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A Novel Fuzzy Marine White Shark Optimization Based Efficient Routing and Enhancing Network Lifetime in MANET

WIRELESS PERSONAL COMMUNICATIONS 2023年第4期132卷 2363-2385页

作者： Devi, K. Lalitha Bhat, C. Rohith Devi, K. Lalitha KSR College of Engineering Tiruchengode Namakkal India Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai Tamil Nadu 602105 India Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India

A mobile ad hoc network (MANET) is an independent wireless temporary network established by employing a set of mobile nodes (i.e. laptops, smartphones, iPods, etc.) appropriate for the environment in which the network infrastructures are not fixed. The most common problems faced by MANET are energy efficiency, high energy consumption, low network lifetime as well as high traffic overhead which create an impact on overall network topology. Hence, it is necessary to provide an energy-effective CH election to take steps against such issues. Therefore, this paper proposes a novel model to enhance the network lifetime and energy efficiency by performing a routing strategy in MANET. In this paper, an optimal CH is selected by proposing a novel Fuzzy Marine White Shark optimization (FMWSO) algorithm which is obtained by integrating fuzzy operation with two optimization algorithms namely the marine predator algorithm and white shark optimizer. The proposed approach comprises three diverse stages namely Generation of data, Cluster Generation and CH selection. A novel FMWSO algorithm is proposed in such a way to determine the CH selection in MANET thereby enhancing the network topology, network lifetime and minimizing the overhead rate, and energy consumption. Finally, the performance of the proposed FMWSO approach is compared with various other existing techniques to determine the effectiveness of the system. The proposed FMWSO approach consumes minimum energy of 0.62 mJ which is lower than other approaches.

关键词： MANET Cluster head Fuzzy Marine White Shark Cluster generation Network lifetime Energy consumption

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Principle of subtraction ghost imaging in scattering medium

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Chinese Physics B 2023年第6期32卷 250-254页

作者：付芹白艳锋谭威黄贤伟南苏琴傅喜泉 College of Computer Science and Electronic Engineering Hunan UniversityChangsha 410082China School of Computer and Information Engineering Hunan University of Technology and BusinessChangsha 410205China

Scattering medium in light path will cause distortion of the light field,resulting in poor signal-to-noise ratio(SNR)of ghost *** disturbance is usually eliminated by the method of *** deduce the intensity fluctuation correlation function of the ghost imaging with the disturbance of the scattering medium,which proves that the ghost image consists of two correlated results:the image of scattering medium and the target *** effect of the scattering medium can be eliminated by subtracting the correlated result between the light field after the scattering medium and the reference light from ghost image,which verifies the theoretical *** research may provide a new idea of ghost imaging in harsh environment.

关键词： ghost imaging image reconstruction techniques scattering medium

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Drought Prediction in Agriculture with Support Vector Neural Networks: Enhancing Accuracy 8th

Drought Prediction in Agriculture with Support Vector Neural...

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8th International Conference on Internet of Things and Connected Technologies, ICIoTCT 2023

作者： Patil, Mithun B. Adhatrao, Ashlesha S. Computer Science and Engineering N. K. Orchid College of Engineering and Technology Maharashtra Solapur India Computer Science and Engineering N K Orchid College of Engineering and Technology Solapur India

ISBN: (纸本)9789819757855

Accurate drought prediction plays a pivotal role in water resource management and agricultural planning. This study delves into the realm of machine learning algorithms to enhance the accuracy of such predictions. The core focus centers on the integration of support vector neural networks (SVNNs) as a means to predict drought occurrences, leveraging data from diverse sources including meteorological, hydrological, and remote sensing. The unique characteristic of SVNNs lies in their amalgamation of support vector machines and neural networks, allowing for the nuanced capture of intricate data relationships. The refinement of feature engineering strategies is instrumental in optimizing the predictive models. These strategies address the challenges posed by imbalanced datasets through the implementation of resampling techniques, paired with careful selection of evaluation metrics. The performance evaluation of SVNNs is conducted through a rigorous assessment against historical drought events, further accentuated by a comparison against conventional methods. To expand the scope, the integration of remote sensing data enriches the models with comprehensive spatiotemporal insights, enhancing their predictive capabilities. Moreover, the research extends its exploration into ensemble techniques and hybrid models, showcasing the versatile potential of machine learning in the domain of drought prediction. The findings of this study unequivocally demonstrate the superiority of the projected method when compared to existing methodologies, particularly in terms of accuracy, precision, and F1-score. The novel drought prediction models put forward in this research hold the potential for unprecedented advancements, significantly enhancing the efficacy of drought mitigation strategies. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Prediction models

