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2023 International Conference on Artificial Intelligence and Smart Communication, AISC 2023

作者： Kumar, Amit Bhowmik, Biswajit National Institute of Technology Brics Laboratory Department of Computer Science and Engineering Karnataka Bharat Mangalore India

ISBN: (纸本)9798350322309

Bharat is a cultivation-based country that cultivates various crops, including rice, in colossal capacity. Rice is one of the daily used crops across the country and other parts of the globe. Rice is cultivated in almost every state in Bharat. However, rice plant diseases severely degrade the quality and quantity of the crop. Rice plants can be affected by different conditions, e.g., foot rot, sheath blight, etc., causing a loss in the farming output. Various steps, including academic research, are practiced to detect rice diseases in their early stage to minimize the loss and increase the productivity of the agriculture sector. The schemes involve traditional and technology-based innovative solutions like agricultural cyber-physical systems (ACPS) or precision agriculture. This paper provides a state-of-the-art on rice crop cultivation, its disease classifications, etc. The paper depicts the importance of rice in daily life and how it plays a vital role in health. Later, this paper provides statistics on rice production in Bharat, followed by the export of rice to different countries. Subsequently, we offer a detailed analysis of rice cultivation, various diseases that can occur in rice plants and other prevention mechanisms for these diseases in the early stages of their life cycle. Finally, recent developments in precision agriculture are provided. © 2023 IEEE.

关键词： Precision agriculture

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Classification of Stakeholders of an Information System using Fuzzy based Adjusted Cosine Similarity Measure during Requirements Elicitation Process 3

Classification of Stakeholders of an Information System usin...

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3rd International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2023

作者： Akram, Faiz Ahmad, Tanvir Sadiq, Mohd Department of Computer Engineering Faculty of Engineering and Technology Jamia Millia Islamia New Delhi25 India Software Engineering Laboratory Computer Engineering Section UPFET Jamia Millia Islamia New Delhi25 India

Requirements elicitation process is employed for the identification of stakeholders of an information system. A large number of stakeholders from different domains across the globe participate during the requirements elicitation process. Based on our review, we found that existing methods for the analysis of stakeholders do not support how to classify the stakeholders based on the similarity measures. These measures play an important role for the recommendation of an item/user in a recommendation system. Thus, the objective of this paper is to classify the stakeholders of an information system using fuzzy-based adjusted cosine similarity measure. We have identified the stakeholders of library information system and their opinions for different requirements are recorded. The identified stakeholders have been classified and analysed based on the following similarity measures under fuzzy environment, i.e., Cosine similarity measure, Euclidean distance, Pearson coefficient correlation etc., so that stakeholders having similar requirements can be recommended to strengthen the requirements elicitation process. © 2023 Elsevier B.V.. All rights reserved.

关键词： Recommender systems

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IoT-Enabled Smart Applications and Challenges 8

IoT-Enabled Smart Applications and Challenges

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8th International Conference on Communication and Electronics Systems, ICCES 2023

作者： Prathyusha, M.R. Bhowmik, Biswajit National Institute of Technology Karnataka Brics Laboratory Department of Computer Science and Engineering Mangalore575025 India

ISBN: (纸本)9798350396638

Significant advances in the development of information and communication technology lead to collaborating computational entities into cyber-physical systems (CPS). A CPS serves as the foundation for many latest technology, e.g., the internet of things (IoT). Both IoT and CPS technologies provide several advantages that make our daily lives more simple and smart. These technologies are employed for a wide range of smart applications like smart city, smart home, smart transport, smart healthcare, etc., that can improve business, security, and health-related experiences. This paper primarily explores various IoT-enabled smart applications and examines their necessity, characteristics, and related issues. The paper also highlights various challenges of these smart applications followed by the state-of-the-art to address them. Each smart application promotes a sustainable, healthier, and comfortable life, including their future in the smart world. © 2023 IEEE.

关键词： Cyber Physical System

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Games for Artificial Intelligence Research: A Review and Perspectives

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 5949-5968页

作者： Hu, Chengpeng Zhao, Yunlong Wang, Ziqi Du, Haocheng Liu, Jialin Research Institute of Trustworthy Autonomous System Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Shenzhen518055 China LightSpeed Studios Tencent Shenzhen518057 China

Games have been the perfect test beds for artificial intelligence (AI) research for the characteristics that widely exist in real-world scenarios. Learning and optimization, decision-making in dynamic and uncertain environments, game theory, planning and scheduling, design, and education are common research areas shared between games and real-world problems. Numerous open-source games or game-based environments have been implemented to study AI. In addition to single- or multiplayer, collaborative, and adversarial games, there has also been growing interest in implementing platforms for creative design in recent years. Those platforms provide ideal benchmarks for exploring and comparing AI ideas and techniques. This article reviews the games and game-based platforms for AI research, provides guidance on matching particular types of AI with suitable games for testing and matching particular needs in games with suitable AI techniques, discusses the research trend induced by the evolution of those games and platforms, and gives an outlook. © 2024 IEEE.

