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3rd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2024

作者： Vani, B.V. Guruprakash, C.D. Sri Siddhartha Academy of Higher Education Sri Siddhartha Institute of Technology Saraswathipuram Information Science and Engineering Karnataka Tumakuru572105 India Sri Siddhartha Institute of Technology Department Computer Science and Engineering Karnataka Tumakuru572105 India

ISBN: (纸本)9798350364569

Crop yield prediction has sought tremendous attention in recent years several approaches were developed for crop yield prediction. However, these techniques created certain limitations in real-time applications including a reduction in relative errors, and low prediction accuracy. Therefore, this research proposed a deep learning technique known as improved Adaptive Moment estimation enabled mixed attention-based long short-term memory (IA-MSTM) to predict crop yields. The mixed attention techniques combine the advantages of channel attention with the hard attention mechanism (HAM), which can capture long-term dependencies that are crucial for understanding crop growth dynamics over time. Additionally, the improved repeated Loss-based Adam optimizer adapts the learning rate during training, leading to quicker convergence compared to traditional methods. The performance of the IA-MSTM achieves a minimal error value in terms of Mean Absolute Error (MAE) of 2.00 for TP 90. © 2024 IEEE.

关键词： Prediction models

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Joint User Cooperation and Scheduling for Federated Learning in CFmMIMO Networks 2

Joint User Cooperation and Scheduling for Federated Learning...

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2nd International Conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2023

作者： Bao, Han Gao, Bo Xiong, Ke Fan, Pingyi Beijing Jiaotong University School of Computer and Information Technology Beijing China Beijing Jiaotong University Engineering Research Center of Network Management Technology for High Speed Railway Ministry of Education School of Computer and Information Technology Beijing China Tsinghua University Department of Electronic Engineering Beijing China

ISBN: (纸本)9798350348002

This paper proposes a joint user cooperation and scheduling (JUCS) method to minimize the maximum transmission time among users in the synchronous federated learning (FL) architecture, since the user with the worst quality link limits the training performance of FL. To this end, an optimization problem is formulated to find the optimal user cooperation pairing and the power control coefficients. Meanwhile, as the links among users operate on the same frequency bands, the interface between them is taken into account in this paper. As the problem is non-convex, we decouple it into two sub-problems. For the first sub-problem, we propose a fair scheduling (FS)-based algorithm to schedule the helping users to pair with the ones who are required to be assisted. For the second sub-problem, an algorithm based on deterministic policy gradient (DDPG) is proposed to find the optimized power control coefficients. Experiments show that the proposed JUCS method shortens about 34.48% transmission time of the worst user in FL compared to the traditional one without JUCS. © 2023 IEEE.

关键词： Scheduling algorithms

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Decomposition-Based Classifier Chains for Multi-Dimensional Classification

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2022年第2期3卷 176-191页

作者： Jia, Bin-Bin Zhang, Min-Ling School of Computer Science and Engineering Southeast University Nanjing210096 China College of Electrical and Information Engineering Lanzhou University of Technology Lanzhou730050 China Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University Nanjing210096 China

In multi-dimensional classification, the semantics of objects are characterized by multiple class variables from different dimensions. To model the dependencies among class variables, one natural strategy is to build a number of multiclass classifiers in a chaining structure, one per dimension, where the subsequent classifiers on the chain augment the feature space with all labeling information used by the preceding classifiers. However, it is shown that this strategy cannot compete with existing state-of-the-art approaches via comparative studies. One possible reason is that inaccurate predictions of preceding classifiers would degenerate the performance of subsequent ones. Besides, it is more difficult to learn a multiclass classifier than a binary one with the same accuracy, and better performance can be expected if the multi-dimensional classification problem can be solved by building multiple binary classifiers in a chaining structure. Based on these conjectures, this article proposes an approach, which builds a chain of binary classifiers to solve the multi-dimensional classification problem with the help of one-versus-one decomposition. To address the issue that different one-versus-one decomposed problems involve different training examples, the feature space is augmented with the binary predictions of preceding classifiers on the chain to train the subsequent ones. To alleviate the effect of the specified chaining order, the ensemble version of the proposed approach is further investigated. Comparative studies over 20 benchmark datasets clearly show the superiority of the proposed approach against the state-of-the-art multi-dimensional classification baselines. © 2020 IEEE.

