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Communication, Computing and Energy Efficient Technologies (I3CEET), International Conference on

作者： Mohammed Al-Farouni Barathi K Anjani Kumar Rai I.B. Sapaev Jatinder Kumar A. Krishna Kumar Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department of Electrical and Electronics Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Tamil Nadu India Department of Computer Engineering and Applications GLA University Mathura India the Department «Physics and Chemistry» Tashkent Institute of Irrigation and Agricultural Mechanization Engineers" National Research University Tashkent Uzbekistan Western Caspian University Scientific Researcher Baku Azerbaijan Department of Computer Science Engineering Chandigarh Engineering College Chandigarh Group of Colleges Mohali Punjab India Department of Civil Engineering Karpagam Academy of Higher Education Coimbatore India

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

Quantitative and volumetric investigations utilizing magnetic resonance imaging are an essential technique used by researchers to examine the brain throughout normal maturation and different neurological illnesses, including aging. Despite the processing time and need for human adjustment, volumetric analysis is still commonly employed in clinical practice despite the unavailability of open-source brain segmentation tools. Here, we provide a comprehensive segmentation of deep brain and cortical gray matter structures that match typical aseg+aparc areas in the popular open-source software Freesurfer. We are extending the usage of machine learning models beyond proof-of-con, as was previously announced. This study aids clinical translation and quantitative brain segmentation research by offering a quick and easy-to-use deep learning-based brain segmentation tool. Improved segmentation quality and a shorter processing time are two benefits of the offered technologies. This is the first research to apply a surface-based approach, which is widely used by specialists in the area, to swiftly and precisely segment 102 brain regions. Additionally, this is the first time that knowledgeable readers have tried to evaluate the level of segmentation quality attained using deep learning-based models. We show that our deep learning based model outperforms Freesurfer, a conventional segmentation tool. DARTS is the name of the suggested deep learning-based technology.

关键词： Deep learning Neuroimaging Image segmentation Translation Computational modeling Magnetic resonance imaging Brain modeling Telecommunication computing Surface treatment Open source software

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Surveillance and Intruder Detection using Pyramid Vision Transformer You Only Look Once Enhanced with Gradient Weighted Class Activation Mapping

Surveillance and Intruder Detection using Pyramid Vision Tra...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： Sripriya Arunachalam Muntather Almusawi Shaik. Rafikiran S. Sivagami Hirald Dwaraka Praveena Department of Computer Science and Applications Faculty of Science and Humanities SRM Institute of Science and Technology Vadapalani City Campus Chennai India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department of Electrical & Electronics Engineering Sri Venkateswara College of Engineering Tirupati India Department of Chemistry New Prince Shri Bhavani College of Engineering and Technology Chennai India Department of Electronics and Communication Engineering School of Engineering Mohan Babu University (Erstwhile Sree Vidya nikethan Engineering College) Tirupati India

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

In this era, wireless networks are economically feasible and flexible to establish in healthcare, defense, surveillance, and traffic monitoring, fire detection. Effective intruder detection systems utilize advanced technologies like Artificial Intelligence (AI), sensors, and cameras. Traditional approaches for surveillance and intruder detection had faced several challenges which include false alarms, high costs, privacy concerns, and vulnerabilities. To overcome these challenges, a Gradient-weighted Class Activation Mapping (Grad CAM) with Pyramid Vision Transformer-You Only Look Once Enhanced (PViT-YOLOE) namely GRADCAM-PViT-YOLOE is proposed to detect intruders in public places using images and videos. For preprocessing, noise reduction, image resizing, and image segmentation are considered to reduce false positives, enhance memory efficiency, and to reduce complexity. Then, PViT-YOLOE is considered for feature extraction to enhance detection of small objects, enhance robustness to variations. At last, Grad CAM is employed as classifier to detect intruders in public places with improved interpretability, robustness to noise, and flexibility. The results demonstrate the efficacy of the proposed GRADCAM-PViT-YOLOE in terms of accuracy (99.25 % ), precision (96.75 % ) recall (89.82 % ) and F1-Score (87.92 % ) when compared to the existing method like Faster Regions Convolutional Neural Networks (FR-CNN).

关键词： Image segmentation Accuracy Noise reduction Noise Memory management Transformers Feature extraction Robustness Sensor systems Videos

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Enhanced Skin Lesion Diagnosis using a Deep Learning Techniques

Enhanced Skin Lesion Diagnosis using a Deep Learning Techniq...