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AMCB: enhancing the authentication process with blockchain integrated with PUF and machine learning

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Multimedia Tools and Applications 2025年第8期84卷 4809-4835页

作者： Al-Ghuraybi, Hind A. AlZain, Mohammed A. Soh, Ben Department of Information Technology College of Computers and Information Technology Taif University Taif21944 Saudi Arabia Department of Computer Science and Computer Engineering La Trobe University Bundoora Australia

The rapid advancement of technology has given rise to medical cyber-physical systems (MCPS), a subset of cyber-physical systems (CPS) specifically tailored for patient care and healthcare providers. MCPS generate substantial volumes of big data, posing challenges in terms of processing, storage, and safeguarding against unauthorized access. This paper introduces the Authenticated Medical Cyber-Physical Blockchain (AMCB) model within MCPS, leveraging Blockchain technology, machine learning, and physically unclonable functions (PUF) to fortify the authentication process, particularly for accessing electronic health records (EHR) stored on cloud servers. The model also involves analyzing data from authenticated devices to ensure its integrity. Experiments on the AMCB model evaluate its efficiency across four layers. In the physical layer, PUF keys are extracted using a built Static Random-Access Memory (SRAM) circuit and stored in a simulated Blockchain, yielding a Hamming Distance standard deviation of 6.60%. The inspection layer analyzes data from authenticated devices using machine learning, with the Random Forest achieving the highest accuracy at 99.21%. In the application layer, centered on user authentication, the average transaction time is 36.15 MS. Overall, the AMCB model requires an average of 49.82 ms to execute operations across all layers. These findings underscore the effectiveness of the proposed model in securing EHR in the dynamic landscape of MCPS. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Authentication

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PSO-DBNet for Peak-to-Average Power Ratio Reduction Using Deep Belief Network

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computer Systems science & engineering 2023年第5期45卷 1483-1493页

作者： A.Jameer Basha M.Ramya Devi S.Lokesh P.Sivaranjani D.Mansoor Hussain Venkat Padhy Department of Computer Science and Engineering Hindusthan Institute of TechnologyCoimbatore641032India Department of Computer Science and Engineering Hindusthan College of Engineering and TechnologyCoimbatore641032India Department of Electronics and Communication Engineering Kongu Engineering CollegeErode638060India Department of Computer Science and Engineering Sri Krishna College of Engineering and TechnologyCoimbatore641008India School of Computing Science&Engineering VIT Bhopal UniversityBhopal466114India

Data transmission through a wireless network has faced various signal problems in the past *** orthogonal frequency division multiplexing(OFDM)technique is widely accepted in multiple data transfer patterns at various frequency bands.A recent wireless communication network uses OFDM in longterm evolution(LTE)and 5G,among *** main problem faced by 5G wireless OFDM is distortion of transmission signals in the *** transmission loss is called peak-to-average power ratio(PAPR).This wireless signal distortion can be reduced using various *** study uses machine learning-based algorithm to solve the problem of PAPR in 5G wireless *** transmit sequence(PTS)helps in the fast transfer of data in wireless *** is merged with deep belief neural network(DBNet)for the efficient processing of signals in wireless 5G *** indicates that the proposed system outperforms other existing ***,PAPR reduction in OFDM by DBNet is optimized with the help of an evolutionary algorithm called particle swarm ***,the specified design supports in improving the proposed PAPR reduction architecture.

