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World Water Policy 2024年第1期10卷 244-279页

作者： Kumar, Rajneesh Mahapatra, Rajendra Prasad Department of Computer Science and Engineering SRM Institute of Science and Technology Uttar Pradesh Ghaziabad India

Crop yield prediction has gained major potential for global food production. Predicting crop yields based on specific parameters like soil, environment, crop, and water has been an interesting research topic in recent decades. To accurately predict crop yields, measuring the severities of natural calamities including water level is mainly required. However, the existing studies failed to predict crop yields accurately because of various issues like overfitting problems, difficulty in training, inability to handle large data, and reduced learning capability. Thus, the proposed study develops an efficient mechanism for accurately predicting crop yields by analyzing several natural calamities. Here, the input samples are initially pre-processed to remove unwanted noises using data normalization and standardization. To enhance the performance of crop yield prediction, natural calamities are computed by using an Extreme Gradient Boosting (XGBoost) model based on parameters like the Palmer Drought Severity Index (PDSI), Severe Hail Index (SHI), and Storm Severity Index (SSI). Also, the hyperparameters of XGBoost model are tuned by utilizing Sheep Flock Optimization Algorithm (SFOA). Finally, the crop yield is predicted by proposing a new one-dimensional convolutional gated recurrent unit neural network (1D-CGRU). The proposed classifier predicts the crop yields with reduced error rates like mean square error (MSE) of 0.4363, root mean square error (RMSE) of 0.1904, normalized root mean squared error (NRMSE) of 0.00101, mean absolute error (MAE) of 0.2437, and R-squared (R²) of.2756. Also, the significant findings of the proposed study positively indicate that this study can be applicable to real-time agricultural practices and is highly suitable for water quality predictions. Also, it can assist farmers and farming businesses in predicting the yield of crops in a specific season when to harvest and crop a plant for attaining improved crop yields. © 2024 Policy

关键词： crop yield prediction deep learning network extreme gradient boosting Palmer Drought Severity Index Severe Hail Index Storm Severity Index

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Hybrid RNN Based Text Classification Model for Unstructured Data

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SN computer science 2024年第6期5卷 726页

作者： Sunagar, Pramod Sowmya, B.J. Pruthviraja, Dayananda Supreeth, S. Mathew, Jimpson Rohith, S. Shruthi, G. Cloudthat Technologies Department of Artificial Intelligence and Data Science Ramaiah Institute of Technology Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education School of Computer Science and Engineering REVA University Department of Computer Science and Engineering Indian Institute of Technology Patna Department of ECE Nagarjuna College of Engineering and Technology

The volume of social media posts is on the rise as the number of social media users expands. It is imperative that these data be analyzed using cutting-edge algorithms. This goal is handled by the many techniques used in text categorization. There are a variety of text categorization techniques available, ranging from machine learning to deep learning. Numerical crunching has become easier with less processing time since the emergence of high-end computer facilities. This has led to the development of sophisticated network architectures that can be trained to achieve higher precision and recall. The performance of neural network models which was evaluated by the F1 score is affected by cumulative performance in precision and recall. The current study intends to analyze and compare the performance of the neural network proposed, A Hybrid RNN model that has two layers of BiLSTM and two layers of GRU to that of previous hybrid models. GloVE dataset is used to train the models and their accuracy, precision, recall, and F1 score are used to assess performance. Except for the RNN + GRU model, the RNN + BILSTM + GRU model has a precision of 0.767, a recall of 0.759, and an F1-score of 0.7585. This hybrid model outperforms the others. © The Author(s) 2024.

关键词： Bidirectional long-short-term memory (BiLSTM) Gated recurrent units (GRU) Recurrent neural networks (RNN) Region-based convolutional neural networks (RCNN) Text classification

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An Efficient Unsupervised Learning Approach for Detecting Anomaly in Cloud

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computer Systems science & engineering 2023年第4期45卷 149-166页

作者： P.Sherubha S.P.Sasirekha A.Dinesh Kumar Anguraj J.Vakula Rani Raju Anitha S.Phani Praveen R.Hariharan Krishnan Department of Information Technology Karpagam College of EngineeringCoimbatoreTamilnaduIndia Department of Computer Science and Engineering Karpagam Academy of Higher EducationCoimbatoreTamilnaduIndia Department of Computer Science and Engineering Koneru Lakshmaiah Education FoundationAndhra PradeshIndia Department of MCA CMR Institute of TechnologyBengaluruKarnatakaIndia Department of Computer Science and Engineering Prasad V.Potluri Siddhartha Institute of TechnologyAndhra PradeshIndia Department of Computer Science and Engineering Residency CollegeChennaiIndia