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International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： M Sornalakshmi G Elizabeth Rani M Sakthimohan M. Thenmozhi D Amuthaguka V Sanjeevi Kumar Department of PG (CS) Arulmigu Kalasalingam College Of Arts And Science Krishnankoil India Department of AI&DS KIT-KalaignarKarunanidhi Institute of Technology Coimbatore Tamilnadu India Department of ECE KalaignarKarunanidhi Institute of Technology Coimbatore Tamilnadu India Department of AI&DS Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Department of Computer Applications Kalasalingam Academy of Research and Education Tamil Nadu India Department of CSBS KGiSL Institute of Technology Coimbatore Tamilnadu India

ISBN: (数字)9798350367904

ISBN: (纸本)9798350367911

Skin diseases are a serious global public health concern that need to be promptly treated using accurate and efficient diagnostic methods. This study addresses the issue of automated skin disease diagnosis by utilising Convolutional Neural Networks (CNNs), a recent advancement in machine learning. The proposed CNNbased method aims to enhance diagnostic abilities by identifying various skin conditions through the analysis of dermatological pictures. A customised CNN architecture is created for effective feature extraction, and a range of annotated skin image datasets are collected and preprocessed for consistency as part of the technique. The model undergoes rigorous training, validation, and testing processes to ensure high performance. Incorporating the trained model into healthcare systems is one potential technique to assist dermatologists in making early diagnoses of skin issues. This research advances the developing field.

关键词： Training Accuracy Skin Convolutional neural networks Medical diagnosis Lesions Public healthcare Medical diagnostic imaging Diseases Testing

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A Brief Review of Explainable Artificial Intelligence in Healthcare

arXiv

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arXiv 2023年

作者： Sadeghi, Zahra Alizadehsani, Roohallah Cifci, Mehmet Akif Kausar, Samina Rehman, Rizwan Mahanta, Priyakshi Bora, Pranjal Kumar Almasri, Ammar Alkhawaldeh, Rami S. Hussain, Sadiq Alatas, Bilal Shoeibi, Afshin Moosaei, Hossein Hladík, Milan Nahavandi, Saeid Pardalos, Panos M. Institute for Big Data Analytics Faculty of Computer Science Dalhousie University Canada Deakin University Geelong Australia The Institute of Computer Technology Tu Wien University Vienna1040 Austria University of Kotli Azad Jammu and Kashmir Azad Kashmir Kotli Pakistan Centre for Computer Science and Applications Dibrugarh University Assam India Department of Management Information Sys Al-Balqa Applied University Salt19117 Jordan Department of Computer Information Systems The University of Jordan Aqaba77110 Jordan Examination Branch Dibrugarh University Assam Dibrugarh India Department of Software Eng. Firat University Elazig23100 Turkey Data Science and Computational Intelligence Institute University of Granada Spain Department of Informatics Faculty of Science Jan Evangelista Purkyně University in Ústí nad Labem Czech Republic Department of Applied Mathematics School of Computer Science Faculty of Mathematics and Physics Charles University Prague Czech Republic Harvard Paulson School of Engineering and Applied Sciences Harvard University AllstonMA02134 United States Center for Applied Optimization Department of Industrial and Systems Engineering University of Florida Gainesville32611 United States

XAI refers to the techniques and methods for building AI applications which assist end users to interpret output and predictions of AI models. Black box AI applications in high-stakes decision-making situations, such as medical domain have increased the demand for transparency and explainability since wrong predictions may have severe consequences. Model explainability and interpretability are vital successful deployment of AI models in healthcare practices. AI applications' underlying reasoning needs to be transparent to clinicians in order to gain their trust. This paper presents a systematic review of XAI aspects and challenges in the healthcare domain. The primary goals of this study are to review various XAI methods, their challenges, and related machine learning models in healthcare. The methods are discussed under six categories: Features-oriented methods, global methods, concept models, surrogate models, local pixel-based methods, and human-centric methods. Most importantly, the paper explores XAI role in healthcare problems to clarify its necessity in safety-critical applications. The paper intends to establish a comprehensive understanding of XAI-related applications in the healthcare field by reviewing the related experimental results. To facilitate future research for filling research gaps, the importance of XAI models from different viewpoints and their limitations are investigated. © 2023, CC BY-NC-ND.