关键词： Machine learning

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Transforming Data Analysis through AI-Powered Data Science 2

Transforming Data Analysis through AI-Powered Data Science

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2nd IEEE International Conference on Futuristic Technologies, INCOFT 2023

作者： Kumar, V. Mathan Vijayaraghavan, P. Meshram, Vidula V. Sharma, Mohit Kumar Nithya, M.S. Kumar, Ritesh Department of Computer ScienceKarpagam Academy of Higher Education Coimbatore641021 India Department of Mechanical Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Vivekananda Global University Department of Electrical Engineering Jaipur India Department of Computer Science and Information Technology Karnataka Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350308846

AI-powered records science is revolutionizing the way facts are analyzed and understood. It can significantly improve the exceptional of information evaluation and boost its speed. AI-powered facts technological know-how enables access to more extensive, extra complicated information sets, faster insights, faster trouble solving, and higher choice making. Using the use of AI-powered information technological know-how techniques and tools, organizations can provide more accurate outcomes with shorter times to choices. AI-powered facts technology also offers more correct predictions of activities and developments and superior accuracy in sample reputation. Furthermore, AI-powered facts science techniques can help agencies discover anomalies in datasets, learn new patterns, and better understand the points. This paper discusses the advantages of AI-powered statistics science and how it may enhance data analysis. It additionally discusses a number of the challenges going through organizations in imposing AI-powered records science projects and gives excellent practices for deploying and coping with records technology projects. © 2023 IEEE.

关键词： Challenges Deploying Number Practices technology

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Objective quality assessment of generated high-resolution human face images 12

Objective quality assessment of generated high-resolution hu...

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12th IEEE International Conference on Identification, information and Knowledge in the Internet of Things, IIKI 2024

作者： Gu, Jiacheng Yue, Ding Yu, Shaode Zhang, Peng Sun, Qiurui Beijing Institute of Technology School of Computer Science Beijing China Communication University of China School of Info. and Comm. Engineering Beijing China Education Management Information Center Ministry of Education of the Prc Beijing China Beijing Normal University Center of Information & Network Technology Beijing China

ISBN: (纸本)9798331510633

Human face image generation can be used in media, marketing, education, creativity, research and privacy protection. Many deep learning networks have been proposed for generating high-resolution human face images, while the quality of generated human face images are not fully investigated. To bridge this gap, 11 StyleGAN-based networks are firstly trained via the database Flickr-Faces-HQ (FFHQ). Then, each network produces 10,000 high-resolution (1024 × 1024) human face images. After that, the quality of these generated images are evaluated with 6 Face Image Quality Assessment (FIQA) metrics. The average metric values suggest that the generated human face images are with higher quality than the realistic images in the FFHQ database. However, the failure cases of generated images remind us that artifacts and distortions exist, and unreasonable combinations and meaningless contents are produced. Therefore, significant progress is still required for achieving reliable quality assessment and high-accuracy generation of human faces. © 2024 IEEE.

关键词： face image quality assessment human face image image generation StyleGAN

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Optimizing Energy Consumption in Smart Grids Using Demand Response Techniques

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Distributed Generation and Alternative Energy Journal 2024年第1期39卷 111-136页

作者： SwornaKokila, M.L. Venkatarathinam, R. Rose Bindu, Joseph P. Manivasagam, M.A. Kishore, Kakarla Hari Department of Computing Technologies School of Computing College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology SRM Nagar Kattankulathur TN Chennai India Department of Information Technology Sri BalajiChockalingam Engineering College A.C.S. Nagar Irumbedu Thiruvannamalai District Tamil Nadu Arni India Department of Mathematics Dayananda Sagar College of Engineering Bangalore India Computer Science and Engineering Siddartha Institute of Science and Technology Andhra Pradesh India Department of Electronics and Communication Engineering KoneruLakshmaiah Education Foundation Guntur Andhra Pradesh Vaddeswaram India

