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Detecting and Diagnosing Alzheimer's Disease: A Transformer-Based Survey (DAD-T) 4

Detecting and Diagnosing Alzheimer's Disease: A Transformer-...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Selvam, Prabu Gunasundari, C. Tamilselvi, M. Haneef, Pinjari Suresh, T. Jini, R. School of Computing Srm Institute of Science & Technology Tiruchirapalli Campus Tiruchirapalli India Roever Engineering College Department of Computer Science & Engineering Tiruchirapalli India India K. Ramakrishnan College of Engineering Department of Aiml Tiruchirapalli India Sri Muthukumaran Institute of Technology Department of Information Technology Chennai India

ISBN: (纸本)9798350352931

Alzheimer's Disease (AD) represents a significant health challenge in today's world. Applying deep learning across various disciplines has demonstrated remarkable potential and has garnered substantial interest. The Transformer model has been shown to have exceptional performance across various computer vision tasks, significantly enhancing its application in medical imaging. Compared to conventional CNN, RNNs, and traditional deep learning algorithms, the Transformer network has demonstrated encouraging results in numerous studies and tasks. This paper reviews multiple works for Mild Cognitive Impairment, early diagnosis, AD and related conditions in the Transformer network over the last half-decade. This paper also presents a comprehensive overview of the data sources, pre-processing, feature extraction, training protocols, and evaluation methods to serve as a resource for future research on AD. Additionally, this survey offers a performance comparison of Transformer models across different datasets, a concise examination of these challenges and suggests potential directions for future research. © 2024 IEEE.

关键词： Neurodegenerative diseases

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Forecasting Wheat Yield Using Long Short- Term Memory Considering Soil and Metrological Parameters 3

Forecasting Wheat Yield Using Long Short- Term Memory Consid...

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3rd International Conference on Intelligent Communication and Computational Techniques, ICCT 2023

作者： Babbar, Nandini Kumar, Ashish Kuma Verma, Vivek Science & Engineering Manipal University Department of Computer Jaipur Rajasthan India Manipal University Department of Information Technology Jaipur Rajasthan India

ISBN: (纸本)9781665453578

Early-season Crop Yield Prediction can assist farmers in India's leading economic sector of agriculture by assisting them in formulating their decision-making strategies. Deep Learning approaches have surpassed conventional statistical methods for yield prediction and crop forecasting as the artificial intelligence field has grown. The goal of the current work is to employ a LSTM model to estimate wheat crop yields in India. The dataset in this paper consist of soil and the metrological parameters. On the basis of consideration of individual factor one at a time, soil parameters such as temperature, humidity, moisture, soil type, crop, nitrogen, potassium, phosphorous in addition to nourishment used with consideration of metrological data, it contains minimum and maximum temperature as well as rainfall. At the end, we are able to get the accuracy and mean absolute error with R2 value for both the parameters. Later, we can merge these two parameters and get more efficient results for accurate prediction. © 2023 IEEE.

关键词： Long short-term memory

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AI meets AI: Artificial Intelligence and Academic Integrity - A Survey on Mitigating AI-Assisted Cheating in Computing Education 23

AI meets AI: Artificial Intelligence and Academic Integrity ...

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24th Annual Conference on information technology Education, SIGITE 2023

作者： Xie, Ying Wu, Shaoen Chakravarty, Sumit Department of Information Technology Kennesaw State University United States Department of Electrical and Computer Engineering Kennesaw State University United States

ISBN: (纸本)9798400701306

This paper discusses pressing issues in the area where Artificial Intelligence (AI) meets Academic Integrity (AI). It starts by outlining the potential consequences of AI-assisted cheating, including the risks posed to education quality, fairness, and the credibility of academic institutions. After reviewing an array of strategies reported in the literature to counteract such cheating, this paper calls for rigorous research to assess the effectiveness of those strategies. It further suggests a range of research topics in detecting AI-generated content and highlights a promising research direction focusing on motivating students' interest in learning through innovative AI applications that divert their efforts away from misuse of technology. Lastly, the paper suggests that addressing AI cheating requires ethical education, academia-industry collaboration, integration into AI ethics, and an international consortium. One of the unique contributions of this paper is outlining a range of potential research directions, both technical and non-technical, in this area where AI meets AI. © 2023 Owner/Author.

