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Enhancing remote sensing image analysis: optimization of a hybrid deep network through HHO algorithm

Multimedia Tools and Applications 2025年 1-21页

作者： Digra, Monia Dhir, renu Shar.a. Nonita Department of Computer Science and Engineering Dr. B. R. Ambedkar National Institute of Technology Punjab Jalandhar144011 India Department of Information Technology Indra Gandhi Delhi Technical University Delhi India

remote sensing (rS) image interpretation includes multi-label picture classification, a key problem. The complicated and diverse character of many remote sensing scenes, which are produced by the spatial combination and association of various objects, makes classification difficult. In this research, an innovative appr.a.h that combines deep neural networks with bio-inspired optimization techniques to handle the challenges of classifying remote sensing images. First, the efficacy of proposed model was evaluated using the publicly accessible remote sensing scene dataset for multi-label classification. Second, this work employs the HHO (Harris Hawk optimization) model to improve classification perfor.a.ce by efficiently choosing features and also reduces the time complexity by 78%. The DenseNet-169 and VGGNet16 models extract the necessary properties to enhance remote sensing classification. Finally, validate the data by integrating additional validation data in the training set, which leads to more reliable and consistent accuracy and loss. The proposed VGGNet16 with Bi-LSTM and DenseNet169 with Bi-LSTM achieves accuracy on four distinct datasets 98.83% and 99.69% on UCM, 97.77% and 97.98% on AID, 95.06% and 96.84% on NWPU, wher.a. rSI-CB256 dataset achieved 98.5% and 98.91% accuracy. The hybrid fusion model distinguishes impor.a.t from irrelevant data. To demonstrate its efficacy, authors conducted extensive tests on various distinct datasets such as UCM, AID, NWPU, rSICB256 and have ascer.a.ned that this appr.a.h exhibits superiority over existing state-of-the-art techniques currently employed in the field. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Deep neural networks

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A Deep Learning Appr.a.h for Detecting Malware Using Autoencoder

IAENG International Journal of Computer Science

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IAENG Inter.a.ional Jour.a. of computer Science 2024年第8期51卷 1051-1059页

作者： Panchagnula, Venu Madhav Satya Keerthi, N.V.L.Ch. Surekha, S. Sujatha, r. Veeraiah, Duggineni ramesh, Eluri Lakshmi, B. Electronics and Communication Engineering Department Prasad V Potluri Siddhartha Institute of Technology Andhra Pradesh Vijayawada India Computer Science and Engineering Department Koneru Lakshmaiah Education Foundation Andhra Pradesh Guntur India AIML Department Vignana Bharathi Institute of Technology Ghatkesar Hyderabad India Information Technology Department M. Kumarasamy College of Engineering Karur India Computer Science and Engineering Department Lakireddy Bali Reddy College of Engineering Andhra Pradesh Mylavaram India Computer Science and Engineering Department R.V.R & J. C. College of Engineering Guntur India Electronics and Communication Engineering Department Rajiv Gandhi University of Knowledge Technologies Andhra Pradesh Nuzvid India

research in the field of malware detection is currently leaning toward methods based on artificial intelligence algorithms due to the incr.a.ing limitations of traditional detection methods and the incr.a.ing accuracy of these methods. This study therefore introduces a novel appr.a.h to malware detection. This method integrates a deep learning model's autoencoder network with a grayscale graphic depiction of malware. By looking at the autoencoder's reconstruction error, we may assess if the grey-scale picture appr.a.h is feasible to use for malware detection. Additionally, it differentiates between malicious and safe software by utilizing the autoencoder's dimensionality reduction capabilities. Using the Android dataset, our newly introduced detection model attained a 95% accuracy and maintained a consistently high F-score of approximately 95%. This outperformed more conventional methods of machine learning detection. © (2024), (Inter.a.ional Association of Engineers). All rights reserved.

