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Energy-Efficient Routing Protocol with Multi-Hop Fuzzy Logic for Wireless Networks

Intelligent Automation & Soft Computing 2023年第5期36卷 2457-2471页

作者： J.Gobinath S.Hemajothi J.S.Leena Jasmine Department of Information and Communication Engineering Anna UniversityChennai600025India Department of Electronics and Communication Engineering Vel Tech High Tech Dr.Rangarajan Dr.Sakunthala Engineering CollegeChennai600062India Department of Computer Science and Engineering Velammal Engineering CollegeChennai600066India

A Wireless Sensor Network(WSN)becomes a newer type of real-time embedded device that can be utilized for a wide range of applications that make regular networking which appears *** the energy produc-tion of the nodes,WSN has major issues that may inﬂuence the stability of the *** a result,constructing WSN requires devising protocols and standards that make the most use of constrained capacity,especially the energy *** faces some issues with increased power utilization and an on going devel-opment due to the uneven energy usage between the *** has proven to be a more effective strategy in this *** the proposed work,a hybrid meth-od is used for reducing the energy consumption among CHs.A Fuzzy Logic-based clustering protocol FLUC(unequally clustered)and Fuzzy Clustering with Energy-Efﬁcient Routing Protocol(FCERP)are used.A Fuzzy Clustering with Energy Efﬁcient Routing Protocol(FCERP)reduces the WSN power usage and increases the lifespan of the *** has created a novel cluster-based fuzzy routing mechanism that uses a limit value to combine the clustering and multi-hop routing *** technique creates uneven groups by using fuz-zy logic with a competitive range to choose the Cluster Head(CH).The input variables include the distance of the nodes from the ground station,concentra-tions,and remaining *** proposed FLUC-FCERP reduces the power usage and improves the lifetime of the network compared with the existing algorithms.

关键词： Energy consumption lifetime wireless sensor network cluster head fuzzy logic unequally clustered fuzzy clustering energy-efﬁcient protocol

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Intelligent Decision-Making in Cybersecurity Using Machine Learning 12th

Intelligent Decision-Making in Cybersecurity Using Machine L...

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12th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2024

作者： Sakhare, Nitin N. Maruthi Shankar, B. Kalnawat, Aarti Sharma, Abha Rege, P.R. Wagh, Kapil Adhar Department of Computer Engineering BRACT’s Vishwakarma Institute of Information Technology Maharashtra Pune India Department of Electronics and Communication Engineering Sri Krishna College of Engineering and Technology Tamil Nadu Coimbatore India Pune India Electronics and Communication Engineering Arya College of Engineering Rajasthan Jaipur India Department of Computer Engineering Vishwakarma Institute of Information Technology Maharashtra Pune India Nutan Maharashtra Institute of Engineering and Technology Maharashtra Pune India

ISBN: (纸本)9789819601462

In the constantly changing field of cybersecurity, the need for advanced decision-making tools is more crucial than ever. This study explores machine learning in order to introduce an innovative method for strengthening cybersecurity protocols. The methodology involves combining Random Forests (RF) and Support Vector Machines (SVM) using the AdaBoost algorithm in an ensemble framework. The ensemble model is intricately crafted to address the complexities of intrusion detection, utilizing the combined capabilities of Random Forest’s data handling and Support Vector Machine’s pattern discrimination. AdaBoost enhances the ensemble’s predictive abilities by iteratively adjusting weights to focus more on misclassified instances. This adaptive learning process enables the model to constantly develop and adjust to new cyber threats. Validation against benchmark datasets demonstrates an impressive accuracy rate of 97.78%, highlighting the strength of the ensemble methodology. The True Positive Rate (TPR) of 98.33% demonstrates the ensemble’s effectiveness in accurately detecting and responding to potential cyber threats. The results demonstrate the superior performance of the proposed ensemble method and highlight its ability to achieve a balance between accuracy and adaptability. This research significantly advances intelligent decision-making in cybersecurity by introducing a potent ensemble approach that smoothly integrates various machine learning algorithms. The method’s proven accuracy and sensitivity make it a powerful tool in combating the increasing complexity of cyber threats. The combination of RF, SVM, and AdaBoost sets a standard for future research in strengthening digital defenses and ensuring the resilience of critical information systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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PAPR Reduction Using Advanced Partial Transmission Scheme for 5G Waveforms

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computer Systems Science & engineering 2022年第8期42卷 483-492页