关键词： Reinforcement learning

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A Novel High-Efficiency Transaction Verification Scheme for Blockchain Systems

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computer Modeling in engineering & Sciences 2024年第5期139卷 1613-1633页

作者： Jingyu Zhang Pian Zhou Jin Wang Osama Alfarraj Saurabh Singh Min Zhu School of Computer&Communication Engineering Changsha University of Science&TechnologyChangsha410015China Science and Technology on Information Systems Engineering Laboratory School of Systems EngineeringNational University of Defense TechnologyChangsha410073China Computer Science Department King Saud UniversityRiyadh11437Saudi Arabia Department of Artificial Intelligence and Big Data Woosong UniversityDaejeon34606South Korea College of Information Science and Technology Zhejiang Shuren UniversityHangzhou310015China

Blockchain can realize the reliable storage of a large amount of data that is chronologically related and verifiable within the *** technology has been widely used and has developed rapidly in big data systems across various *** increasing number of users are participating in application systems that use blockchain as their underlying *** the number of transactions and the capital involved in blockchain grow,ensuring information security becomes *** the verification of transactional information security and privacy has emerged as a critical ***-based verification methods can effectively eliminate the need for centralized third-party ***,the efficiency of nodes in storing and verifying blockchain data faces unprecedented *** address this issue,this paper introduces an efficient verification scheme for transaction ***,it presents a node evaluation module to estimate the activity level of user nodes participating in transactions,accompanied by a probabilistic analysis for all ***,this paper optimizes the conventional transaction organization form,introduces a heterogeneous Merkle tree storage structure,and designs algorithms for constructing these heterogeneous *** analyses and simulation experiments conclusively demonstrate the superior performance of this *** verifying the same number of transactions,the heterogeneous Merkle tree transmits less data and is more efficient than traditional *** findings indicate that the heterogeneous Merkle tree structure is suitable for various blockchain applications,including the Internet of *** scheme can markedly enhance the efficiency of information verification and bolster the security of distributed systems.

关键词： Blockchain architecture transaction verification information security heterogeneous Merkle tree distributed systems

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Underwater Optical Wireless Communications using Ince Gaussian Beams

Underwater Optical Wireless Communications using Ince Gaussi...

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Robertson, Evan Pires, Danilo G. Free, Justin Litchinitser, Natalia Miller, J. Keith Johnson, Eric G. Micro-Photonics Laboratory The Holcombe Department of Electrical and Computer Engineering Clemson University ClemsonSC29634 United States Department of Electrical and Computer Engineering Duke University DurhamNC27708 United States

ISBN: (纸本)9781957171258

Generation of Ince Gaussian beams can be performed through the nonlinear process. Controlling the phase matching condition of the nonlinear process allows the mode spectrum of the decomposition to be carefully controlled. This beam can be used for constant envelope communication through underwater turbid environments. © Optica Publishing Group 2023.

关键词： Phase matching

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Decentralized Game-based Auction Algorithm for Scheduling Robotic Chargers to Service EVs with Uncertain Demands

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-14页

作者： Fang, Qiuyang Zhang, Jianlei Wang, Chen Xie, Guangming Zhang, Chunyan Department of Automation College of Computer and Control Engineering Nankai University Tianjin China National Engineering Research Center of Software Engineering Peking University Beijing China State Key Laboratory for Turbulence and Complex Systems Intelligent Biomimetic Design Lab College of Engineering Peking University Beijing China

In this study, we introduce a novel auction-based algorithm modeled as a decentralized coalition formation game, designed for the complex requirements of large-scale multi-robot task allocation under uncertain demand. This context is particularly illustrative in scenarios where robots are tasked to charge electric vehicles. The algorithm begins by partitioning a composite task sequence into distinct subsets based on spatial similarity principles. Subsequently, we employ a coalition formation game paradigm to coordinate the assembly of robots into cooperative coalitions focused on these distinct subsets. To mitigate the impact of unpredictable task demands on allocations, our approach utilizes the conditional value-at-risk to assess the risk associated with task execution, along with computing the potential revenue of the coalition with an emphasis on risk-related outcomes. Additionally, integrating consensus auctions into the coalition formation framework allows our approach to accommodate assignments for individual robot-task pairings, thus preserving the stability of individual robotic decision autonomy within the coalition structure and assignment distribution. Simulative analyses on a prototypical parking facility layout confirm that our algorithm achieves Nash equilibrium within the coalition structure in polynomial time and demonstrates significant scalability. Compared to competing algorithms, our proposal exhibits superior performance in resilience, task execution efficiency, and reduced overall task completion times. The results demonstrate that our approach is an effective strategy for solving the scheduling challenges encountered by multi-robot systems operating in complex environments. IEEE