关键词： Malware

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Enhanced Visuals through Software-Optimized Memory Pipeline 2

Enhanced Visuals through Software-Optimized Memory Pipeline

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2nd Inter.a.ional Conference on computer, Communication and Control, IC4 2024

作者： ramesh, P.S. Senthilvadivu, S. Sudha, I. Kumar, T. rajesh Naveen, V. Nagarajan, B. Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Saveetha University Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Simats Department of Computer Science and Engineering Tamil Nadu Chennai India Madanapalle Institute of Technology and Science Department of Computer Science and Technology Madanapalle India Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Tamilnadu Coimbatore India

ISBN: (纸本)9798350387933

Image processing algorithms, which supply the image quality, are used by modern mobile devices to capture images. These methods need more rAM to process an image and fix these problems. Software pipelines are utilized to reduce memory usage as much as feasible while maintaining the highest level of flexibility and affor.a.ility. Our method uses a line-based buffer software pipeline, which improves image quality and runs at a minimal cost. By transferring only the output to the next phase, the algorithm operates on an individual basis. Each step is completed in less time. Using picture enhancement and noise reduction techniques results in incr.a.ed productivity, reduced memory consumption, and gr.a.er image quality. The applied framework demonstrates noise reduction with the PSNr.a.gorithm and yields good results. © 2024 IEEE.

关键词： Image quality

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Cyber-Physical Systems and IoT Intrusion Detection: A Hybrid Deep Learning Perspective 3

Cyber-Physical Systems and IoT Intrusion Detection: A Hybrid...

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3rd Inter.a.ional Conference on Electronics and renewable Systems, ICEArS 2025

作者： Prakash, S. Jaya Devi, D. Muthukumaran, N. romila, r. Idhaya Engineering College for Women Department of Computer Science and Engineering Tamilnadu Chinnasalem India Maha Barathi Engineering College Department of Computer Science and Engineering Tamilnadu Chinnasalem India A.R. Engineering College Department of Computer Science and Engineering Tamilnadu Villupuram India Sri Muthukumaran Institute of Technology Department of Information Technology Tamilnadu Chennai India

ISBN: (纸本)9798331509675

Several vital resources are incr.a.ingly being protected by cyber-physical systems (CPSs), makes the detection of incidents on these systems critical. CPSs along with other domains, such as the Internet of Things (IoT), frequently use machine learning (ML) and deep learning (DL) appr.a.hes to combat intrusion detection. Existing appr.a.hes, however, frequently favor the deployment of advanced detection models over their usefulness in actual operations. This research work develops a hybrid intrusion detection technique by combining Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM). This work analyzes the experimental results to LSTM and CNN to develop an effective comparative appr.a.h. The experiments are conducted on the two datasets. The efficiency indicators examined are intrusion detection results such as accuracy, precision, recall, and f1score. This comparison indicates that the proposed hybrid algorithm is the preferable appr.a.h. © 2025 IEEE.

关键词： Network intrusion

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Detection of Dysrhythmias from Electrocardiogram Signals Using Noise removal and Deep Learning Techniques

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SN computer Science 2024年第7期5卷 1-13页

作者： rajesh, P. Balasubramaniyan, M. Thiyagu, T.M. Azhagiri, M. Shanmuganathan, C. Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R & amp D Institute of Science and Technology Tamil Nadu Chennai India Department of Computer Science and Engineering Sri Venkateswara College of Engineering and Technology (Autonomous) Andhra Pradesh Chittoor 517127 India Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Tamil Nadu Chennai 600089 India

Heart related diseases have cr.a.ed a major loss in human lives. Healthy heart ensures good standard of living from infant to aged people for.a.long period. Predicting heart related diseases prior to serious cause would mostly reduce death and save human life. One such heart related issue is dysrhythmias which is an unstable rhythm occurs in the nor.a. heart beat of the humans due to improper electrical dispositions that synchronizes the heartbeat. This endangered disease would definitely lead to the end of life irrespective of the age group. This unstable rhythm would be diagnosed mostly using Electrocardiogram (ECG) which is an electrical waveform that depicts the heartbeat. This research aimed to discover.a.novel method that makes use of the ECG waveforms as a training and testing dataset with the most incipient Deep Learning (DL) algorithms’ to predict dysrhythmia earlier. The DL models like Convolutional Neural Network (CNN) and Bi directional Long Short Term Memory (BLSTM) are combined to experiment the input data by segmenting the signals. A noise removal technique was used to remove the noise in the waveforms of the ECG data and built a two dimensional image which is the input to the proposed method. The proposed method produced remar.a.le results as 99.36 percent in accuracy, 0.99 as F1 score. The objective of the investigation is to save human lives from serious and severe dysrhythmias by predicting it as early as possible by using the proposed methodology. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： BLSTM CNN Dysrhythmias ECG Noise removal