关键词： Artificial intelligence

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Navigating Web3 Evolution with Blockchain's Role in Shaping Next Generation Internet Semantics

Navigating Web3 Evolution with Blockchain's Role in Shaping ...

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2024 International Conference on Communication, computer Sciences and engineering, IC3SE 2024

作者： Subhashini, P. Alekhya, J. Rana, Ajay Lakhanpal, Sorabh Veeresh, G. Al-Allak, Mustafa Abdulhussein Mlr Institute Of Technology Department Of Computer Science And Information Technology Telangana Hyderabad India Institute Of Aeronautical Engineering Department Of Computer Science And Engineering Telangana Hyderabad India Amity School Of Engineering And Technology Amity University Greater Noida India Lovely Professional University Phagwara India New Horizon College Of Engineering Bangalore India College Of Medical Technology The Islamic University Najaf Iraq

ISBN: (纸本)9798350366846

Web3 needs complex semantic frameworks to update, validate, and regulate data across decentralized networks as it grows. The whole semantic system of this study includes SV, SC, DOC, ISV, and SG. The Semantic Validation technique performs parametric validation, scoring, and threshold comparison to safeguard data integrity, whereas the Semantic Agreement algorithm combines meanings by adding agreement values. Decentralized ontologies are improved using Distributed Ontology Construction to accommodate new meaning linkages. connected Semantic Verification detects semantic meaning compatibility across linked networks. Lastly, meaning Governance enables individuals to decide on recommended meaning modifications without centralization. Comparative analysis examines the framework. Tables and illustrations indicate its dominance over key aspects. The recommended strategy generally outperforms Web3 evolution methods in security, scale, interoperability, user privacy, and government effectiveness. Visualizing the process with pie charts, layered analysis, and temporal trends shows its efficacy. This semantic framework's consistency, correctness, and flexibility are key responses to Web3's changes. The framework's continual refinement methods can shape Web3 Internet semantics as open networks change. This semantic framework helps establish a decentralized and connected Internet as bitcoin and Web3 technologies progress. It also allows Web3-aligned semantic development. © 2024 IEEE.

关键词： Semantics

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Applying Fuzzy Logic for Constructing Self-Organized Underwater Communication Networks

Applying Fuzzy Logic for Constructing Self-Organized Underwa...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Gautam, Chandra Kant Kumar, Amit Bhuvana, J. Sanskriti University Department of Mechanical Engineering Uttar Pradesh Mathura India College of Engineering Teetthanker Mahaveer University Uttar Pradesh Moradabad India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350383348

this text surveys the usage of fuzzy logic for constructing self-organized underwater communication networks. Fuzzy common sense is a type of computing logic where approximate in place of particular values are applied. Fuzzy common sense is utilized in several underwater communique network designs due to its potential of making choices primarily based on records that is inconsistent, approximate, and with out clear parameters. The emphasis of this text is on Hierarchical Fuzzy logic (HFL) and its use inside the production of underwater communique networks. HFL allows self-agency thru the use of fuzzy rulebased structures that make use of fuzzy common sense to make choices at each step. thru HFL, a gadget can create an prepared, dependable conversation network based totally on its surroundings and the statistics available. using statistics which includes noise stages, speed/distance of moving objectives, and verbal exchange variety, HFL permits underwater communique networks to create dynamic clusters that could adapt to the surroundings and carefully hyperlink all nodes inside the machine. in addition, HFL provides the ability to detect performance degradation resulting from nodes departing from the network, and restoration techniques to conquer those changes. eventually, the object discusses how HFL can reduce the complexity of the machine, and outlines future research possibilities in its application. © 2024 IEEE.

