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4th International Conference on Frontiers in Computing and Systems, COMSYS 2023

作者： Murthy, Jamuna S. Siddesh, G.M. Department of Computer Science and Engineering Ramiah Institute of Technology Bengaluru India Department of Artificial Intelligence and Data Science Ramiah Institute of Technology Bengaluru India

ISBN: (纸本)9789819726103

The recent surge in public space criminal activities underscores the need for an efficient system to promptly detect, recognize, and track criminals. Existing AI-based criminal detection literature, while insightful, has limitations such as the complexity to analyze video data, time, and speed of accuracy when it comes to training the algorithms that call for further advancements. Hence proposed AI-driven system addresses this demand, aiming to automate criminal identification, equipping law enforcement with a potent tool for proactive crime prevention and resolution. The system utilizes the innovative "CriminalNet-228" Convolutional Neural Network (CNN) architecture, meticulously trained on a vast criminal image dataset for enhanced detection accuracy. To bolster face detection, additional computational resources like parallel processing and distributed computing are employed, enabling real-time analysis of extensive CCTV footage. Notably, the system tracks identified criminals, providing law enforcement with real-time situational awareness. CriminalNet-228 achieves an impressive overall Mean Average Precision (mAP) of 0.65 and excels in detecting facial features as small as 4 × 4 pixels, demonstrating its detail-oriented recognition capabilities. When evaluating Proposed CriminalNet-228 in comparison to established state-of-the-art techniques like Fast-RCNN, Yolov7, and AlexNet, it surpassed them in terms of precision, recall, f-measure, and accuracy, achieving an impressive accuracy rate of 99.2%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Face recognition

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A Survey of Integrating Generative Artificial Intelligence and 6G Mobile Services: Architectures, Solutions, Technologies and Outlooks

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IEEE Transactions on Cognitive Communications and Networking 2025年第3期11卷 1334-1356页

作者： Liu, Yi-Jing Du, Hongyang Xu, Xinyi Zhang, Ruichen Feng, Gang Cao, Bin Niyato, Dusit Kim, Dong In Jamalipour, Abbas Letaief, Khaled B. Tafazolli, Rahim University of Electronic Science and Technology of China National Key Lab on Wireless Communications Chengdu China University of Hong Kong Department of Electrical and Electronic Engineering Pok Fu Lam Hong Kong Nanyang Technological University College of Computing and Data Science Singapore Singapore Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing China Sungkyunkwan University Department of Electrical and Computer Engineering Suwon Korea Republic of University of Sydney School of Electrical and Computer Engineering Sydney Australia Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Hong Kong University of Surrey Institute for Communication Systems School of Computer Science and Electronic Engineering United Kingdom

Generative artificial intelligence (GenAI) is rapidly driving a new phase of artificial intelligence revolution, marked by various applications such as ChatGPT, Sora and DeepSeek. With powerful capabilities in content generation, personalization and user interaction, GenAI can drive significant enhancements in various mobile services to satisfy individual user preferences. Meanwhile, mobile services offer substantial support for accessibility, functionality and intelligence of GenAI applications. However, current research on integrating GenAI and mobile services remains nascent, lacking necessary theoretical and technical support. In this survey, we break down a new vision to guide research on integrating GenAI and mobile services, aiming to improve 6G intelligence levels and enable 6G mobile networks to support the integration of GenAI and mobile services effectively. We begin by examining the necessity of integrating GenAI with 6G mobile services and exploring potential learning architectures of GenAI models within mobile networks. Next, we analyze potential solutions from two perspectives: how 6G mobile networks empower GenAI and how GenAI enables mobile services. We subsequently explore a promising use case, along with essential features and techniques, to facilitate the integration of GenAI and mobile services. Finally, we discuss future research directions, aiming at realizing intelligent 6G networks. © 2015 IEEE.

关键词： Generative adversarial networks

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Research on User Behaviour Prediction in Social Networks Based on Deep Learning Model

Research on User Behaviour Prediction in Social Networks Bas...