The Cloud system shows its growing functionalities in various industrial *** safety towards data transfer seems to be a threat where Network Intrusion Detection System(NIDS)is measured as an essential element to fulfill ***,Machine Learning(ML)approaches have been used for the construction of intellectual *** IDS are based on ML techniques either as unsupervised or *** supervised learning,NIDS is based on labeled data where it reduces the efficiency of the reduced model to identify attack ***,the unsupervised model fails to provide a satisfactory ***,to boost the functionality of unsupervised learning,an effectual auto-encoder is applied for feature selection to select good ***,the Naïve Bayes classifier is used for classification *** approach exposes the finest generalization ability to train the *** unlabelled data is also used for adoption towards data ***,redundant and noisy samples over the dataset are *** validate the robustness and efficiency of NIDS,the anticipated model is tested over the NSL-KDD *** experimental outcomes demonstrate that the anticipated approach attains superior accuracy with 93%,which is higher compared to J48,AB tree,Random Forest(RF),Regression Tree(RT),Multi-Layer Perceptrons(MLP),Support Vector Machine(SVM),and ***,False Alarm Rate(FAR)and True Positive Rate(TPR)of Naive Bayes(NB)is 0.3 and 0.99,*** compared to prevailing techniques,the anticipated approach also delivers promising outcomes.

关键词： Network intrusion detection system feature selection auto-encoder support vector machine(SVM) anomaly

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A Meticulous Range of Forensics Tech Advancement in the field of IoT, IIoT and MIoT 5

A Meticulous Range of Forensics Tech Advancement in the fiel...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Matta, Priya Kamalraj, R. Saxena, Kumud Tula's Institute Department of Computer Science and Engineering Dehradun India Department of Computer Science and Information Technology Bangalore India Noida Institute of Engineering and Technology Department of Computer Science Engineering Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350383522

Wide application of millions of connected, intelligent, and flexible gadgets in critical infrastructures like medical facilities, public transit, sustainability, and home automation has been driven by latest expansions in intelligence technology and hardware. Clients profit from consistency and accessibility when data is transmitted across the internet deprived of the need for any kind of person to person or person-to-computer communication however this additionally creates new prospects for hackers and increases a number of complicated and challenging issues for the area of online forensics. Regardless of the fact that IoT records may be a valuable evidence source, forensics professionals must deal with a wide range of issues, starting the massive diversity of IoT devices and unrecognized setups to the multi-tenant platforms of cloud and the ensuing multijurisdictional lawsuits. Encryption at all levels, which compromises users' rights regarding confidentiality for the benefit of forensics study, is another difficulty. The aim of this work is to recognize and address the issues connected through the complex procedure of IoT based inquiries, particularly the privacy and security problems. The study also examines the application of IoT forensics across different industries. © 2024 IEEE.

关键词： IoT (Internet of things) IoT forensics privacy issues and digital forensics Security issues

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EdgeFedNet: Edge Server Based Communication and Computation Efficient Federated Learning

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SN computer science 2025年第3期6卷 1-10页

作者： Gowtham, L. Annappa, B. Sachin, D.N. Department of Computer Science and Engineering National Institute of Technology Karnataka Surathkal 575025 India

Federated learning (FL) is a new learning framework for training machine learning and deep learning models using data spread over several edge devices. Edge devices like mobile phones and IoT devices have constraints on computational power, resources, and connectivity for training the model. Also, many model parameters will be exchanged while training the model, leading to high communication costs in FL when bandwidth is limited. This paper presents EdgeFedNet a new form of training the model in FL. The proposed method reduces the model parameters by pruning the model and restricts the communication between clients and the cloud server by implementing edge servers. An edge server near a set of clients forms a cluster and coordinates the FL training. The aggregated model updates from all the edge servers are sent to the cloud server, restricting the frequent communication between the clients and the cloud server. The experimental results exhibit a remarkable reduction in the number model parameters (up to 54%) and effectively address the communication overhead by reducing communication rounds by 59% compared to the baseline approach FedAvg. These enhancements are achieved without sacrificing accuracy, presenting promising implications for more efficient model parameter pruning and communication strategies. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Communication Computation Edge computing Federated learning