Smart grids have developed as a potentially game-changing strategy for controlling the demand and supply of energy. Unfortunately, peak demand is a significant source of grid instability and rising energy prices, making it one of the most critical difficulties in smart grids. During times of high energy demand on the grid, demand response (DR) strategies incentivize consumers to change how they use energy. This study’s overarching goal is to learn how DR methods may be used to help smart grids make better use of their energy resources. The primary research is to develop a smart DR system that can predict times of high energy demand and proactively alter usage to reduce such periods. Machine learning strategies are utilized in the proposed system to estimate peak demand via past data, weather predictions, and other variables. The system will then alter energy use based on real-time data from smart meters along with other sensing devices to meet the projected demand. The simulation model will include several scenarios for testing the DR system’s flexibility, including a range of weather conditions, load profiles, and grid topologies. Several indicators, including peak demand reduction (80.04%), energy savings (38.09%), environmental consequences, and reaction time ( © 2024 Taylor and Francis Inc.. All rights reserved.

关键词： Smart power grids

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EsECC_SDN:Attack Detection and Classification Model for MANET

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computers, Materials & Continua 2023年第3期74卷 6665-6688页

作者： Veera Ankalu Vuyyuru Youseef Alotaibi Neenavath Veeraiah Saleh Alghamdi Korimilli Sirisha Department of Computer Science and Engineering Koneru Lakshmaiah Education FoundationVaddeswaram522502A.PIndia Department of Computer Science College of Computers and Information SystemsUmm Al-Qura UniversityMakkah21955Saudi Arabia Department of Electronics and Communications DVR&DHS MIC Engineering CollegeKanchikacharla521180A.PIndia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Electronics and Communications BVC Institute of Technology&ScienceAmalapuram533221A.PIndia

Mobile Ad Hoc Networks(MANET)is the framework for social networking with a realistic *** theMANETenvironment,based on the query,information is transmitted between the sender and *** the MANET network,the nodes within the communication range are involved in data *** the nodes that lie outside of the communication range are involved in the transmission of relay ***,due to the openness and frequent mobility of nodes,they are subjected to the vast range of security threats ***,it is necessary to develop an appropriate security mechanism for the dataMANET environment for data *** paper proposed a security framework for the MANET network signature escrow *** proposed framework uses the centralised Software Defined Network(SDN)with an ECC cryptographic *** developed security framework is stated as Escrow Elliptical Curve Cryptography SDN(EsECC_SDN)for attack detection and *** developed EsECC-SDN was adopted in two stages for attack classification and detection:(1)to perform secure data transmission between nodes SDN performs encryption and decryption of the data;and(2)to detect and classifies the attack in theMANET hyper alert based HiddenMarkovModel Transductive Deep ***,the EsECC_SDN is involved in the assignment of labels in the transmitted data in the database(DB).The escrow handles these processes,and attacks are evaluated using the hyper *** labels are assigned based on the k-medoids attack clustering through label assignment through a transductive deep learning *** proposed model uses the CICIDS dataset for attack detection and *** developed framework EsECC_SDN’s performance is compared to that of other classifiers such as AdaBoost,Regression,and Decision *** performance of the proposed EsECC_SDN exhibits∼3%improved performance compared with conventional techniques.

关键词： MANET security classifier cryptography attack escrow ECC

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Hyper-Relational Knowledge Representation Learning with Multi-Hypergraph Disentanglement 25

Hyper-Relational Knowledge Representation Learning with Mult...