关键词： Polynomial approximation

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Stress Detection Using Deep Learning Algorithms 14

Stress Detection Using Deep Learning Algorithms

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Angalakuditi, Hindu Bhowmik, Biswajit National Institute of Technology Karnataka Brics Laboratory Department of Computer Science and Engineering Mangalore575025 India

ISBN: (纸本)9798350335095

Stress has become a prevalent issue in modern society, with various negative impacts on mental and physical health. Stress in people is a physiological and psychological reaction to an imagined threat or difficulty. Several things, including employment, relationships, income, health problems, and significant life transitions, can cause stress. Depending on the person and the circumstance, stress symptoms can vary. They frequently include emotions of worry, irritation, and restlessness, as well as physical symptoms like headaches and muscle strain. Early stress detection is crucial for effective intervention and prevention of stress-related health issues. Detecting stress in real-time can be valuable in various domains such as healthcare, mental health, human-computer interaction, and workplace performance. This paper proposes a method for detecting stress using deep learning. A set of pre-trained models are employed for stress detection. The proposed technique is evaluated with publicly available datasets. Experimented results showed that the proposed stress detection method achieves accuracy in the range of 85.71-97.50% and the loss ranging from 0.4061 to 1.8144. © 2023 IEEE.

关键词： Stresses

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Cybersecurity advancements for medical image transmission: a hybrid optical-based cryptosystem harnessing chaos, DNA sequences, and mandelbrot keys

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Multimedia Tools and Applications 2025年 1-41页

作者： Alalwan, Nasser El-Shafai, Walid Amoon, Mohammed Benjdira, Bilel Department of Computer and Communication Engineering King Saud University Riyadh Saudi Arabia Prince Sultan University Riyadh11586 Saudi Arabia Robotics and Internet-of-Things Laboratory Prince Sultan University Riyadh12435 Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt

In today's advanced technological age, characterized by innovations like big data processing, cloud computing, and the Internet of Things (IoT), there is a rising utilization of medical multimedia data, especially medical images. These images, integral to the Internet of Healthcare Things (IoHT), necessitate secure transmission due to the increasing risks of unauthorized breaches and tampering. Current security methods, especially for cloud and mobile platforms, often struggle with challenges related to processing capacity, memory use, data size, and energy, making them ill-suited for extensive medical data or resource-limited environments. To address these challenges, this study introduces a novel hybrid cryptosystem, drawing on the unique qualities of the optical Arnold chaotic map, DNA (DeoxyriboNucleic Acid) sequences, and Mandelbrot keys, providing a fortified approach to the secure streaming of medical images. The proposed framework operates via a precise and structured procedure. It begins by applying the optical Arnold chaotic map cipher to each of the three-color channels (R, G, and B) within a medical image. This is followed by overlaying DNA encoding sequences on the resultant encrypted image from the earlier ciphering phase. Leveraging this groundwork, we incorporate an advanced Mandelbrot set-driven shift mechanism specifically designed to create complex confusion patterns within the R, G, and B segments of the encrypted medical imagery. The efficacy of the proposed cryptosystem is rigorously substantiated through an extensive array of simulations supported by a comprehensive security analysis. The results highlight its unparalleled resilience and security capabilities in the realm of medical image encryption, marking a significant leap over previous systems in the literature. Essentially, our work pioneers a solution to a pressing challenge in medical image security, ensuring enhanced protection of delicate health data among the rapidly evolving advanc

关键词： Cybersecurity

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Detection and Identification of Tomato Disease Based on Deep Learning and Machine Learning algorithms 1

Detection and Identification of Tomato Disease Based on Deep...

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1st IEEE Afro-Mediterranean Conference on Artificial Intelligence, AMCAI 2023

作者： Boukhris, Abdelouafi Antari, Jilali Asri, Hiba Sadiq, Abderrahmane Ibn Zohr University Laboratory of Computer Systems Engineering Mathematics and Applications Polydisciplinary Faculty of Taroudant Taroudant Morocco LISI Laboratory Faculty of Sciences Semlalia Department of Computer Sciences Marrakech Morocco

ISBN: (纸本)9798350394689

Plant diseases negatively affect crop production;the quality and the quantity of crop production are being interrupted. Identifying diseases manually is a hard task and time consuming, also the majority of farmers don't have the necessary knowledge to do this operation. So, we need agriculture engineer which are rare. To address these issues, we need an accurate and automated detection system for crop disease. A new technique based on deep learning to detect crop disease is required. In this paper, we propose a new technique called ALS-NN (Autoencoder Latent Space-Neural network) which combines two models Autoencoder and Neural Network. The Autoencoder compress data into Latent Space representation, and the Neural Network is used for classification based on compressed data. The dataset used in this paper is PlantVillage with 4 types of Tomato diseases. Our system achieved 91% accuracy. © 2023 IEEE.

关键词： Deep learning

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