作者： Arun Kumar Sumit Chakravarty SSuganya Mehedi Masud Sultan Aljahdali Department of Electronics and Communication Engineering JECRC UniversityJaipur303905INDIA Department of Electrical and Computer Engineering Kennesaw State UniversityGAUSA Electronics and Communication Engineering CMR Institute of TechnologyBangaloreINDIA Department of Computer Science College of Computers and Information TechnologyTaif University11099Saudi Arabia

The implementation of Peak Average to Power Ratio(PAPR)reduction technologies will play an important role in the regularization of Fifth Generation(5G)radio *** reduction in the advanced waveform will be the key part of designing a 5G network for different *** work introduces the simulation of an Advanced Partial Transmission Sequence(A-PTS)reduction techniques for Orthogonal Frequency Division Multiplexing(OFDM)and Filter Bank Multi-Carrier(FBMC)transmission *** the projected A-PTS,the FBMC signals are mapped into the number of sub-blocks and Inverse Fast Fourier transform(IFFT)is performed to estimate the high peak power in the time *** FBMC sub-blocks are multiplied with the phase elements to achieve an optimal PAPR value.A MATLAB 2014v simulation is used to estimate the PAPR,Bit Error Rate(BER),Error Vector Magnitude(EVM),and Modulation Error Rate(MER)performance of the proposed reduction *** simulated result reveals that the performance of the projected algorithm is better than the conventional algorithms.

关键词： PAPR PTS OFDM FBMC 5G

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Enhancing Blurred Image Processing with IoT Integration for Improved Clarity 2

Enhancing Blurred Image Processing with IoT Integration for ...

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2nd IEEE International Conference on Emerging Research in Computational Science, ICERCS 2024

作者： Tummalapalli, Geetamma Summia Parveen, H. Ramesh, S.M. Monica, P. Santhiya, B. Rajasekaran, Arun Sekar GMR Institute of Technology Department of Electronics and Communication Engineering Rajam India Sri Eshwar College of Engineering Department of Computer Science Engineering Coimbatore India KPR Institute of Engineering and Technology Department of Electronics and Communication Engineering Coimbatore India GITAM Deemed to be University Department of Electronics and Communication Engineering GST Visakhapatnam India Bannari Amman Institute of Technology Department of Information Technology Sathyamangalam India SR University Department of Electronics and Communication Engineering Warangal India

ISBN: (纸本)9798331534950

A blurred image is an image that has undergone a blurring or smoothing effect, resulting in a loss of sharpness and clarity. Blurring is a technique used in image processing to reduce noise, remove unwanted details, or create a visual effect. It involves averaging or blending neighboring pixels to create a smoother appearance. The specific processing techniques and algorithms used will depend on the nature and extent of the blur, the intended outcome, and the available software or programming tools. It's important to experiment with different methods and parameters to achieve the desired result. IoT (Internet of Things) can be used in image processing in several ways to enhance functionality, efficiency, and data processing capabilities enhances image processing by enabling real-time analysis, edge computing capabilities, integration with other IoT devices, and remote monitoring and control. These advancements facilitate applications such as object detection, image recognition, and real-time decision-making, contributing to more efficient and intelligent systems in various domains. Here is a proposed model for blurred image processing with IoT. This proposed model demonstrates the integration of IoT devices, image processing techniques, and cloud-based analytics to address the challenges of blurred image processing. It combines edge processing for real-time analysis and cloud-based processing for more complex algorithms, enabling efficient and intelligent image processing applications in various domains. © 2024 IEEE.

关键词： Edge computing

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Analysing Forecasting of Stock Prices: An Explainable AI Approach 2

Analysing Forecasting of Stock Prices: An Explainable AI App...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Kumar, Priyanshu Hota, Lopamudra Tikkiwal, Vinay Anand Kumar, Arun Department of Computer Science and Engineering National Institute of Technology Rourkela769008 India Department of Electronics and Communication Engineering Jaypee Institute of Information Technology Noida201304 India

Predicting stock prices is a well-known and significant problem. We can learn about market behaviour over time and identify trends that might not have been seen without an effective stock prediction model. Machine learning will be a useful approach to solving this issue with the increased processing capacity of computers. Behavioural economics also asserts that the investments made by investors depend on their emotions so psychological theories can also be applied to explain their behaviour and its impact on the market. Combining the analysis of these behavioural patterns with the use of historical financial data sets can result in an approach for accurate stock price predictions. The primary focus of the proposed model is on comparing different models to provide a comparative analysis of the results provided by models used in the literature. The paper provides insight into the improvisation of the current techniques and how different parameters and different error analysis techniques can be implemented. © 2024 Elsevier B.V.. All rights reserved.