关键词： Decision making

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International Conference on Smart Computing & Communications (ICSCC)

作者： Rushikesh Shaharao Gopal Sakarkar Abhijeet Khandagale Mahima Gaurihar Ramsha Shaikh Rupak Kadhare Agus Putu Abiyasa Department of Artificial Intelligence G. H. Raisoni College of Engineering Nagpur India Department of Computer Science & Applications Dr Vishwanath Karad MIT World Peace University Pune India Vruksh Ecosystem Foundation India Department of DIC(Data Science CSE(IoT) CSE(Cyber Security) G. H. Raisoni College of Engineering Nagpur India Ekatra Learning Inc. Mumbai India Department of Electrical Engineering Universitas Pendidikan Nasional Denpasar Indonesia

ISBN: (数字)9798350363104

ISBN: (纸本)9798350363111

Semantic Similarity, also known as Semantic Textual Similarity, is an NLP task that evaluates the relationship between texts using predefined metrics. It is crucial in domains like information retrieval and sentiment analysis. This research proposes a methodology to enhance learner performance by leveraging semantic similarity for multi-source information retrieval. The methodology involves four stages: text extraction from PDFs and web scraping from web pages, preprocessing, embedding, and semantic similarity computation. Various embedding techniques, such as word2vec, GloVe, Hugging Face embeddings, and BERT, are employed to vectorize text data. We compute cosine similarity between the query and sentences to find the top N matches. Our approach aims to retrieve relevant information efficiently, supporting learners in accessing information pertinent to their queries. The results demonstrate that our methodology outperforms traditional information retrieval methods in precision and speed. This approach can significantly benefit NLP and information retrieval, especially in improving learner performance.

关键词： Measurement Sentiment analysis Electronic learning Accuracy Semantics Education Web pages Information retrieval Faces

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Exploring diabetes through the lens of AI and computer vision: Methods and future prospects

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computers in Biology and Medicine 2025年 185卷 109537-109537页

作者： Chundi, Ramesh G., Sasikala Basivi, Praveen Kumar Tippana, Anitha Hulipalled, Vishwanath R. N., Prabakaran Simha, Jay B. Kim, Chang Woo Kakani, Vijay Pasupuleti, Visweswara Rao School of Computer Applications Dayananda Sagar University Bangalore India School of Computer Science and Applications REVA University Rukmini Knowledge Park Bangalore560064 India Pukyong National University Industry-University Cooperation Foundation Pukyong National University Busan48513 Korea Republic of Department of Biotechnology School of Applied Sciences REVA University Rukmini Knowledge Park Bangalore560064 India School of Computing and Information Technology REVA University Rukmini Knowledge Park Bangalore560064 India School of Computer Science and Engineering Vellore Institute of Technology Tamilnadu Vellore632014 India Abiba Systems CTO and RACE Labs REVA University Rukmini Knowledge Park Bangalore560064 India Department of Nanotechnology Engineering College of Engineering Pukyong National University Busan48513 Korea Republic of Integrated System Engineering Inha University 100 Inha-ro Nam-gu Incheon22212 Korea Republic of School of Biosciences Taylor's University Lakeside Campus Selangor Subang Jaya47500 Malaysia Faculty of Earth Sciences Universiti Malaysia Kelantan Campus Jeli Jeli Kelantan17600 Malaysia

Early diagnosis and timely initiation of treatment plans for diabetes are crucial for ensuring individuals’ well-being. Emerging technologies like artificial intelligence (AI) and computer vision are highly regarded for their ability to enhance the accessibility of large datasets for dynamic training and deliver efficient real-time intelligent technologies and predictable models. The application of AI and computer vision techniques to enhance the analysis of clinical data is referred to as eHealth solutions that employ advanced approaches to aid medical applications. This study examines several advancements and applications of machine learning, deep learning, and machine vision in global perception, with a focus on sustainability. This article discusses the significance of utilizing artificial intelligence and computer vision to detect diabetes, as it has the potential to significantly mitigate harm to human life. This paper provides several comments addressing challenges and recommendations for the use of this technology in the field of diabetes. This study explores the potential of employing Industry 4.0 technologies, including machine learning, deep learning, and computer vision robotics, as effective tools for effectively dealing with diabetes related aspects. © 2024 Elsevier Ltd

关键词： Artificial intelligence computer vision Diabetes Deep learning Machine learning

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Preface

Studies in Classification, Data Analysis, and Knowledge Orga...

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Studies in Classification, Data Analysis, and Knowledge Organization 2023年 v-vi页

作者： Grilli, Leonardo Lupparelli, Monia Rampichini, Carla Rocco, Emilia Vichi, Maurizio Department of Statistics Computer Science Applications "G. Parenti" University of Florence Florence Italy

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Redefining quality in engineering education through the flipped classroom model 9

Redefining quality in engineering education through the flip...