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Development of Deepfake Detection Techniques for Protecting Multimedia Infor.a.ion using Deep Learning 3

Development of Deepfake Detection Techniques for Protecting ...

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3rd Inter.a.ional Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Siva rama Lingham, N. Devakanth, J Jude Moses Anto raj, Gowtham Gayathri, K. Janani, r. Dhanapal, r. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Acharya Institute of Technology Department of Computer Science and Engineering Bangalore India Bannari Amman Institute of Technolgy Department of Information Technology Erode India Sri Krishna College of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Karpagam Academy of Higher Education Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350375190

The development of proposed deepfake detection techniques plays a prominent role in the safeguarding the multimedia infor.a.ion. The various challenges are addressed using the aid of deep learning techniques. This involves hybrid optimization techniques such as particle swarm optimization and genetic algorithms. This helps to improve the accuracy and efficiency in the detection of deepfake. PSO helps in optimizing the weights and parameters of the neural network to obtain faster convergence. GA helps in obtaining potential solutions to obtain the robust deepfake detection model. The integration of deep learning with hybrid optimization involves the collection and preprocessing of the multimedia infor.a.ion and proceeds to an optimization algorithm. The manipulated content in the online platform are detected during the training process. The hybrid optimization tcehniques helps in obtaining model generability and achieving resilience in various attacks occurring in the deepfake generation. The integration of PSO and GA helps in the accurate detection of deepfake content through continuous training and evaluation. Thus the proposed system forms a safeguarding tool for multimedia content and helps to eliminate various risks. © 2024 IEEE.

关键词： Genetic algorithms

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Hybrid Technique-based Optimal Energy Management in Smart Home Appliances

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IEIE Transactions on Smart Processing and Computing 2024年第5期13卷 425-434页

作者： Shirley, C.P. Depaa, r.a.B. Priya, A. Sarala, r. Solanki, rajdeep Singh Malini, K.V. Venkatakrishna reddy, Ch. Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Department of Civil Engineering Dr. M.G.R Educational and Research Institute Maduravoyal Chennai95 India School of Architecture Koneru Lakshmaiah Educational Foundation Andhra Pradesh Guntur India Department of Computer Science and Engineering Velammal College of Engineering and Technology Madurai Rameshwaram High Road Viraganoor Madurai625009 India Department of Computer Science Medi-Caps University Indore India Department of Electrical and Electronics Engineering Sri Sairam College of Engineering Anekal India Department of Electrical and Electronics Engineering Chaitanya Bharathi Institute of Technology Hyderabad75 India

This manuscript presents a hybrid method for optimal energy management in smart home appliances. The proposed appr.a.h combines the Ebola Optimization Search Algorithm (EOSA) with the perfor.a.ce of spiking neural networks (SNN). The key objective of the proposed strategy is cost reduction through day-ahead load scheduling while also considering the best demand response (Dr), and self-consumption of photovoltaic (PV). The EOSA method manages air conditioning (AC) and ensures ther.a. comfort. In addition, the SNN method was used to predict the optimal system control signal. Comparative analysis with existing systems, such as PSO, r.a. and BCO, showed that the proposed strategy resulted in 13% cost savings. The proposed system considered parameters, such as solar radiation, occupant presence, and electricity prices. The proposed technique is implemented in the MATLAB software, and the perfor.a.ce is compared with existing appr.a.hes. The efficiency of the proposed appr.a.h is higher than the existing techniques. © 2024 The institute of Electronics and Infor.a.ion Engineers.

