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A Deep Learning and Powerful Computational Framework for Brain Cancer MRI Image Recognition

Journal of The Institution of Engineers (India): Series B 2024年第1期105卷 1-18页

作者： Kumar, Ankit Shukla, Santosh Kumar Prakash, Navin Yadav, Rakesh Kumar Department of Information Technology Babu Banarasi Das Institute of Technology and Management Lucknow India Department of Computer Science and Engineering Babu Banarasi Das Engineering College Lucknow India Department of Computer Science and Engineering MSOET Maharishi University of Information Technology Lucknow India

Brain tumors are one of the deadliest diseases and require quick and accurate methods of detection. Finding the optimum image for research goals is the first step in optimizing MRI images for pre- and post-processing. As a result, the mean grey-level approach was used to segment the MRI images using a threshold. In the second stage of statistical feature analysis, the picture features were extracted using the spatial gray-level dependency matrix using Hara lick's feature equations. As a consequence, the best features were picked and the tumor was placed in the appropriate location. In the third stage, an automated tool for classifying the photographs under assessment as having a tumor or not was created utilizing supervised learning and an artificially intelligent methodology. An efficient test of the network's performance yielded 97% of the desired results. © 2023, The Institution of Engineers (India).

关键词： Tumors

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Energy Efficient Unequal Fault Tolerance Clustering Approach

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computer Systems science & Engineering 2023年第5期45卷 1971-1983页

作者： Sowjanya Ramisetty Divya Anand Kavita Sahil Verma NZ Jhanjhi Mehedi Masud Mohammed Baz Department of Computer Science and Engineering KG Reddy College of Engineering and TechnologyHyderabadIndia School of Computer Science and Engineering Lovely Professional UniversityPhagwaraIndia Department of Computer Science and Engineering Chandigarh UniversityMohaliIndia Department of Computer Science and Engineering Tailor’s UniversityMalaysia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Engineering College of Computer and Information TechnologyTaif UniversityTaif21994Saudi Arabia

For achieving Energy-Efficiency in wireless sensor networks(WSNs),different schemes have been proposed which focuses only on reducing the energy consumption.A shortest path determines for the Base Station(BS),but fault tolerance and energy balancing gives equal importance for improving the network *** saving energy in WSNs,clustering is considered as one of the effective methods for Wireless Sensor *** of the excessive overload,more energy consumed by cluster heads(CHs)in a cluster based WSN to receive and aggregate the information from member sensor nodes and it leads to *** increasing the WSNs’lifetime,the CHs selection has played a key role in energy consumption for sensor *** Energy Efficient Unequal Fault Tolerant Clustering Approach(EEUFTC)is proposed for reducing the energy utilization through the intelligent methods like Particle Swarm Optimization(PSO).In this approach,an optimal Master Cluster Head(MCH)-Master data Aggregator(MDA),selection method is proposed which uses the fitness values and they evaluate based on the PSO for two optimal nodes in each cluster to act as Master Data Aggregator(MDA),and Master Cluster *** data from the cluster members collected by the chosen MCH exclusively and the MDA is used for collected data reception from MCH transmits to the ***,the MCH overhead *** the heavy communication of data,overhead controls using the scheduling of Energy-Efficient Time Division Multiple Access(EE-TDMA).To describe the proposed method superiority based on various performance metrics,simulation and results are compared to the existing methods.

关键词： Energy-efficiency unequal fault tolerant clustering approach particle swarm optimization master data aggregator energy efficient time division multiple access optimal nodes

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An efficient optimal load balancing algorithm for distributed file systems in cloud environment

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International Journal of Networking and Virtual Organisations 2024年第2期30卷 134-151页

作者： Nebagiri, Manjula H. Latha, P.H. Department of Computer Science Atria Institute of Technology Bengaluru India Department of Information Science Sambhram Institute of Technology Bengaluru India

Efficient operations in distributed environments can be obtained by load balancing (LB). LB has turned out to be a vital and interesting research area with respect to the cloud owing to the swift augmentation of cloud computing, and the more services together with better outcomes demand of the clients. The work has developed a framework named an efficient optimal LB (EOLB) for distributed files system to beat the challenges faced in LB. LB was done by means of the framework centred on node distribution together with task distribution. Centred upon the data aspects as well as cloud servers, say CPU in addition to memory usage, together with disk IO occupancy rate, etc., it renders task distribution. Experimental analysis exhibits that the framework attains a better response rate of 74.68 ms, and a processing time (PT) of 0.43 ms, in addition, remains to be efficient when weighed with the prevailing methods. © 2024 Inderscience Enterprises Ltd.