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9th World Engineering Education Forum, WEEF 2019

作者： Bhat, Sathyendra Raju, Ragesh Bhat, Shreeranga D'Souza, Rio Department of Computer Applications St Joseph Engineering College Mangaluru575028 India Department of Mechanical Engineering St Joseph Engineering College Mangaluru575028 India Department of Computer Science and Engineering St Joseph Engineering College Mangaluru575028 India

The students in engineering institutes are expected to be tech-savvy and have an appetite for technology-based learning. Sadly, most of the courses in the current Indian engineering education scenario are taught using straight lectures where students are made to play a passive role while the teacher takes the center stage. The need of the hour is to get the students out of this passive mode of learning and bring them to the forefront in the teaching-learning cycle. Most often than not, teachers utilize the class time teaching technical intricacies that are crucial to solving problems which leave very little time to do anything else. Hence, the onus falls on the students to apply the learning in the class to solve problems on their own outside the classroom which is far from ideal. The flipped class is a model wherein much of the technical content is delivered outside the classroom via a virtual platform which in turn makes the classroom a congenial place for discussions, problem-solving and promoting active learning in the presence of the teacher. Through this paper, a study has been conducted to bring in this modern technology into engineering education. The objective is to analyze the power of technology in teaching courses that require significant work beyond class hours. The flipped classroom model was applied on 32 final year Post Graduate students through the Android Mobile Application Development course. A fundamental topic was chosen that forms the base to build sophisticated Android Apps. Since it was very difficult to grasp the topic at one go, the class was flipped so that the students get ample time to go through the learning resources and come well prepared for the class. To begin with, the students were given the task of going through a lecture video from one of the NPTEL courses on Modern Application Development. However, the video shared was 22 minutes long and to simplify the learning experiences of students, a 3-minute Introductory video on the cou

关键词： Students

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Implementing and Analyzing Behaviour of ML based Tamil Songs Recommendation System

Implementing and Analyzing Behaviour of ML based Tamil Songs...

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Sustainable Computing and Data Communication Systems (ICSCDS), International Conference on

作者： Shalini Garg Pooja Shailee Lohmor Chaoudhary Rinku Dixit Nikhil Govil MIT Art Design and Technology University Pune MH India School of CSE Galgotias University Gr. Noida Delhi - NCR India Department of AIML – Data Science New Delhi Institute of Management New Delhi India Department of Computer Engineering & Applications GLA University Mathura U.P. India

Music gives listeners a chance to relax, relieve fatigue and connect one culture with another. In such a situation, if the favorite song-music is heard one after another and according to the emotion, it gives a unique and wonderful experience to the listener. It is determined based on the emotion and interest, ratings provided by listeners and search patterns. In this research article, the proposed method presents Tamil songs compilation and recommendation system with the help of these factors. The behavior of this recommendation system is also analyzed in this system. The proposed system is developed in Java programming language by adopting the techniques of Machine Learning. Through this system, listeners have the choice to play category wise songs. As per the listening history and patterns, upcoming songs are predicted to the listeners. This system also provides ratings to each song. This developed application can also be used commercially as it has been validated that the ratings provided by the listeners are in line with IMDb ratings.

关键词： Java computer languages Music Machine learning Fatigue Behavioral sciences History

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A Multi-Conditional Proxy Broadcast Re-Encryption Scheme for Sensor Networks

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computers, Materials & Continua 2020年第12期65卷 2079-2090页

作者： Pang Li Lifeng Zhu Brij B.Gupta Sunil Kumar Jha Nanjing Vocational College of Information Technology Nanjing210044China State Key Laboratory of Bioelectronics Jiangsu Key Laboratory of Remote Measurement and ControlSchool of Instrument Science and EngineeringSoutheast UniversityNanjing210096China Department of Computer Engineering National Institute of TechnologyKurukshetra136119India Department of Computer Science and Information Engineering Asia UniversityTaichung41449Taiwan IT Fundamentals and Education Technologies Applications University of Information Technology and Management in RzeszowRzeszow Voivodeship100031Poland

In sensor networks,it is a challenge to ensure the security of data exchange between packet switching nodes holding different private *** order to solve this problem,the present study proposes a scheme called multi-conditional proxy broadcast re-encryption(MC-PBRE).The scheme consists of the following roles:the source node,proxy server,and the target *** the condition is met,the proxy can convert the encrypted data of the source node into data that the target node can directly *** allows the proxy server to convert the ciphertext of the source node to a new ciphertext of the target node in a different group,while the proxy server does not need to store the key or reveal the *** the same time,the proxy server cannot obtain any valuable information in the *** paper formalizes the concept of MC-PBRE and its security model,and proposes a MC-PBRE scheme of ciphertext ***,the scheme security has been proved in the random oracle.

关键词： Proxy re-encryption sensor network security broadcast re-encryption

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