关键词： 5G wireless network orthogonal frequency division multiplexing signal distortion peak to average power ratio partial transmit sequence deep belief network

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Efficient Structure Slimming for Spiking Neural Networks

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第8期5卷 3823-3831页

作者： Li, Yaxin Fang, Xuanye Gao, Yuyuan Zhou, Dongdong Shen, Jiangrong Liu, Jian K. Pan, Gang Xu, Qi Dalian University of Technology School of Computer Science and Technology Dalian116024 China Zhejiang University College of Computer Science and Technology Zhejiang310027 China University of Birmingham College of Engineering and Physical Sciences BirminghamB152TT United Kingdom

Spiking neural networks (SNNs) are deeply inspired by biological neural information systems. Compared to convolutional neural networks (CNNs), SNNs are low power consumption because of their spike based information processing mechanism. However, most of the current structures of SNNs are fully connected or converted from deep CNNs which poses redundancy connections. While the structure and topology in human brain systems are sparse and efficient. This article aims at taking full advantage of sparse structure and low power consumption which lie in human brain and proposed efficient structure slimming methods. Inspired by the development of biological neural network structures, this article designed types of structure slimming methods including neuron pruning and channel pruning. In addition to pruning, this article also considers the growth and development of the nervous system. Through iterative application of the proposed neural pruning and rewiring algorithms, experimental evaluations on Canadian Institute for Advanced Research (CIFAR)-10, CIFAR-100, and dynamic vision sensor (DVS)-gesture datasets demonstrate the effectiveness of the structure slimming methods. When the parameter count is reduced to only about 10% of the original, the performance decreases by less than 1%. © 2020 IEEE.

关键词： Energy efficiency

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Sentiment Classification for Depression Detection: Integrating Capsule Networks with CNNs on Review Data

Sentiment Classification for Depression Detection: Integrati...

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2025 International Conference on Emerging Technologies for Intelligent Systems, ETIS 2025

作者： Sagili, Sreenivas Reddy Shibi, B. Rvs, Praveen Anjana, P. Rochester Institute of Technology Department of Computer Science United States Sona College of Technology Computer Science and Engineering Tamil Nadu Salem India LTI Mindtree Digital Engineering India Awh Engineering College Computer Science and Engineering Kerala India

ISBN: (纸本)9798331507541

This research is focused on the analysis of how Capsule Networks should be combined with Convolutional Neural Networks (CNNs) for improving the sentiment classification with the target of depression detection based on the data offered by the reviews. Other methodologies of analysing the sentiment of a phrase may encounter disability in the process of analysing texts in natural language following depression since they do not analyse the text deeply enough to consider the context. The proposed method intends to enhance accuracy and resistance when compared to state-of-the-art techniques by combining the attributes of Capsule Networks, that effectively maintain the spatial hierarchy and the connections within the data, with CNNs' feature extraction ability. The proposed hybrid model helps overcome disadvantages of the plain sentiment analysis because it identifies intricate patterns and dependencies in the review data and enhances the depressive sentiments' detection. This paper assesses the propriety of the integrated Capsule-CNN through an evaluation on review datasets in relation to its ability in the successful differentiate between depressive and non-depressive sentiments. The findings reveal a considerable increase in method accuracy and present a leap of progress in the field of automatic depression detection, which in turn helps to develop more accurate and efficient tools for monitoring patients' mental status. © 2025 IEEE.

关键词： Emotion Recognition

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An efficient integrity based multi-user blockchain framework for heterogeneous supply chain management applications

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International Journal of computers and Applications 2023年第4期45卷 337-351页

作者： Karumanchi, Mani Deep Sheeba, J.I. Devaneyan, S. Pradeep Department of Computer Science and Engineering Bapatla Engineering College Bapatla India Department of Computer Science and Engineering Puducherry Technological University Puducherry India Department of Mechanical Engineering Sri Venkateshwaraa College of Engineering and Technology Puducherry India

Most of the traditional cloud-based applications are insecure and difficult to compute the data integrity with variable hash size on heterogeneous supply chain datasets. Also, cloud storage systems are independent of integrity computational and data security due to structured data and computational memory. As the size of the cloud data files is increasing in the public and private cloud servers, it is difficult to provide strong data security due to file format and high dimensionality. The computational time and storage space of the conventional attribute-based encryption and decryption models are high during the data integrity verification in the traditional blockchain frameworks. This paper implements a hybrid variable size data integrity algorithm on the heterogeneous cloud supply chain data files for the strong data encryption and decryption process. This work implements an optimized blockchain framework using the advanced heterogeneous integrity computation approach and integrity policy-based attribute encryption and decryption approach for better cloud data security. Experimental results proved that the proposed integrity-based encryption model has better efficiency than the traditional integrity-based encryption frameworks on cloud heterogeneous data types. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Blockchain