关键词： Distributed cloud

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A systematic review on lightweight security algorithms for a sustainable IoT infrastructure

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Discover Internet of Things 2025年第1期5卷 1-20页

作者： Sarker, Kamal Uddin Department of Computer Science and MIS Oman College of Management and Technology Barka Oman

Operational technology, industrial automation, advanced healthcare systems, and smart city infrastructures are common forms of IoT integrated distributed networks. Numerous IoT components require vast amounts of power for sensing, data extraction, processing, and sharing over the internet. Moreover, IoT devices are in operation with a lack of standards that poses a severe security threat. Intrusion detection systems and cryptographic algorithms are two important components in the security posture of a cyberspace. IoT applications provide real-time services, so security measures are active 24/7/365. Hence, these algorithms are also related to energy consumption. It is a study of security algorithms based on their hardware and software implementations. This review systematically searches for and selects the latest research documents, conducting an analysis of sustainability and security. It depicts the lightweight features of cryptographic algorithms;the relationship of gate density, chip area and power consumption in CMOS technology;and the importance of Machine Learning (MA) applications for IDS and cryptographic solutions in Next Generation IoT (NGIoT). © The Author(s) 2025.

关键词： Intrusion detection

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Supervised Enhancement for Fingertip OCT Images Based on Paired Dataset Generation Strategy

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IEEE Transactions on information Forensics and Security 2025年 20卷 5039-5053页

作者： Miao, Qingran Wang, Haixia Zhou, Jianru Zhang, Yilong Chen, Peng Liang, Ronghua Feng, Yuanjing Zhejiang University of technology College of Information Engineering Hangzhou China Zhejiang University of technology College of Computer Science and Technology Hangzhou China

Optical Coherence Tomography (OCT) is a high-resolution, non-invasive imaging technology increasingly used for biometric data collection from fingertips. OCT captures volume data up to 3mm below the skin surface in the form of a series of B-scan images, enabling the reconstruction of internal fingerprints (IF) and internal sweat pores (ISP), thereby enhancing the security of biometric recognition. Despite the advantages, OCT images suffer from speckle noise and tissue discontinuity, making the extraction of subcutaneous biometric features challenging. Traditional hardware and software-based enhancement methods often result in over-smoothing and structural loss. Recent advancements in deep learning (DL) offer promising alternatives, with supervised DL methods showing efficacy when trained with high-quality paired datasets. However, the absence of ground-truth (GT) data makes it impossible to apply these models. This study proposes a novel supervised enhancement method for fingertip OCT images, with a paired dataset generation strategy. An OCT few-shot GAN and a Quality Estimation Module are proposed and incorporated into the strategy to realize translation from minimal GT manual augmentation to high-quality paired dataset, effectively addressing the challenge of data scarcity. A Fast Supervised Enhancement GAN (FSE-GAN) is proposed thereafter to perform simultaneous speckle noise reduction and tissue structure restoration, facilitating accurate extraction of internal fingerprints and sweat pores. Experiments demonstrate that the enhanced images significantly simplify IF and ISP extraction while achieving outstanding result quality. © 2005-2012 IEEE.

关键词： Image enhancement

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Insider Attack Prevention:LAPUP—Lightweight Authentication Protocol Using PUF

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Journal of Communications and information Networks 2024年第2期9卷 192-206页

作者： Siranjeevi Rajamanickam Satyanarayana Vollala N.Ramasubramanian Department of Computer Engineering Government Polytechnic CollegeTiruchirappalli 620022India Department of Computer Science and Engineering Dr.Shyama Prasad Mukherjee International Institute of Information Technology-Naya RaipurChhattisgarh 493661India Department of Computer Science and Engineering National Institute of TechnologyTiruchirappalli 620015India

The incredible progress in technologies has drastically increased the usage of Web *** share their credentials like userid and password or use their smart cards to get authenticated by the application *** cards are handy to use,but they are susceptible to stolen smart card attacks and few other notable security *** prefer to use Web applications that guarantee for security against several security attacks,especially insider attacks,which is *** of several existing schemes prove the security pitfalls of the protocols from preventing security attacks,specifically insider *** paper introduces LAPUP:a novel lightweight authentication protocol using physically unclonable function(PUF)to prevent security attacks,principally insider *** PUFs are used to generate the security keys,challenge-response pair(CRP)and hardware signature for designing the *** transmitted messages are shared as hash values and encrypted by the keys generated by *** messages are devoid of all possible attacks executed by any attacker,including insider *** is also free from stolen verifier attacks,as the databases are secured by using the hardware signature generated by *** analysis of the protocol exhibits the strength of LAPUP in preventing insider attacks and its resistance against several other security *** evaluation results of the communication and computation costs of LAPUP clearly shows that it achieves better performance than existing protocols,despite providing enhanced security.

