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Exploring Deep Learning Methods for computer Vision Applications across Multiple Sectors:Challenges and Future Trends

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computer Modeling in engineering & sciences 2024年第4期139卷 103-141页

作者： Narayanan Ganesh Rajendran Shankar Miroslav Mahdal Janakiraman SenthilMurugan Jasgurpreet Singh Chohan Kanak Kalita School of computer science and engineering Vellore Institute of TechnologyChennai600127India department of computer science and engineering Koneru Lakshmaiah Education FoundationVaddeswaram522502India department of Control Systems and Instrumentation Faculty of Mechanical EngineeringVSB-Technical University of OstravaOstrava70800Czech Republic department of computer science and engineering Vel Tech High Tech Dr.Rangarajan Dr.Sakunthala Engineering CollegeChennai600062India department of Mechanical engineering and university Centre for Research&Development Chandigarh UniversityMohali140413India department of Mechanical engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyAvadi600062India

computer vision(CV)was developed for computers and other systems to act or make recommendations based on visual inputs,such as digital photos,movies,and other *** learning(DL)methods are more successful than other traditional machine learning(ML)methods *** techniques can produce state-of-the-art results for difficult CV problems like picture categorization,object detection,and face *** this review,a structured discussion on the history,methods,and applications of DL methods to CV problems is *** sector-wise presentation of applications in this papermay be particularly useful for researchers in niche fields who have limited or introductory knowledge of DL methods and *** review will provide readers with context and examples of how these techniques can be applied to specific areas.A curated list of popular datasets and a brief description of them are also included for the benefit of readers.

关键词： Neural network machine vision classification object detection deep learning

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Advancements and applications of position-sensitive detector(PSD): a review

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Optoelectronics Letters 2024年第6期20卷 330-338页

作者： Shaher Dwik G.Sasikala S.Natarajan department of Electronics&Communication engineering School of Electrical&CommunicationVel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai 600062India department of computer science and engineering Amrita School of ComputingAmrita Vishwa VidyapeethamChennai 601103India1

This paper presents a review of the position-sensitive detector(PSD) sensor, covering different types of PSD and recent works related to this field. Furthermore, it explains the theoretical concepts and provides information about its structure and principles of operation. Moreover, it includes the main information about the available commercial PSDs from different companies, along with a comparison between the common modules. The PSD features include high position resolution, fast response, and a wide dynamic range. These features make it suitable for various fields and applications, such as imaging, spectrometry, spectroscopy and others.

关键词： Advancements and applications of position-sensitive detector a review PSD

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Adaptive Marine Predator Optimization Algorithm(AOMA)-Deep Supervised Learning Classification(DSLC)based IDS framework for MANET security

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Intelligent and Converged Networks 2024年第1期5卷 1-18页

作者： M.Sahaya Sheela A.Gnana Soundari Aditya Mudigonda C.Kalpana K.Suresh K.Somasundaram Yousef Farhaoui department of Electronics and Communication engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai 600069India department of computer science and engineering Saveetha School of EngineeringSIMATSChennai 602105India JNIAS School of Planning and Architecture Hyderabad 500034India department of computer science and engineering NPR College of Engineering and TechnologyNatham Dindigul 624401India department of computer science and engineering PSNA College of Engineering and TechnologyPoolangulathupatti 620009India department of computer science and engineering Sri Muthukumaran Institute of TechnologyChennai 600069India department of computer science Moulay Ismail UniversityMeknes 5003Morocco

Due to the dynamic nature and node mobility,assuring the security of Mobile Ad-hoc Networks(MANET)is one of the difficult and challenging tasks *** MANET,the Intrusion Detection System(IDS)is crucial because it aids in the identification and detection of malicious attacks that impair the network’s regular *** machine learning and deep learning methodologies are used for this purpose in the conventional works to ensure increased security of ***,it still has significant flaws,including increased algorithmic complexity,lower system performance,and a higher rate of ***,the goal of this paper is to create an intelligent IDS framework for significantly enhancing MANET security through the use of deep learning ***,the min-max normalization model is applied to preprocess the given cyber-attack datasets for normalizing the attributes or fields,which increases the overall intrusion detection performance of ***,a novel Adaptive Marine Predator Optimization Algorithm(AOMA)is implemented to choose the optimal features for improving the speed and intrusion detection performance of ***,the Deep Supervise Learning Classification(DSLC)mechanism is utilized to predict and categorize the type of intrusion based on proper learning and training *** evaluation,the performance and results of the proposed AOMA-DSLC based IDS methodology is validated and compared using various performance measures and benchmarking datasets.