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Fraud Detection in Banking using the Kaggle Credit Card Dataset and XGBoost Model 5

Fraud Detection in Banking using the Kaggle Credit Card Data...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Ango, Rithika Masih, Raj Kumar Reddy, C Kishor Kumar Shuaib, Mohammed Monika Singh, T. Alam, Shadab Stanley College of Engineering & Technology for Women Department of Computer Science and Engineering Hyderabad India Jazan University College of Engineering and Computer Science Department of Computer Science Jazan Saudi Arabia

ISBN: (纸本)9798331518097

The threat posed by credit card fraud, and by extension, online banking, continues to grow with the convenience brought forth by online banking services. Many financial institutions and customers stand at great risk because of this reality. The purpose of the paper is to enhance the security of banking systems through advanced technologies - machine learning. The research employed the well-known dataset from Kaggle Credit Card, which contains numerous records of transactions labeled as either fraud or not, thus presenting an aspect similar to real life imbalanced data. The main focus in the present work is the XGBoost model which involves one of the ensemble learning methods, which is characterized by a high level of precision and fast execution times. As a result of hyperparameter optimization and feature engineering, the paper also works on the performance of the algorithm improving its ability to detect fraudulent transactions in a sea of normal transactions. The results obtained from the experiments show that the XGBoost model is not only proven to be more accurate than earlier approaches to fraud detection, but also greatly reduces the number of false positives. This study not only contributes to the existing literature on detecting financial fraud but also represents the ways in which finance can benefit from machine learning to design secure systems within banking operations. The metrics in which the model suggested outperformed the available approaches are, the accuracy is 98.60%, error rate is 2.10% while its precision reached 95.40, recall of 92.30, F1 score 93.20 and AUC-ROC 99.70. This study not only contributes to the existing literature on detecting financial fraud but also represents the ways in which finance can benefit from machine learning to design secure systems within banking operations. © 2024 IEEE.

关键词： Banking

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An Autoencoder-BiLSTM framework for classifying multiple types of lung diseases from CXR images

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Multimedia Tools and Applications 2024年第38期83卷 86091-86120页

作者： Ankayarkanni, B. Sangeetha, P. Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Jeppiaar Nagar Chennai600 119 India Rajiv Gandhi College of Engineering Research and Technology Maharashtra Chandrapur442403 India

Millions of people die from lung illness each year as a result of its rise in recent years. CXR imaging is one of the most widely used and reasonably priced diagnostic techniques for the diagnosis of many illnesses. Unfortunately, even for seasoned radiologists, accurately diagnosing sickness from Chest X-Rays (CXR) samples is challenging. To combat the pandemic, a reliable, affordable, and efficient way to diagnose lung disease has become essential. Consequently, a unique optimized Auto Encod-BI Long-Short Term Memory (Bi-LSTM) model is proposed in this research work. Pre-processing, segmentation, feature extraction, and multiple types of lung illness diagnosis are the four main stages of the suggested model. First, Laplacian filtering and Contrast Limited Adaptive Histogram Equalization (CLAHE) are used to pre-process the gathered CXR pictures. Next, the Region of Interest (ROI) from the previously processed images are recognized by means of the newly enhanced MobileNetV2. The new Self-Improved Slime Mould Algorithm (SI-SMA) is used to fine-tune the hyper-parameters of MobileNetV2 in order to precisely identify the afflicted locations. Based on the phenomenon of slime mould oscillation, the conventional Slime Mould Algorithm (SMA) model has been modified with the creation of the SI-SMA model. Next, characteristics like the Local Binary Pattern (LBP) and Histogram of Oriented Gradient (HOG) are taken out. Finally, a unique AutoEncod-BiLSTM Framework—which is divided into three categories—is shown to automate the process of identifying illnesses in CXR pictures: pneumonia, COVID-19, and normal. The autoencoder and Bi-LSTM are combined to create the suggested AutoEncod-BiLSTM model. The retrieved features are used to train the AutoEncod-BiLSTM Framework. Moreover, the proposed model enhanced the disease detection efficiency than the existing models and the disease detection accuracy of the proposed model is about 99.1%. Furthermore, the suggested model attains better

关键词： Equalizers

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