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Modelling a Learning-Based Dynamic Tree Routing Model for Wireless Mesh Access Networks

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computer Systems science & engineering 2023年第2期44卷 1531-1549页

作者： N.Krishnammal C.Kalaiarasan A.Bharathi Department of Computer Science and Engineering Sri Shakthi Institute of Engineering and TechnologyCoimbatoreIndia School of Engineering Presidency UniversityBengaluruIndia Department of Information Technology Bannari Amman Institute of TechnologySathyamangalamIndia

Link asymmetry in wireless mesh access networks(WMAN)of Mobile ad-hoc Networks(MANETs)is due mesh routers’transmission *** is depicted as signiﬁcant research challenges that pose during the design of network protocol in wireless *** on the extensive review,it is noted that the substantial link percentage is symmetric,i.e.,many links are *** is identiﬁed that the synchronous acknowledgement reliability is higher than the asynchronous ***,the process of establishing bidirectional link quality through asynchronous beacons underrates the link reliability of asym-metric *** paves the way to exploit an investigation on asymmetric links to enhance network functions through link ***,a novel Learning-based Dynamic Tree routing(LDTR)model is proposed to improve network performance and *** the evaluation of delay measures,asymmetric link,interference,probability of transmission failure is *** proportion of energy consumed is used for monitoring energy conditions based on the total energy *** learning model is a productive way for resolving the routing issues over the network model during *** asymmetric path is chosen to achieve exploitation and exploration *** learning-based Dynamic Tree routing model is utilized to resolve the multi-objective routing ***,the simulation is done with MATLAB 2020a simulation environment and path with energy-efﬁciency and lesser E2E delay is evaluated and compared with existing approaches like the Dyna-Q-network model(DQN),asymmetric MAC model(AMAC),and cooperative asymmetric MAC model(CAMAC)*** simulation outcomes demonstrate that the anticipated LDTR model attains superior network performance compared to *** average energy consump-tion is 250 J,packet energy consumption is 6.5 J,PRR is 50 bits/sec,95%PDR,average delay percentage is 20%.

关键词： Wireless mesh access networks mobile ad-hoc network reinforcement learning multi-objective constraint asymmetric link

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Developing an Esports Assistant System for First Person Shooting Game Based on Multimodal Learning 14th

Developing an Esports Assistant System for First Person Shoo...

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14th International Conference on Frontier Computing, FC 2024

作者： Chiu, Wei-Hsiang Hung, Jason C. Ma, Hao-Shang Huang, Chun-Hong Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taichung Taiwan Department of Computer Information and Network Engineering Lunghwa University of Science and Technology Taoyuan City Taiwan

ISBN: (纸本)9789819623945

We plan to develop a specialized training system to enhance the competitive skills of players in the first-person shooter game "Valorant", aiming to improve their abilities and tactical understanding within the game. We will adopt a multimodal image-text fusion technique to enhance the model’s learning of sample similarity. Specifically, we will use the cross-entropy function to maximize the diagonal values of the similarity matrix, thereby capturing the features of similar samples and reducing the similarity between dissimilar samples. Through this process, we ensure that the model can map the same entity across different modalities into similar embedding representations, while distinguishing unrelated samples. Our goal is to find a way of embedding representation during the learning process that enables the model to better understand and represent multimodal data. We will utilize the YOLOv7 model for object detection to identify destructible skills. Based on the results of object detection, we will generate relevant textual descriptions for each object. This will enable novice players to quickly understand the function of diverse and complex skills and take appropriate actions when encountering them. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Object detection

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Atrous Convolution-Based Fusion Attention Mechanism for Brain Tumor Segmentation

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IEEE Access 2025年 13卷 92611-92621页

作者： Jadhav, Abhishek Rasool, Akhtar Gyanchandani, Manasi Maulana Azad National Institute of Technology Department of Computer Science and Engineering Bhopal India