关键词： physically unclonable function(PUF) insiderattacks authenticationkeymanagementserver(AKMS) security

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A Reputation-Based AODV Protocol for Blackhole and Malfunction Nodes Detection and Avoidance

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computers, Materials & Continua 2024年第8期80卷 1867-1888页

作者： Qussai M.Yaseen Monther Aldwairi Ahmad Manasrah Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjman346United Arab Emirates Department of Computer Information Systems Faculty of Computer and Information TechnologyJordan University of Science and TechnologyIrbid22110Jordan College of Technological Innovation Zayed UniversityAbu Dhabi144534United Arab Emirates Faculty of Information Technology and Computer Science Yarmouk UniversityIrbid21163Jordan Computer Information Systems Higher Colleges of TechnologySharjah341214United Arab Emirates

Enhancing the security of Wireless Sensor Networks(WSNs)improves the usability of their ***,finding solutions to various attacks,such as the blackhole attack,is crucial for the success of WSN *** paper proposes an enhanced version of the AODV(Ad Hoc On-Demand Distance Vector)protocol capable of detecting blackholes and malfunctioning benign nodes in WSNs,thereby avoiding them when delivering *** proposed version employs a network-based reputation system to select the best and most secure path to a *** achieve this goal,the proposed version utilizes the Watchdogs/Pathrater mechanisms in AODV to gather and broadcast reputations to all network nodes to build the network-based reputation *** minimize the network overhead of the proposed approach,the paper uses reputation aggregator nodes only for forwarding reputation ***,to reduce the overhead of updating reputation tables,the paper proposes three mechanisms,which are the prompt broadcast,the regular broadcast,and the light broadcast *** proposed enhanced version has been designed to perform effectively in dynamic environments such as mobile WSNs where nodes,including blackholes,move continuously,which is considered a challenge for other *** the proposed enhanced protocol,a node evaluates the security of different routes to a destination and can select the most secure routing *** paper provides an algorithm that explains the proposed protocol in detail and demonstrates a case study that shows the operations of calculating and updating reputation values when nodes move across different ***,the paper discusses the proposed approach’s overhead analysis to prove the proposed enhancement’s correctness and applicability.

关键词： AODV blackhole malfunction nodes pathrater reputation system wireless sensor networks(WSNs) watchdogs

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Spatial attention-based hybrid VGG-SVM and VGG-RF frameworks for improved cotton leaf disease detection

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Neural Computing and Applications 2025年第14期37卷 8309-8329页

作者： Pandiyaraju, V. Anusha, B. Senthil Kumar, A.M. Jaspin, K. Venkatraman, Shravan Kannan, A. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai India Department of Computer Science and Engineering Marian Engineering College Trivandram Kerala Trivandrum India Department of Computer Science and Engineering St. Joseph’s Institute of Technology Tamil Nadu Chennai India Department of Information Science and Technology College of Engineering Guindy Anna University Tamil Nadu Chennai India

The agriculture industry's production and food quality have been impacted by plant leaf diseases in recent years. Hence, it is vital to have a system that can automatically identify and diagnose diseases at an initial stage for enhancing the quality of agricultural output and also for stopping the overall plant extinction. Most state-of-the-art methods do not provide appropriate accuracy of identification when the similarity among different diseases is higher and the input leaf images have complex background. In this work, a transfer learning-based automated crop disease recognition system is proposed to handle this problem. The cotton leaf disease detection framework being proposed here includes two key stages: (1) the feature extraction stage, where the convolutional neural network (CNN) model from the visual geometry group (VGG-16) is employed to obtain the deep features from cotton leaf images with complex background. (2) Cotton leaf disease detection stage: Here, the extracted optimal features are input to the machine learning classifiers, namely random forest (RF) and nonlinear support vector machine (SVM), for the initial classification of the diseases. For providing an optimal cotton crop disease detection performance, Kaggle’s four-class cotton disease dataset with complex background is chosen in this work. For the final decision making and analysis, two hybrid models named spatial attention-based hybrid VGG-RF and hybrid VGG-SVM have been proposed in this work for enhancing the decision accuracy. These hybrid models offered an average accuracy of approximately 98.29% and 99.31%, respectively, which is higher than the accuracy provided by the related work. This proved that the proposed hybrid model, namely spatial attention-based VGG-SVM and VGG-RF models, with spatial attention outperformed the state-of-the-art classifiers. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Convolutional neural networks