关键词： Intrusion Detection System(IDS) Security Mobile Ad-hoc Network(MANET) min-max normalization Adaptive Marine Predator Optimization Algorithm(AOMA) Deep Supervise Learning Classification(DSLC)

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An Intelligent Big Data Security Framework Based on AEFS-KENN Algorithms for the Detection of Cyber-Attacks from Smart Grid Systems

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Big Data Mining and Analytics 2024年第2期7卷 399-418页

作者： Sankaramoorthy Muthubalaji Naresh Kumar Muniyaraj Sarvade Pedda Venkata Subba Rao Kavitha Thandapani Pasupuleti Rama Mohan Thangam Somasundaram Yousef Farhaoui department of Electrical and Electronics engineering CMR College of Engineering&TechnologyHyderabad 501401India department of Electronics and Communication engineering Vardhaman College of Engineering KacharamShamshabad 501218India department of Electronics and Communication engineering Sreenidhi Institute of Science and TechnologyHyderabad 501301India department of Electronics and Communication engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai 600062India department of Electrical and Electronics engineering Bharat Institute of Engineering and TechnologyHyderabad 501510India department of computer science and engineering Amrita School of ComputingAmrita Vishwa VidhyapeethamBengaluru 560035India T-IDMS Department of Computer ScienceFaculty of Sciences and TechniquesMoulay Ismail UniversityErrachidia 52000Morocco

Big data has the ability to open up innovative and ground-breaking prospects for the electrical grid,which also supports to obtain a variety of technological,social,and financial *** is an unprecedented amount of heterogeneous big data as a consequence of the growth of power grid technologies,along with data processing and advanced *** main obstacles in turning the heterogeneous large dataset into useful results are computational burden and information *** original contribution of this paper is to develop a new big data framework for detecting various intrusions from the smart grid systems with the use of AI ***,an AdaBelief Exponential Feature Selection(AEFS)technique is used to efficiently handle the input huge datasets from the smart grid for boosting ***,a Kernel based Extreme Neural Network(KENN)technique is used to anticipate security vulnerabilities more *** Polar Bear Optimization(PBO)algorithm is used to efficiently determine the parameters for the estimate of radial basis ***,several types of smart grid network datasets are employed during analysis in order to examine the outcomes and efficiency of the proposed AdaBelief Exponential Feature Selection-Kernel based Extreme Neural Network(AEFS-KENN)big data security *** results reveal that the accuracy of proposed AEFS-KENN is increased up to 99.5%with precision and AUC of 99%for all smart grid big datasets used in this study.

关键词： smart grid Machine Learning(ML) big data analytics AdaBelief Exponential Feature Selection(AEFS) Polar Bear Optimization(PBO) Kernel Extreme Neural Network(KENN)

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Information Centric Networking Based Cooperative Caching Framework for 5G Communication Systems

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computers, Materials and Continua 2024年第3期80卷 3945-3966页

作者： Mahaveerakannan, R. Tamilvizhi, Thanarajan Rayen, Sonia Jenifer Khalaf, Osamah Ibrahim Hamam, Habib department of computer science and engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai 602105 India department of computer science and engineering Panimalar Engineering College Chennai 600123 India department of computer science and engineering Sathyabama Institute of Science and Technology Chennai 600119 India department of Solar Energy Al-Nahrain Research Center for Renewable Energy Al-Nahrain University Jadriya Baghdad 64074 Iraq Faculty of engineering Université de Moncton New Brunswick E1A3E9 Canada Hodmas university College Mogadishu 521376 Somalia Bridges for Academic Excellence Tunis 1002 Tunisia School of Electrical engineering University of Johannesburg Auckland Park Johannesburg 2006 South Africa

The demands on conventional communication networks are increasing rapidly because of the exponential expansion of connected multimedia content. In light of the data-centric aspect of contemporary communication, the information-centric network (ICN) paradigm offers hope for a solution by emphasizing content retrieval by name instead of location. If 5G networks are to meet the expected data demand surge from expanded connectivity and Internet of Things (IoT) devices, then effective caching solutions will be required to maximize network throughput and minimize the use of resources. Hence, an ICN-based Cooperative Caching (ICN-CoC) technique has been used to select a cache by considering cache position, content attractiveness, and rate prediction. The findings show that utilizing our suggested approach improves caching regarding the Cache Hit Ratio (CHR) of 84.3%, Average Hop Minimization Ratio (AHMR) of 89.5%, and Mean Access Latency (MAL) of 0.4 s. Within a framework, it suggests improved caching strategies to handle the difficulty of effectively controlling data consumption in 5G networks. These improvements aim to make the network run more smoothly by enhancing content delivery, decreasing latency, and relieving congestion. By improving 5G communication systems’ capacity to manage the demands faced by modern data-centric applications, the research ultimately aids in advancement. Copyright © 2024 The Authors. Published by tech science Press.