关键词： communique performance possibilities self-organized speed/distance

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A Comparative Analysis of Routing Protocols in Vehicular Delay-Tolerant Networks 15

A Comparative Analysis of Routing Protocols in Vehicular Del...

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

作者： Khurana, Jatin Roopashree, R. Meshram, Vishal A. Balaji, N.V. Gupta, Shikhar Reena, R. Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India School of Sciences Department of Chemistry and Biochemistry Karnataka Bangalore India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Karpagam Academy of Higher Education Department of Computer Science Coimbatore641021 India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Department of Information Technology Chennai127 India

ISBN: (纸本)9798350370249

In this paper, a comprehensive comparative evaluation of routing protocols based totally on global Telecommunication Union-Telecommunication (ITU-T) requirements and IEEE standards for vehicular delay-tolerant networks (V-DTNs) is performed. The performance of the protocols, namely, Dynamic source Routing (DSR), supply Tree Adaptive Routing (big name) Protocol, ad hoc On-demand Distance Vector Routing (AODV), vacation spot-Sequenced Distance-Vector Routing (DSDV), vicinity-primarily based Opportunistic Forwarding (LOF), and link country-based Routing Protocol (LSRP) is evaluated for exclusive routing metrics, which encompass communication overhead, routing latency, quit-to-quit postpone, records delivery ratio, packet delivery ratio, and network throughput. Moreover, the simulations experiments are conducted with numerous network configurations, vehicular mobility, vehicular density, and ranging variety of nodes. The result of the comparative evaluation well-known shows that the DSR, megastar Protocol, and LRP performs higher than the other routing protocols for V-DTNs. additionally, DSR protocol has the bottom routing latency and the highest throughput and packet transport ratio for all the examined network environments. © 2024 IEEE.

关键词： Distance Environments Experiments Latency Numerous Routing Throughput Transport

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Object Detection with YOLO Version 3 for Big Data 1

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5th International Conference on information Systems and Management Science, ISMS 2022

作者： Saxena, Rajat Dubey, Shatendra Karna, Vishma Kumar Big Data Lab Department of Computer Science and Engineering Shri Vaishnav Institute of Information Technology Shri Vaishnav Vidyapeth Vishwavidyalaya Indore India Big Data Lab Department of Information Technology NRI Institute of Information Science and Technology Bhopal India

ISBN: (数字)9783031311536

ISBN: (纸本)9783031311529

Object detection is the most vital task for the application of computer vision. The You only Look Once (YOLO) version3 is the most promising technique used for deep learning-based object detection. It is the k-means cluster method, which estimates initial weight and height of the predicting bounding boxes that are sensitive to the initial cluster centers. This paper focuses on study of approaches for efficient object detection techniques in the field of computer vision. The improvement in the accuracy had been studied based on the deep learning techniques. It aims to develop an online tool for a better study about the object detection techniques as a platform to review the object detection in both online and offline images. A popular state of art object detection algorithm called YOLOv3 which is trained on COCO and ImageNet. The result obtained in the developed online tool has shown a positive result with a possibility in detecting the most common objects available in this real world. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Object detection

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Performance Comparison of Routing Protocols for Mobile Wireless Mesh Networks

Performance Comparison of Routing Protocols for Mobile Wirel...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Kavitha, R. Gautam, Chandra Kant Vera, Pradeep Kumar Department of Computer Science and Information Technology Bangalore India Sanskriti University Department of Mechanical Engineering Uttar Pradesh Mathura India College of Engineering Teetthanker Mahaveer University Uttar Pradesh Moradabad India