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34th ACM Web Conference, WWW 2025

作者： Li, Jiecheng Luo, Xudong Lu, Guangquan Zhang, Shichao School of Computer Science and Engineering Key Lab of Education Blockchain and Intelligent Technology of the Ministry of Education Guangxi Key Lab of Multi-source Information Mining & Security Guangxi Normal University Guilin China

ISBN: (纸本)9798400712746

Hyper-relational knowledge graphs (HKGs) extend the traditional triplet-based knowledge graph by adding qualifiers to the relationships, making HKGs particularly useful for tasks that require more profound understanding and inference from relationships between entities. However, existing hyper-relational knowledge representation learning methods (HKRL) focus on direct neighbourhood information of entities only by neglecting the relational similarity of the main triple in hyper-relational facts and the attribute details in the qualifiers. In addition, few works extract common and private information across multiple views to minimize noise and interference. This paper proposes a multi-hypergraph disentanglement method for HKRL to address the above issues. Specifically, we first construct four hypergraphs to mine and utilise the inherent structure information of HKGs, and then propose to extract common representations among hypergraphs and private representations within individual hypergraphs to mine the semantic information and the task-relevant information, respectively. Experiment results on four real datasets demonstrate the effectiveness of the proposed method compared to SOTA methods in link prediction tasks on HKGs. © 2025 Copyright held by the owner/author(s).

关键词： hyper-relational knowledge graph

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Uplink Control information Based Clustered Access and Discontinuous Reception Management for mMTC

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IEEE Communications Letters 2025年第6期29卷 1380-1384页

作者： Wang, Shao-Di Wang, Chang-Long Wang, Hui-Ming Zhou, Feng Leung, Victor C. M. Xidian University Key Laboratory of Electronic Information Countermeasure and Simulation Technology of Ministry of Education Xi’an710071 China Xi’an Jiaotong University School of Information and Communication Engineering Ministry of Education Key Lab for Intelligent Networks and Network Security Xi’an710049 China Shenzhen MSU BIT University Artificial Intelligence Research Institute Shenzhen518172 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada

Uplink control information (UCI) and discontinuous reception (DRX) play important roles for massive machine type communication (mMTC). Despite their standalone significance, a conspicuous gap exists in comprehensively exploring the synergy between UCI and DRX concerning both energy conservation and latency reduction for mMTC. In this letter, we propose a UCI-based clustered access and DRX management (UCADM) scheme that strikes a balance between these two critical performance metrics, reducing energy usage while preserving low latency in uplink-downlink interaction for mMTC. Specifically, we present a strictly feasible solution for the proposed UCADM scheme by using the spectral radius and the constraint projection, which can effectively solve DRX switching operations as indicated by scheduling information from UCI. Simulation results confirm the effectiveness of our scheme. © 1997-2012 IEEE.

关键词： information management

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An optimized framework for implementation of smart waste collection and management system in smart cities using IoT based deep learning approach

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International Journal of information technology (Singapore) 2024年第8期16卷 5033-5040页

作者： William, P. Patil, Jaikumar M. Panda, Sunita Venugopal, Anita Vidyullatha, Pellakuri Kumar, Nellore Manoj Jandwani, Aman Department of Information Technology Sanjivani College of Engineering SPPU Department of Computer Science and Engineering Shri Sant Gajanan Maharaj College of Engineering SGBAU Department of Electrical Electronics and Communication Engineering GITAM School of Technology IT Unit Dhofar University Department: Computer Science and Engineering Koneru Lakshmaiah Education Foundation Department of Mathematics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Research Task

Nation’s rapid urbanization growth and economic development, waste generation has significantly increased. An important environmental concern on a global scale is waste management. In a majority of the world’s nations, including India’s metropolitan centers, organic waste management is a major problem. Therefore, it is necessary to create a productive system that will either eliminate or significantly decrease this issue. It will assist us in effectively maintaining a hygienic environment, and natural atmosphere. The implementation of smart cities requires an effective garbage collection system. In the given paper, we propose a smart waste collection and management (SWCM) system centered on the Internet of Things (IoT) architecture. A deep learning (DL) approach for classifying waste is being used. The proposed framework includes the use of smart bins for the collection of garbage, the categorization of that waste into several groups, and the management of smart waste. For this study, data from smart garbage cans were gathered. The data was normalized during preprocessing. The efficacy of the proposed system is assessed and compared with conventional as well as existing recent studies based on performance metrics such as classification accuracy (%) and power consumption (%). The results of the study reveal that the proposed approach is impactful in the smart collection and management of waste. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： And hygienic environment Deep learning Internet of things (IoT) Smart cities Smart waste

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