关键词： Non-negative matrix factorization

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Enhancing Human Action Recognition with Adaptive Hybrid Deep Attentive Networks and Archerfish Optimization

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computers, Materials and Continua 2024年第3期80卷 4791-4812页

作者： Ahmad, Ahmad Yahiya Ahmad Bani Alzubi, Jafar James, Sophers Nyangaresi, Vincent Omollo Kutralakani, Chanthirasekaran Krishnan, Anguraju department of Accounting and Finance Faculty of Business Middle East University Amman 11831 Jordan Faculty of engineering Al-Balqa Applied University Salt 19117 Jordan department of Mathematics Kongunadu College of Engineering and Technology (Autonomous) Tholurpatti Trichy 621215 India department of computer science and Software engineering Jaramogi Oginga Odinga University of Science and Technology Bondo 210-40601 Kenya department of Electronics and Communication engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai 602105 India department of Electronics and Communication engineering Saveetha Engineering College (Autonomous) Chennai 602105 India department of computer science and engineering Kongunadu College of Engineering and Technology (Autonomous) Tholurpatti Trichy 621215 India

In recent years, wearable devices-based Human Activity Recognition (HAR) models have received significant attention. Previously developed HAR models use hand-crafted features to recognize human activities, leading to the extraction of basic features. The images captured by wearable sensors contain advanced features, allowing them to be analyzed by deep learning algorithms to enhance the detection and recognition of human actions. Poor lighting and limited sensor capabilities can impact data quality, making the recognition of human actions a challenging task. The unimodal-based HAR approaches are not suitable in a real-time environment. Therefore, an updated HAR model is developed using multiple types of data and an advanced deep-learning approach. Firstly, the required signals and sensor data are accumulated from the standard databases. From these signals, the wave features are retrieved. Then the extracted wave features and sensor data are given as the input to recognize the human activity. An Adaptive Hybrid Deep Attentive Network (AHDAN) is developed by incorporating a "1D Convolutional Neural Network (1DCNN)" with a "Gated Recurrent Unit (GRU)" for the human activity recognition process. Additionally, the Enhanced Archerfish Hunting Optimizer (EAHO) is suggested to fine-tune the network parameters for enhancing the recognition process. An experimental evaluation is performed on various deep learning networks and heuristic algorithms to confirm the effectiveness of the proposed HAR model. The EAHO-based HAR model outperforms traditional deep learning networks with an accuracy of 95.36, 95.25 for recall, 95.48 for specificity, and 95.47 for precision, respectively. The result proved that the developed model is effective in recognizing human action by taking less time. Additionally, it reduces the computation complexity and overfitting issue through using an optimization approach. Copyright © 2024 The Authors. Published by tech science Press.

关键词： Convolutional neural networks

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Genetic grey wolf optimization and C-mixture for collaborative data publishing

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International Journal of Modeling, Simulation, and Scientific Computing 2018年第6期9卷 188-210页

作者： Yogesh R.Kulkarni T.Senthil Murugan vel tech university Avadi Chennai 600062Tamil NaduIndia department of computer science and engineering vel tech university chennai 600062 Tamil NaduIndia

Data publishing is an area of interest in present day technology that has gained huge attention of researchers and *** concept of data publishing faces a lot of security issues,indicating that when any trusted organization provides data to a third party,personal information need not be ***,to maintain the privacy of the data,this paper proposes an algorithm for privacy preserved collaborative data publishing using the Genetic Grey Wolf Optimizer(Genetic GWO)algorithm for which a C-mixture parameter is *** C-mixture parameter enhances the privacy of the data if the data does not satisfy the privacy constraints,such as the k-anonymity,l-diversity and the m-privacy.A minimum fitness value is maintained that depends on the minimum value of the generalized information loss and the minimum value of the average equivalence class *** minimum value of the fitness ensures the maximum utility and the maximum *** was carried out using the adult dataset,and the proposed Genetic GWO outperformed the existing methods in terms of the generalized information loss and the average equivalence class metric and achieved minimum values at a rate of 0.402 and 0.9,respectively.

关键词： k-Anonymity l-diversity m-privacy C-mixture Genetic GWO.