关键词： Demand response

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Plant Disease Detection Using Machine Learning Appr.a.hes: A review 4th

Plant Disease Detection Using Machine Learning Approaches: A...

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4th Inter.a.ional Conference on Data, engineering, and Applications, IDEA 2022

作者： Saraf, Puja Dipak Patil, Jayantrao Bhaurao Department of Computer Engineering R. C. Patel Institute of Technology Shirpur425405 India

ISBN: (纸本)9789819700363

Agriculture plays an impor.a.t role in the country’s development, and it is the key source of food for the population of the world. But in developing countries like India, Pakistan and Nepal, the condition of the agriculture sector is getting worse day by day. There are many r.a.ons behind it, but one of the r.a.ons is the degeneration of crops by pests and disease. Agriculture solely depends on the biological health of the seeds, crops, infor.a.ion on weather conditions soils and biotic features liable for the crop diseases. robust diagnostic methods are accomplished by dispensation and scrutinizing large quantities of records near get additional consistent mater.a. and ample more accurate predictions remain vital. Deep learning, image processing, soft computing and data mining are likely to play a vital role in recognizing plant diseases. This paper.a.ms to review existing studies and investigations using the recent practices to solve a variety of agricultural problems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Adver.a.ial machine learning

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A Systematic Literature review of XAI-based Appr.a.hes on Brain Disease Detection using Brain MrI Images 4

A Systematic Literature Review of XAI-based Approaches on Br...

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4th Inter.a.ional Conference on Advanced research in Computing, ICArC 2024

作者： Madapatha, Savin Fer.a.do, Pumudu University of Westminster Department of Computer Science and Engineering London United Kingdom School of Computing Informatics Institute of Technology Colombo Sri Lanka

ISBN: (纸本)9798350384864

Brain Magnetic resonance Imaging (MrI) analysis is a widely used medical procedure for the early diagnosis of various brain diseases. Accurate pathology identification during the brain MrI analysis procedure is crucial as misdiagnoses or missed findings can gr.a.ly affect a patient's tr.a.ment and long-term prediction. With the recent advancement of Artificial Intelligence (AI) in the medical field, researchers have appr.a.hed various techniques to detect brain diseases using AI. Although AI models exhibit high accuracy, they suffer from a lack of transparency and interpretability, paving the way for the development of eXplainable Artificial Intelligence (XAI) methods in brain disease diagnosis. Image segmentation, machine learning, deep learning and XAI are impor.a.t for.a.sisting the diagnostic procedure. In this paper, a comprehensive overview of various existing techniques in brain disease detection using MrI is presented, starting with image segmentation techniques, followed by classification techniques, and finally, XAI techniques. In conclusion, the paper identifies a critical need for further research on XAI integration to advance brain disease detection. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Advancing Color Perception: An IoT-driven Pedagogical System for Visually Impaired Children 1

Advancing Color Perception: An IoT-driven Pedagogical System...

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1st IEEE Inter.a.ional Conference on Advances in Signal Processing, Power, Communication, and Computing, ASPCC 2024

作者： Balage, Prasandika Jayawardene, Nuwan University of Westminster School of Computer Science and Engineering London United Kingdom Informatics Institute of Technology Department of Computing Colombo Sri Lanka

ISBN: (纸本)9798350355093

Visual impairment is characterized by the loss of vision, encompassing both complete blindness and partial vision loss. Studies reveal a notable prevalence of visual impairment among school-aged children. The significance of colors in acquiring and discerning visual infor.a.ion is crucial, aiding children in effectively perceiving and comprehending their surroundings. This study presents an Internet of Things (IoT) system designed for teaching colors to visually impaired children. The system achieved an accuracy rate sur.a.sing 77.8%, and it is operated effectively regardless of surrounding lighting conditions. Given that the target users of the system are visually impaired children, this research was conducted in close alignment with Human-computer Interaction (HCI) principles. © 2024 IEEE.

关键词： Color vision

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