关键词： Forecasting

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Intrusion detection system with walrus optimization algorithm (WOA) and BiGRU-CNN for securing IoT systems

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International Journal of information Technology (Singapore) 2025年 1-8页

作者： Appathurai, Ahilan Bachan, P. Kaushik, Ruchi Tamizhselvi, A. Jodha, Rajshree Singh, Kulwant Department of Electronics and Communication Engineering PSN College of Engineering and Technology Tamil Nadu Melathediyoor India Department of Electronics and Communication Engineering GLA University Mathura India Xavier Institute of Management and Entrepreneurship Bengaluru India St. Joseph’s College of Engineering Chennai India Computer Science and Engineering Indian Institute of Information Technology (IIIT) Kota India Electronics and Communication Engineering Yadavindra Department of Engineering (YDoE) Punjabi University Guru Kashi Campus (PUGKC) District Bathinda Talwandi Sabo India

Security has become a primary issue in IoT (Internet of Things) because of its complex environment, handling multiple devices, several connection methods, and transmission of a huge volume of data. Traditional techniques don’t manage datasets or learn features optimally, which degrades attack detection accuracy. To solve the issues, an effective Intrusion Detection System (IDS) is proposed for detecting security risks in IoT systems. The framework comprises three phases, namely data pre-processing, feature selection, and classification. Pre-processing is applied to the data gathered from the public repository. The best features are then selected from the pre-processed dataset by inputting the pre-processed data into the Walrus Optimization Algorithm (WOA). It is followed by data classification using Bidirectional Gated Recurrent Unit-Convolutional Neural Network (BiGRU-CNN). This framework can classify data into five categories, such as normal (benign), DoS (Denial of Service), Shellcode (U2R), Fuzzers (R2L), and Probe attacks. The effectiveness of the proposed framework is evaluated based on metrics such as intrusion detection rate (IDR %) and network accuracy. The results are highly satisfactory, leading to network accuracy and IDR as 98.20% and 98.15%, respectively. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： BiGRU-CNN IDR IDS IoT security Network accuracy WOA

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Enhancing visual clarity in hazy media: a comprehensive approach through preprocessing and feature fusion attention-based dehazing

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Multimedia Tools and Applications 2025年第17期84卷 18071-18093页

作者： Ayoub, Abeer El-Shafai, Walid El-Samie, Fathi E. Abd Hamad, Ehab K. I. Rabaie, El-Sayed M. Department of Electronics and Electrical Communication Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Electrical Engineering Department Faculty of Engineering Aswan University Aswan81542 Egypt

Particles in the atmosphere, such as dust and smoke, can cause visual clarity problems in both images and videos. Haze is the result of the interaction between airborne particles and light, which is scattered and attenuated. Hazy media present difficulties in a variety of applications due to the reduced contrast and loss of essential information. In response, dehazing techniques have been introduced to bring hazy videos and images back to clarity. Here, we provide a novel technique for eliminating haze. It comprises preprocessing steps before dehazing. Preprocessing is applied to hazy images through homomorphic processing and Contrast Limited Adaptive Histogram Equalization (CLAHE). We present a dehazing technique referred to as the pre-trained Feature Fusion Attention Network (FFA-Net) that directly lets dehazed images be restored from hazy or preprocessed hazy inputs without requiring the determination of atmospheric factors, such as air light and transmission maps. The FFA-Net architecture incorporates a Feature Attention (FA) method to do this task. We assess the proposed technique in a variety of circumstances, including visible frames, Near-Infrared (NIR) frames, and real-world hazy images. Evaluation criteria like entropy, correlation, and Peak Signal-to-Noise Ratio (PSNR) are used to compare the quality of dehazed frames or images to their hazy counterparts. Furthermore, histogram analysis and spectral entropy are adopted to determine the effectiveness of the proposed technique in comparison to existing dehazing techniques. Comparative results are presented for both real-world and simulated environments. The benefits of the proposed technique are demonstrated by a comparison of the results obtained from the standalone pre-trained FFA-Net and the proposed comprehensive methodology. Moreover, a thorough assessment is carried out for comparing the effectiveness of the proposed FFA-Net technique to those of some current dehazing techniques on real hazy images. T

关键词： Image enhancement

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Malware image classification: comparative analysis of a fine-tuned CNN and pre-trained models