Accurate medical image segmentation is pivotal for advanced diagnostic and therapeutic planning, especially for intricate tasks such as brain tumor delineation. However, existing segmentation methods often struggle with challenges such as heterogeneous tumor morphologies, diffuse boundaries, and low-contrast regions, limiting their reliability in complex medical imaging scenarios. In this work, we introduce an Atrous Convolution-Based Fusion Attention Mechanism, a novel framework that combines local and global attention through an innovative fusion block. Local attention leverages atrous convolutions to capture fine-grained spatial features at multiple scales, while global attention extracts long-range contextual dependencies across the image. These complementary features are dynamically fused via a novel fusion block, ensuring balanced representation and robust feature aggregation for precise segmentation. The proposed model is rigorously evaluated on the BraTS 2019, 2020, and 2023 datasets, demonstrating consistent and significant improvements across key segmentation metrics compared to baseline models. Quantitatively, the model achieves an absolute average increase of 0.2522, 0.1873, and 0.1934 in the Dice Coefficient, and 0.3071, 0.2365, and 0.2369 in the Intersection over Union (IoU) for enhancing tumor, tumor core, and whole tumor, respectively, across all datasets. The results highlight robustness and adaptability of the proposed architecture, underlining its potential to address critical challenges in brain tumor segmentation and advance the state-of-the-art in medical image analysis. © 2013 IEEE.

关键词： Image segmentation

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SHBF: a secure and scalable hybrid blockchain framework for resolving trilemma challenges

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International Journal of Information technology (Singapore) 2024年第6期16卷 3879-3890页

作者： Al-Kafi, G. M. Abdullah Ali, Golam Faiza, Jannateen Tajree Pal, Koli Rani Reno, Saha Department of Computer Science and Engineering Ahsanullah University of Science and Technology (AUST) Dhaka 1208 Bangladesh

In the rapidly evolving landscape of blockchain consensus algorithms, existing methods encounter limitations in terms of security, time efficiency, and versatility. This research addresses these challenges through the proposal and implementation of a novel consensus algorithm designed to overcome the blockchain trilemma, ensuring a harmonious balance between decentralization, security, and scalability. Implemented as a federated Byzantine agreement system, this consensus incorporates the strengths of various consensus models, including Honey Badger BFT (HBFT), Proof of Authority (PoA), Stellar Consensus Protocol (SCP) and Practical Byzantine Fault Tolerance (PBFT). The algorithm integrates adaptive validation mechanisms to accommodate both private and public blockchain environments, enhancing its versatility. Through a comprehensive fault tolerance and identity management system, this consensus ensures robustness against malicious nodes, promoting a secure and reliable consensus process. The proposed consensus algorithm optimizes time efficiency by streamlining the consensus process, drawing inspiration from the nomination and ballot protocol of SCP. The analysis of outcomes shows how effective this consensus is at performing transactions with a throughput of 1627 transactions per second (TPS) and a latency of just 45 milliseconds, significantly reducing the required block time for securing 90.9% protection against double spending, maintaining a low fork probability of 10%, and achieving a decentralization measure of 8.094 out of 10. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Blockchain Consensus Decentralization Scalability Security Trilemma

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A deep decentralized privacy-preservation framework for online social networks

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Blockchain(Research and Applications) 2024年第4期5卷 163-174页

作者： Samuel Akwasi Frimpong Mu Han Emmanuel Kwame Effah Joseph Kwame Adjei Isaac Hanson Percy Brown School of Computer Science and Communication Engineering Jiangsu UniversityZhenjiang212013China Department of Computer Engineering Ghana Communication Technology UniversityAccraPMB 100Ghana Department of Electrical&Electronic Engineering Ghana Communication Technology UniversityAccraPMB 100Ghana Department of Computer Science and Information Systems Ashesi UniversityAccra3042Ghana Department of Telecommunication Engineering Ghana Communication Technology UniversityAccraPMB 100Ghana

This paper addresses the critical challenge of privacy in Online Social Networks(OSNs),where centralized designs compromise user *** propose a novel privacy-preservation framework that integrates blockchain technology with deep learning to overcome these *** methodology employs a two-tier architecture:the first tier uses an elitism-enhanced Particle Swarm Optimization and Gravitational Search Algorithm(ePSOGSA)for optimizing feature selection,while the second tier employs an enhanced Non-symmetric Deep Autoencoder(e-NDAE)for anomaly ***,a blockchain network secures users’data via smart contracts,ensuring robust data *** tested on the NSL-KDD dataset,our framework achieves 98.79%accuracy,a 10%false alarm rate,and a 98.99%detection rate,surpassing existing *** integration of blockchain and deep learning not only enhances privacy protection in OSNs but also offers a scalable model for other applications requiring robust security measures.

关键词： Preprocessing Privacy-preservation Blockchain Deep learning Online social network

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