ISBN: (纸本)9798350383348

Wireless mesh networks (WMNs) are becoming increasingly famous because of their easy deployment with minimum infrastructure and occasional operational prices. As a result, routing protocols are a crucial thing of WMNs, and performance metrics, including throughput, throughput equity, and packet shipping ratio, have been extensively used to compare routing protocols. In this technical abstract, we evaluate the overall performance comparison of routing protocols for cell WMNs in terms of the metrics above. The overall performance of routing protocols for cellular WMNs can vary greatly depending on the environment and mobility sample of the nodes. Several routing protocols have been proposed inside the literature, together with advert hoc on-call for distance vector (AODV), dynamic supply routing (DSR), and distance vector routing (DVR). Network simulations are essential. Some works have investigated the performance of AODV, DSR, and DVR in cell WMNs. The consequences of this research display that DSR and AODV have better throughput and throughput fairness than DVR in cellular WMNs. However, the AODV packet delivery ratio is higher than DSR in situations with nodes exhibiting random mobility. In eventualities with mild node mobility, AODV additionally has a fine packet delivery ratio. Additionally, simulations. © 2024 IEEE.

关键词： Additionally Deployment Exhibiting Increasingly Performance Protocols Technical

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Optimizing Large-Scale Data Processing with Artificial Intelligence 3

Optimizing Large-Scale Data Processing with Artificial Intel...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Khurana, Saniya Kumar, Ritesh Beemkumar, N. Shukla, Anupam Nirmala, D. Meshram, Vidula V. Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Mechanical Engineering Karnataka Bangalore India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350319125

massive-scale statistics processing presents precise demanding situations in extracting insights, as the sheer amount of records can be overwhelming for classic analytics systems. Artificial Intelligence (AI) can offer a powerful answer for coping with large-scale records units, because it leverages system mastering algorithms to construct sophisticated fashions and automate complex evaluation. AI algorithms are also adaptive and incredibly green, which reduces the time it takes to mine valuable insights from big information units. This paper will study the diverse approaches that AI can enhance large-scale information processing and explore the numerous advantages of deploying AI in statistics processing workflows. The dialogue will include the advantages of leveraging AI for both supervised and unsupervised getting to know tasks in addition to the technical concerns around deciding on the proper AI technology for information processing jobs. Subsequently, the paper will present actual- world applications of AI in huge-scale records processing and evaluate the ability for groups to gain an aggressive aspect by deploying AI of their information pipelines. © 2023 IEEE.

关键词： Aggressive Applications information Numerous Present

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Exploring Supervised Machine Learning Classifiers for Epileptic Seizure Detection over Two Distinct Preprocessed Datasets 9

Exploring Supervised Machine Learning Classifiers for Epilep...

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9th IEEE International Conference for Convergence in technology, I2CT 2024

作者： Sharma, Ashish Singh, Vinai K Motherhood University Haridwar Uttarakhand Roorkee India Lebanese French University College of Engineering And Computer Science Department of Information Technology Kurdistan Region Iraq

ISBN: (纸本)9798350394474

Purpose: Medical practitioners face difficulty reviewing continuous ElectroEncephaloGram (EEG) recordings in terms of automatically detecting seizures for diagnosing ***: Machine learning (ML) techniques are extensively used for many application areas. We apply supervised ML methods are support vector machine (SVM), decision tree (DT), random forest (RF), k-nearest neighbor (k-NN), and extreme gradient boost (XGBoost) to compare two Dataset-1: 'PREPROCESSED CHB-MIT SCALP EEG DATABASE', and Dataset-2: 'PREPROCESSED EEG DATASET WITH EPILEPTIC SEIZURE FROM SNMC BAGALKOT, INDIA' distinct datasets' performance for classifying non-seizure andseizure ***:It is shown that the approximate classification accuracy reaches for Dataset-1 and Dataset-2 up to 74% and 99%, respectively. The highest values under performance analysis have been shown through each dataset's computing are precision of 77.72% and 99.25%, recall of 69.43% and 99.44%, f1-score of 71.51% and 99.31%;and Matthews Correlation Coefficients (MCC) of 48.58% and 98.61% metrics ***: Hence, the XGBoost is an efficient classifier for both datasets used in this research. ML classifier is an effective tool for neuro practitioners to detect epileptic seizures in a biomedical EEG signal. © 2024 IEEE.

关键词： Support vector machines

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