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International Journal of computers and Applications 2023年第11期45卷 709-721页

作者： Majhi, Santosh Kumar Panda, Abhipsa Srichandan, Suresh Kumar Desai, Usha Acharya, Biswaranjan Department of Computer Science and Engineering Veer Surendra Sai University of Technology Burla India Department of Information Technology Veer Surendra Sai University of Technology Burla India Department of Electronics and Communication Engineering SR University Telangana Warangal India Department of Computer Engineering-AI & BDA Marwadi University Gujarat Rajkot India Department of Electronics and Communication Engineering S.E.A. College of Engineering & Technology Bengaluru India

A crucial part is played by malware detection and classification in ensuring the safety and security of computer systems. In this work, a comprehensive study has been presented for the classification of harmful or malware images that uses a Convolutional Neural Network (CNN) which has been finely tuned and its performance has been compared with five pre-trained models: ResNet50, InceptionResNetV2, VGG16, Xception and InceptionV3. The suggested CNN framework has been trained using the dataset MalImg_9010, consisting of 9,376 grayscale images resized to 128 × 128 pixels. The models have been evaluated based on their F1 score, recall, precision, and accuracy. The experiments that were conducted demonstrate that the fine-tuned CNN model achieves an impressive 0.965 as the F1 score and a 95.57% accuracy. Furthermore, the comparison with pre-trained models reveals the dominance of the presented framework concerning the F1 score and accuracy. The output of the conducted simulation suggests that the fine-tuned CNN approach shows promise for accurate malware image classification. Additionally, the paper discusses potential improvements, such as increasing the number of training epochs and incorporating larger and more diverse malware datasets, including RGB images and a broader range of malware families. The current research article gives valuable observations on various models’ effectiveness for classifying malware images and highlights the future scopes for research incorporating this domain. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Image classification

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Exploring Twitter Sentiments for Predicting Match Outcomes in the Game of Cricket 2

Exploring Twitter Sentiments for Predicting Match Outcomes i...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Sharma, Karan Hota, Lopamudra Tikkiwal, Vinay Anand Kumar, Arun Department of Computer Science and Engineering National Institute of Technology Rourkela769008 India Department of Electronics and Communication Engineering Jaypee Institute of Information Technology Noida201304 India

In recent years, social media platforms such as Twitter have become popular among fans to discuss and share their opinions about the matches. This research aims to contribute to the growing body of knowledge on utilizing the sentiments expressed through messages on social media platforms to predict outcomes of matches in the sport of cricket. This research work provides insights into the effectiveness of sentiment analysis techniques in forecasting match outcomes, which can be beneficial to cricket fans, and cricket analysts. In this work, sentiment analysis is carried out based on models developed using various machine-learning techniques. The work also explores the impact of different features, such as the number of tweets collected, the timing of tweets, and the sentiment analysis techniques on the accuracy of the prediction. The results of this study have implications for cricket fans, sports analysts, and the broader field of predictive analytics. © 2024 Elsevier B.V.. All rights reserved.

关键词： Sentiment analysis

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Robust Pipeline for Detection of Adversarial Images 4th

Robust Pipeline for Detection of Adversarial Images

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4th International Conference on Machine Intelligence and Signal Processing, MISP 2022

作者： Vohra, Ankit Reddy, Natesh Dhawale, Kritika Jain, Pooja Department of Computer Science and Engineering Indian Institute of Information Technology Nagpur India Department of Electronics and Communication Engineering Indian Institute of Information Technology Nagpur India

ISBN: (纸本)9789819900848

Deep neural networks (DNN’s) have achieved state-of-the-art (SOTA) for many image-related tasks like classification, object detection, etc. The authors have shown that DNN’s can be easily fooled by adversarial examples that can be generated by adding small distortions or perturbations to the images. Several attack methods to add these perturbations in images have been proposed like the Fast Gradient Sign Method (FGSM), Projected Gradient Descent (PGD), etc. to create distortions in images that cause the DNN’s to misclassify instances. It is essential to come up with strategies to identify such fake images. Keeping this in mind, we propose an end-to-end pipeline capable of detecting adversarially attacked images, i.e., images with perturbations. For a given image, we generate images with manipulation masks using Mantra-Net and pass them through a trained binary classifier that distinguishes between perturbed and original images. Experiments on the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) 2012 dataset have shown that our pipeline achieves an accuracy of 0.986 on the task of detecting adversarially crafted images. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep neural networks

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