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2024 IEEE International Conference on Electronic Systems and Intelligent Computing, ICESIC 2024

作者： Kavitha, G. Veena, K. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai600 119 India

ISBN: (纸本)9798331522988

User interaction and social behaviour are key components in the study of social networks because they help manage and forecast user behaviour on sites like Facebook, Twitter, microblogging, and others. The mobile communication network, which is intimately related to people's everyday life, is taken into consideration in this study. By determining the rules that exist in the structure of communication social networks, the structural features are analysed through use of complex network analysis methodologies. Deep learning, artificial intelligence, and statistics, this challenge is frequently solved. The challenge of identifying unusual activity on social media has been tackled in a number of ways, which fall into three categories: Hybrid approaches, behavioural techniques based on the extraction and analysis of user activity, and structural methods based on the study of social network graphs are also available. By identifying key characteristics of the users' calling behaviour, social behaviours or patterns related to age, gender, social scope, age disparities, and time may be examined. to increase speed and performance. User behaviour in social networks may be ascertained. © 2024 IEEE.

关键词： Federated learning

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Prediction on Soybean Crop Yield Applying Machine Learning Regression 1

Prediction on Soybean Crop Yield Applying Machine Learning R...

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1st International Conference on Cognitive, Green and Ubiquitous Computing, IC-CGU 2024

作者： Ram, Melam Siva Jayasaikumar, Y. Iniyan, S. SRM Institute of Science and Technology Kattankulathur Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350361780

In modern times, agriculture heavily relies on crop yield prediction systems to provide farmers with various insights into the potential outcomes of their farming practices. The system we developed uses regression to predict the soybean crop yield. The soybean crop yield prediction system is invaluable for farmers, aiding them in making informed decisions regarding the optimal timing for getting analytics about the crops based on different factors to maximize yield. The factors, such as precipitation, solar radiation, and minimum and maximum temperatures, are also considered in the developed model data. In this study, the dataset used consists of soybean crop yield data and all the crop yield affecting parameters from the past thirty-eight years, which are gathered from 105 different locations. © 2024 IEEE.

关键词： Decision trees

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A Review of Fintech Management and Security Development 10

A Review of Fintech Management and Security Development

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10th IEEE Information technology International Seminar, ITIS 2024

作者： Ritonga, Ali Akbar Zuhri Harahap, Syaiful Sooai, Adri Gabriel Nugroho, Aryo Samsir Prasetyo Adi, Puput Dani Universitas Labuhanbatu Faculty of Science and Technology Department of Informatics Management North Sumatera Labuhanbatu Indonesia Universitas Labuhanbatu Faculty of Science and Technology Department of Information System North Sumatera Labuhanbatu Indonesia Universitas Katolik Widya Mandira Faculty of Computer Science Department of Computer Engineering NTT Kupang Indonesia Universitas Narotama Faculty of Computer Science Department of Information System Surabaya Indonesia Universitas Labuhanbatu Faculty of Computer Science Department of Informatics Engineering North Sumatera Labuhanbatu Indonesia Department of Telecommunication Bandung Indonesia

ISBN: (纸本)9798331521295

This research focuses on the review of Fintech and its development on the IoT Platform and also the risks that can be posed to the IoT network used. Finance is the most essential side of several other sectors which in this case often experience many losses, one of the biggest losses is the existence of hackers and theft of money through the Internet platform or Internet Server. This research reviews how the ins and outs of the flow of funds located on servers or online banks. And ways to overcome hackers and bypass data. Comprehensively reviewed in this research review. This research discusses Cryptocurrency Analysis, Bitcoin, and Ethereum Comparison which are some of the methods used in online-based Finance, which is without currency but has the same value even more than currency if the conversion process is carried out at the bank. In this research, it is comprehensively discussed how methods are carried out in the process of securing data, Cryptocurrency, Bitcoin, and Ethereum are comprehensively discussed to get a lot of data and comparisons and the right steps to deal with crime via the internet. The predicted use of IoT in the Fintech sector is 140 billion connected devices, IoT Loans 110 billion connected devices, and IoT insurance with an estimated 90 billion connected devices, in the approaching 2026. © 2024 IEEE.

