

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A Deep Learning Based Image Processing Technique for Early Lung Cancer Prediction

A Deep Learning Based Image Processing Technique for Early L...

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2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems, ICETSIS 2024

作者： Tasnim, Nowshin Noor, Kazi Rifah Islam, Mursalina Huda, Mohammad Nurul Sarker, Iqbal H. Chittagong University of Engineering & Technology Dept. of Computer Science & Engineering Chittagong4349 Bangladesh United International University Dept. of Computer Science & Engineering Dhaka1212 Bangladesh Edith Cowan University PerthWA6027 Australia

ISBN: (纸本)9798350372229

Lung cancer is the primary cause of cancer mor-tality all over the world due to the increase of tobacco consumption, and industrialization in developing nations. As the early-stage diagnosis can reduce the mortality rate significantly, early detection with the availability of high-tech Medical facilities is highly necessary. In this research, we used deep learning (DL) methods initially on patient's 1190 CT scan images from the Kaggle IQ-OTH lung cancer dataset, and after significant image preprocessing steps we found augmented images including normal, malignant, and benign cases to identify high-risk in-dividuals to detect lung cancer and also predict the malignancy and thus, taking early actions to prevent long-term consequences. A thorough performance comparison between several classifiers, including the conventional CNN, Resnet50, and InceptionV3, has been presented. Here, affine transformation, gaussian noise, and other rigorous image preprocessing techniques are used. The contribution obtained a 98% validation accuracy while reducing the model's complexity with the previous preprocessing stage. The comparison method shows that the suggested preprocessing method yields a higher F1 score value of 97%, validating our suggested methodology. © 2024 IEEE.

关键词： CT scans deep learning healthcare analytics image processing lung cancer prediction

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Advanced Analytics for Proactive Cybersecurity Breach Prediction and Prevention 4

Advanced Analytics for Proactive Cybersecurity Breach Predic...

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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Pathmanaban, J. Sangeetha, S. Harini, A. Pavithra, S. Hemasri, P. Sri Sairam Institute of Technology Dept of Computer Science and Engineering Chennai India Veltech Multitech Dr.Rangarajan Dr.Sakunthala Engineering College Dept of Information Technology Chennai India

ISBN: (数字)9798331508845

ISBN: (纸本)9798331508845

As technology advances, so do the potential risks and security threats associated with it. It's crucial to prioritize and invest in cybersecurity measures to safeguard these advancements. Balancing innovation with robust security measures is essential for staying proactive and implementing security protocols by this we can mitigate the risks and ensure a safer digital future. This project focuses on the development of a predictive model for cyber-attack detection on servers. Leveraging advanced analytics and machine learning techniques, the system aims to proactively identify potential security breaches before they manifest. This involves the analysis of server logs, network traffic, and anomaly behaviour to create a robust predictive model. Various types of Cyberattacks which include DDOS, PROBE, U2R, and R2L attacks. In this, we use the RNN algorithm for classification and training in order to improve the efficiency of IDS detection. © 2024 IEEE.

关键词： Cyber attacks

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Ensemble Model Classifier in Hybrid CNN to Predict Diabetic Foot Ulcers 15

Ensemble Model Classifier in Hybrid CNN to Predict Diabetic ...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Athira, Anil Jyothi Varshepally, Anjana Manogna, Akula Sridhar, Alakunta Vivek, D. B V Raju Institute of Technology Dept.of Computer Science Engineering Medak Telangana Narsapur India

ISBN: (纸本)9798350370249

Diabetes arises due to decrease in the level of sufficient insulin produced in the human body. The prevalent issue faced by the individuals suffering from diabetes is Diabetic foot ulcer (DFU). Diabetic Feet affected around 1-4% individuals within 6.1% of diabetic population in 2023 globally. In this developing world, computer vision technologies are playing a vital role even in the field of medicine, particularly it can be beneficial in detecting and monitoring diabetes, along with its difficulties such as foot ulcers. Various models in computer vision are used for detecting the foot ulcers by image processing. Here the proposed novel hybrid model detects and monitors the diabetic foot ulcer in a proficient way. The proposed model is a combination of multiple neural network frameworks including EfficientNet as a Convolutional neural network architecture along with InceptionNet, Stacked Sparse Autoencoder (SSAE) and Sparrow Search Optimization (SSO). Every framework helps in performing different tasks in contribution for an efficient and accurate output. By analyzing diabetic foot images, EfficientNet brings out the information related to visual features and Inception-ResNet-v2 is able to capture the specific features as well as patterns from the images. Stacked Sparse Autoencoder (SSAE) can learn about the features in detail. Sparrow Search Optimization (SSO) is an optimization algorithm which can make the models work together optimally moreover, can eliminate unnecessary parameters which results in less memory requirement and makes the model smaller. The proposed model can improve the detection and management of Diabetic Foot ulcers and is expected to be 2 percentage higher than any existing models. © 2024 IEEE.

关键词： Convolutional Neural Network (CNN) Diabetic Foot Ulcer (DFU) Digital image processing Frameworks Telemedicine application

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