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Preemptive Min Max Optimal Cost Based Scheduling for Improving the Load Balancing in Virtualized Cloud Environment

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SN computer science 2024年第6期5卷 754页

作者： Ravikumar, K. Saravanakumar, K. Viswanathan, Anand Department of Computer Science and Engineering RRASE College of Engineering Department of Computer Science and Engineering Sri Eshwar College of Engineering Department of Information Technology Ponjesly College of Engineering

Load balancing and scheduling are essential components of cloud computing that aim to optimize resource allocation and utilization. In a cloud environment, multiple virtual machines and applications compete for shared resources, and efficient load balancing and scheduling mechanisms are crucial for ensuring optimal performance and resource utilization. The prior methodologies attains higher utilization to allocate task due to makspan problem leads more deadlock occurrence and request failures with higher energy consideration. Furthermore, the paper examines the challenges and considerations in load balancing and scheduling, such as dynamic workload variations, heterogeneity of resources, and QoS requirements. To handle this issues, to propose an Efficient Load Balancing Based Predictive Priority-based Preemptive Min max priority Load balancer (PMin-Max LB) to improve the service optimality in Decentralized cloud server. Initially the user request accessibility and task progress is evaluated through Service level Workload Impact score (SLWIS). Then introducing Workload Scaling Job vector estimation is carried by Particle Swarm Optimization (PSO). By evaluating the Makespan Instance Scaling Changeover Virtualization (MISCV) task weights are assigned to virtual machine by applying Preemptive Min max priority Load balancer (PMin-Max LB) to reduce the workload to improve the performance. The proposed system improves the impact of load balancing and scheduling on cloud performance, scalability, and cost-effectiveness. Finally proposed system attains the potential advancements in load balancing and scheduling techniques improves the computing approach in virtualization process to reduce the workload burned to optimize the heterogeneity and Quality of service. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cloud computing Load balancer Particle swarm optimization Priority scheduling Task allocation Virtual machine

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Deploying Hybrid Ensemble Machine Learning Techniques for Effective Cross-Site Scripting(XSS)Attack Detection

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computers, Materials & Continua 2024年第10期81卷 707-748页

作者： Noor Ullah Bacha Songfeng Lu Attiq Ur Rehman Muhammad Idrees Yazeed Yasin Ghadi Tahani Jaser Alahmadi School of Cyber Science and Engineering Huazhong University of Science and TechnologyWuhan430073China Department of Computer Science and Engineering University of Engineering and TechnologyLahore54000Pakistan Department of Computer Science and Software Engineering Al Ain UniversityAl Ain12555Abu Dhabi Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia

Cross-Site Scripting(XSS)remains a significant threat to web application security,exploiting vulnerabilities to hijack user sessions and steal sensitive *** detection methods often fail to keep pace with the evolving sophistication of cyber *** paper introduces a novel hybrid ensemble learning framework that leverages a combination of advanced machine learning algorithms—Logistic Regression(LR),Support Vector Machines(SVM),eXtreme Gradient Boosting(XGBoost),Categorical Boosting(CatBoost),and Deep Neural Networks(DNN).Utilizing the XSS-Attacks-2021 dataset,which comprises 460 instances across various real-world trafficrelated scenarios,this framework significantly enhances XSS attack *** approach,which includes rigorous feature engineering and model tuning,not only optimizes accuracy but also effectively minimizes false positives(FP)(0.13%)and false negatives(FN)(0.19%).This comprehensive methodology has been rigorously validated,achieving an unprecedented accuracy of 99.87%.The proposed system is scalable and efficient,capable of adapting to the increasing number of web applications and user demands without a decline in *** demonstrates exceptional real-time capabilities,with the ability to detect XSS attacks dynamically,maintaining high accuracy and low latency even under significant ***,despite the computational complexity introduced by the hybrid ensemble approach,strategic use of parallel processing and algorithm tuning ensures that the system remains scalable and performs robustly in real-time *** for easy integration with existing web security systems,our framework supports adaptable Application Programming Interfaces(APIs)and a modular design,facilitating seamless augmentation of current *** innovation represents a significant advancement in cybersecurity,offering a scalable and effective solution for securing modern web applications against evolving threats.

关键词： Cross-site scripting machine learning XSS detection stacking ensemble learning hybrid learning

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