关键词： Ethereum

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DNA Transcription and Translation Inspired Deep Features for Classification-Based CBIR 23th

DNA Transcription and Translation Inspired Deep Features fo...

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23rd International Conference on Intelligent Systems Design and Applications, ISDA 2023

作者： Pradhan, Jitesh Pal, Arup Kumar Islam, SK Hafizul Samanta, Debabrata Department of Computer Science and Engineering National Institute of Technology Jharkhand Jamshedpur831014 India Jharkhand Dhanbad826004 India Department of Computer Science and Engineering Indian Institute of Information Technology West Bengal Kalyani741235 India Department of Computing and Information Technologies Rochester Institute of Technology Rochester Kosovo

ISBN: (纸本)9783031648465

DNA is a macromolecule that carries the genetic information of nearly all living things on the planet. They not only determine the characteristics and behavior of an organism but also pass the essential features to the next generation, ensuring that "like begets like." Because of their same genetic structure, organisms of the same species appear identical. Inspired by this property, a novel DNA-based scheme for class-based image retrieval has been proposed. The algorithm imitates the flow of genetic information, which is initially stored in the DNA and is transcripted and translated to RNA and amino-acid sequences, respectively, using genetic coding. Since similar images would generate a similar sequence, ensuring the preservation of salient features of the images required for retrieval. Thus, these amino-acid sequences are then deployed on a DNA-inspired ResNet-50 CNN architecture for performing image classification-based image retrieval. The proposed scheme has been extensively tested on six different datasets to demonstrate its performance. Comparative results reveal that the proposed scheme outperforms competing state-of-the-art Content-based Image Retrieval techniques in terms of retrieval performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： DNA

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Information Structures in AC/DC Grids 63

Information Structures in AC/DC Grids

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Taylor, Josh New Jersey Institute of Technology Newark Department of Electrical and Computer Engineering NJ07102 United States

ISBN: (纸本)9798350316339

The converters in an AC/DC grid form actuated boundaries between the AC and DC subgrids. In both simple linear and balanced dq-frame models, the states on either side of these boundaries are coupled only by control inputs. In this paper, we show how this topological property imparts all AC/DC grids with poset-causal information structures. A practical benefit is that certain decentralized control problems that are hard in general are tractable for poset-causal systems. We also show that special cases like multi-terminal DC grids can have coordinated and leader-follower information structures. © 2024 IEEE.

关键词： Decentralized control

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Bloom’s Taxonomy Based Question Analysis for Personalized Learning 7th

Bloom’s Taxonomy Based Question Analysis for Personalized L...

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7th IFIP TC 12 International Conference on Computational Intelligence in Data Science, ICCIDS 2024

作者： Jeslin Shanthamalar, J. Sheelam, Dinesh Bodla, Shiva Raj Gowri Manohari, V. Nancy Noella, R.S. Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Tamil Nadu Chennai600119 India

ISBN: (纸本)9783031699856

In the 1950s, Benjamin Bloom and his associates introduced Bloom’s Taxonomy, a foundational framework for categorizing learning objectives and cognitive skills. While question classification typically operates at a fine level, such as sentences and phrases, and text classification focuses on the document level, past research has explored the intersection of question classification and Bloom’s Taxonomy to assess learners’ cognitive levels in higher education. However, existing feature types from previous studies may excel in datasets with narrowly focused queries, necessitating the development of multiple classifiers for diverse fields or areas. To address this, A new kind of feature called "taxonomy-based" is suggested to improve the accuracy of question classification in datasets from different fields. Utilizing datasets comprising questions from distinct topics, the study evaluates the effectiveness of taxonomy-based features. Support vector machines (SVMs) are chosen as the classifier for their reputed text classification accuracy. The research reveals that taxonomy-based features significantly improve classifier performance when applied to question sets from different domains. This effort is driven by the need to improve question categorization accuracy over a variety of datasets and the realization of Bloom’s Taxonomy’s continuing use in educational settings. Although Bloom’s Taxonomy offers an extensive framework for comprehending cognitive abilities and learning objectives, current question classification techniques frequently do not have the granularity necessary to fully utilize this taxonomy. Furthermore, prior work has mostly concentrated on fine-level categorization, ignoring the advantages of integrating Bloom’s Taxonomy into the classification procedure. The highest accuracy achieved using SVM with TF-IDF vectorizer is 74.17% for BCL’s Data set and 95.23% for BT Dataset. Employing the KNN algorithm with the TF-IDF vectorizer yields 59.17% accuracy for