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Design of Online Vitals Monitor by Integrating Big Data and IoT

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computer Systems science & engineering 2023年第3期44卷 2469-2487页

作者： E.Afreen Banu V.Rajamani department of computer science engineering Vel Tech Multitech Dr.Rangarajan Dr.Sakunthala Engineering CollegeChennai600062India department of Electronics&Communication engineering Vel Tech Multitech Dr.Rangarajan Dr.Sakunthala Engineering CollegeChennai600062India

In this work,we design a multisensory IoT-based online vitals monitor(hereinafter referred to as the VITALS)to sense four bedside physiological parameters including pulse(heart)rate,body temperature,blood pressure,and periph-eral oxygen ***,the proposed system constantly transfers these signals to the analytics system which aids in enhancing diagnostics at an earlier stage as well as monitoring after *** core hardware of the VITALS includes commercial off-the-shelf sensing devices/medical equipment,a powerful microcontroller,a reliable wireless communication module,and a big data analytics *** extracts human vital signs in a pre-programmed interval of 30 min and sends them to big data analytics system through the WiFi module for further *** use Apache Kafka(to gather live data streams from connected sen-sors),Apache Spark(to categorize the patient vitals and notify the medical pro-fessionals while identifying abnormalities in physiological parameters),Hadoop Distributed File System(HDFS)(to archive data streams for further analysis and long-term storage),Spark SQL,Hive and Matplotlib(to support caregivers to access/visualize appropriate information from collected data streams and to explore/understand the health status of the individuals).In addition,we develop a mobile application to send statistical graphs to doctors and patients to enable them to monitor health conditions *** proposed system is implemented on three patients for 7 days to check the effectiveness of sensing,data processing,and data transmission *** validate the system accuracy,we compare the data values collected from established sensors with the measured readouts using a commercial healthcare monitor,the Welch Allyn®Spot *** pro-posed system provides improved care solutions,especially for those whose access to care services is limited.

关键词： Big data analytics blood pressure body temperature physiological parameters pulse rate sensors SPo2

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Water wave optimization-based routing protocol for vehicular adhoc networks

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International Journal of Modeling, Simulation, and Scientific Computing 2018年第5期9卷 210-232页

作者： Mukund B.Wagh N.Gomathi department of computer science vel tech Rangarajan Dr.Sagunthala R&D technical university Avadi Chennai 600062India

It is well known that Vehicular Adhoc Network(VANET)plays the major role in the field of Intelligent Transport System(ITS)to enhance road safety as *** entire communication among vehicles is covered in *** the number of researches is in progress for the enhancement of VANET ***,the issues arising with this field is the achievement of multi-constrained Quality of Service(QoS)*** solve this issue,our paper intends to propose a cost model that considers certain network quality measures like travel,collision,congestion and the awareness about the ***,this paper adopts a renowned algorithm named Water Wave Optimization algorithm(WWO)to find the optimal *** proposed method analyses its performance over other conventional methods like Lion Algorithm(LA),Genetic Algorithm(GA)and LA with Adaptive Mutation(LAAM)in terms of cost analysis,Convergence analysis and Computational *** the analytical results prove the superiority of proposed work in terms of decreased routing cost.

关键词： VANET routing protocol cost reduction QoS fuzzification WWO.

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Deep Root Memory Optimized Indexing Methodology for Image Search Engines

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computer Systems science & engineering 2022年第2期40卷 661-672页

作者： R.Karthikeyan A.Celine Kavida P.Suresh department CSE Vel Tech HighTech Dr.Rangarajan Dr.Sakunthala Engineering CollegeChennai600062TamilnaduIndia department of Physics Vel Tech Multitech Dr.Rangarajan Dr.Sakunthala Engineering CollegeChennai600062Tamil NaduIndia department of ECE Veltech Rangarajan Dr Sagunthala R&D Institute of Science and TechnologyChennaiTamilnadu600062India

Digitization has created an abundance of new information sources by altering how pictures are *** large image databases from a web portal requires an opted indexing structure instead of reducing the contents of different kinds of databases for quick *** approach paves a path toward the increase of efficient image retrieval techniques and numerous research in image indexing involving large image *** retrieval usually encounters difficulties like a)merging the diverse representations of images and their Indexing,b)the low-level visual characters and semantic characters associated with an image are indirectly proportional,and c)noisy and less accurate extraction of image information(semantic and predicted attributes).This work clearly focuses and takes the base of reverse engineering and de-normalizing concept by evaluating how data can be stored ***,retrieval becomes straightforward and *** research also deals with deep root indexing with a multidimensional approach about how images can be indexed and provides improved results in terms of good performance in query processing and the reduction of maintenance and storage *** focus on the schema design on a non-clustered index solution,especially cover *** schema provides a filter predication to make an index with a particular content of rows and an index table called filtered ***,we include non-key columns in addition to the key *** on two image data sets‘with and without’filtered indexing show low query *** compare efficiency as regards accuracy in mean average precision to measure the accuracy of retrieval with the developed coherent semantic *** results show that retrieval by using deep root indexing is simple and fast.

关键词： Multi-dimensional indexing deep root hashing image retrieval filtered indexing

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