关键词： Support vector machines

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Restaurant Recommendation System Based on ML Algorithms and Real-Time Web Scraping

Restaurant Recommendation System Based on ML Algorithms and ...

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2024 International Conference on Big Data Analytics in Bioinformatics, DABCon 2024

作者： Rudrapati, Mahima Varghese, Benetta Paul, Carissa Jeya Sekar, MacHlin Singh, Priyamvada Fr. C. Rodrigues Institute of Technology Department of Computer Engineering Navi Mumbai India

ISBN: (纸本)9798350378047

As the restaurant industry expands, the sheer number of dining options makes it increasingly difficult for individuals to find establishments that match their preferences. To address this challenge, we present a personalized restaurant recommendation system that leverages both collaborative filtering and content-based techniques, augmented by real-time web scraping. Users provide their past restaurant ratings and desired location, which is used to recommend dining options based on two algorithms: user similarity and Google reviews. If a location is absent in the database, the system dynamically scrapes Google reviews and applies natural language processing (NLP) to extract feature-opinion pairs, identify sentiment, and compute a reliability score. Collaborative filtering identifies user similarities based on historical ratings, while the content-based model combines sentiment analysis, average ratings, review count, and reviewer reliability to generate a comprehensive restaurant score. Recommendations are ranked and accompanied by a detailed breakdown of the contributing factors, offering users transparency in how the suggestions are tailored to their preferences. This approach not only enhances the accuracy of recommendations but also provides a more informed and personalized dining experience. © 2024 IEEE.

关键词： content based filtering data analysis feature opinion mining natural language processing real time data analysis recommendation sentiment analysis web-scraping

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Dipper Throated Optimization for Detecting Black-Hole Attacks inMANETs

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computers, Materials & Continua 2023年第1期74卷 1905-1921页

作者： Reem Alkanhel El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Mostafa Abotaleb Doaa Sami Khafaga Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of System Programming South Ural State UniversityChelyabinsk454080Russia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

In terms of security and privacy,mobile ad-hoc network(MANET)continues to be in demand for additional debate and *** more MANET applications become data-oriented,implementing a secure and reliable data transfer protocol becomes a major concern in the ***,MANET’s lack of infrastructure,unpredictable topology,and restricted resources,as well as the lack of a previously permitted trust relationship among connected nodes,contribute to the attack detection burden.A novel detection approach is presented in this paper to classify passive and active black-hole *** proposed approach is based on the dipper throated optimization(DTO)algorithm,which presents a plausible path out of multiple paths for statistics transmission to boost MANETs’quality of service.A group of selected packet features will then be weighed by the DTO-based multi-layer perceptron(DTO-MLP),and these features are collected from nodes using the Low Energy Adaptive Clustering Hierarchical(LEACH)clustering *** is a powerful classifier and the DTO weight optimization method has a significant impact on improving the classification process by strengthening the weights of key features while suppressing the weights ofminor *** hybridmethod is primarily designed to combat active black-hole *** the LEACH clustering phase,however,can also detect passive black-hole *** effect of mobility variation on detection error and routing overhead is explored and evaluated using the suggested *** diverse mobility situations,the results demonstrate up to 97%detection accuracy and faster execution ***,the suggested approach uses an adjustable threshold value to make a correct conclusion regarding whether a node is malicious or benign.

关键词： Black-hole attack mobile ad-hoc network optimization dipper throated optimization

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