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Heterogeneous Ensemble Feature Selection Model(HEFSM)for Big Data Analytics

computer Systems science & engineering 2023年第5期45卷 2187-2205页

作者： M.Priyadharsini K.Karuppasamy Department of Information Technology Sri Ramakrishna Engineering CollegeCoimbatoreTamilnaduIndia Department of Computer Science&Engineering RVS College of Engineering and TechnologyCoimbatoreTamilnaduIndia

Big Data applications face different types of complexities in *** and purifying data by eliminating irrelevant or redundant data for big data applications becomes a complex operation while attempting to maintain discriminative features in processed *** existing scheme has many disadvantages including continuity in training,more samples and training time in feature selections and increased classification execution *** ensemble methods have made a mark in classification tasks as combine multiple results into a single *** comparing to a single model,this technique offers for improved *** based feature selections parallel multiple expert’s judgments on a single *** major goal of this research is to suggest HEFSM(Heterogeneous Ensemble Feature Selection Model),a hybrid approach that combines multiple *** major goal of this research is to suggest HEFSM(Heterogeneous Ensemble Feature Selection Model),a hybrid approach that combines multiple ***,individual outputs produced by methods producing subsets of features or rankings or voting are also combined in this ***(K-Nearest Neighbor)classifier is used to classify the big dataset obtained from the ensemble learning *** results found of the study have been good,proving the proposed model’s efficiency in classifications in terms of the performance metrics like precision,recall,F-measure and accuracy used.

关键词： PSO(Particle Swarm Optimization) GWO(GreyWolf Optimization) EHO(Elephant Herding Optimization) data mining big data analytics feature selection HEFSM classifier

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Aspect Oriented Sentiment Analysis on Customer Reviews on Restaurant Using the LDA and BERT Method

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SN computer science 2023年第4期4卷 399页

作者： Lohith, C. Chandramouli, H. Balasingam, Udayabalan Arun Kumar, S. Department of Computer Science and Engineering Gopalan College of Engineering and Management Bengaluru India Department of Computer Science and Engineering East Point College of Engineering and Technology Bengaluru India Department of Information Science and Engineering East Point College of Engineering and Technology Bengaluru India Department of Computer Science and Engineering Nagarjuna College of Engineering and Technology Bengaluru India

This study presents a new approach for administering point-of-view-based assessment exams. The purpose of our ranking is to gain a sense of the best and most hilariously bad works of art. Given a collection of free-text clients, a specific object reviews. Our method begins with a handcrafted match to find viewpoints, use dependency courses in individual sentences. It then congregates by combining different notifications of a same location using measure of similarity based on the Word Net. Finally, it performs an each point of view obtains a score, which contributes to the overall score. A new assessment of a client’s reaction to a social occasion it is a part of it there is no need for any of that in our ideology express a seed word or space Data, as it merely makes use of an off-the-shelf word reference for evaluation we look at what makes people interested. Perceiving and rating points from online book reviews are the outcome of groundwork. We investigate the topic’s breadth. Diverse supply tendencies management approaches based on the multi sentence verification and interexamination work assessment of the rating are presented. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Aspect_modeling LDA Sentiment_analysis Topic_aspect_mapping Topic_modeling

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Adaptive Kernel Firefly Algorithm Based Feature Selection and Q-Learner Machine Learning Models in Cloud

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computer Systems science & engineering 2023年第9期46卷 2667-2685页

作者： I.Mettildha Mary K.Karuppasamy Department of Information Technology Sri Ramakrishna Engineering CollegeCoimbatoreTamilnaduIndia Department of Computer Science&Engineering RVS College of Engineering and TechnologyCoimbatoreTamilnaduIndia

CC’s(Cloud Computing)networks are distributed and dynamic as signals appear/disappear or lose ***(Machine learning Techniques)train datasets which sometime are inadequate in terms of sample for inferring information.A dynamic strategy,DevMLOps(Development Machine Learning Operations)used in automatic selections and tunings of MLTs result in significant performance ***,the scheme has many disadvantages including continuity in training,more samples and training time in feature selections and increased classification execution ***(Recursive Feature Eliminations)are computationally very expensive in its operations as it traverses through each feature without considering correlations between *** problem can be overcome by the use of Wrappers as they select better features by accounting for test and train *** aim of this paper is to use DevQLMLOps for automated tuning and selections based on orchestrations and messaging between *** proposed AKFA(Adaptive Kernel Firefly Algorithm)is for selecting features for CNM(Cloud Network Monitoring)*** methodology is demonstrated using CNSD(Cloud Network Security Dataset)with satisfactory results in the performance metrics like precision,recall,F-measure and accuracy used.

关键词： Cloud analytics machine learning ensemble learning distributed learning clustering classification auto selection auto tuning decision feedback cloud DevOps feature selection wrapper feature selection Adaptive Kernel Firefly Algorithm(AKFA) Q learning

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IM-EDRD from Retinal Fundus Images Using Multi-Level Classification Techniques

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Intelligent Automation & Soft Computing 2023年第1期35卷 567-580页

作者： M.P.Karthikeyan E.A.Mary Anita Department of Computer Science and Engineering R.M.K.Engineering CollegeChennai601206India Department of Computer Science and Engineering School of Engineering and TechnologyChrist UniversityBengaluru560029India

In recent years,there has been a significant increase in the number of people suffering from eye illnesses,which should be treated as soon as possible in order to avoid *** Fundus images are employed for this purpose,as well as for analysing eye abnormalities and diagnosing eye *** can be recognised as bright lesions in fundus pictures,which can be thefirst indicator of diabetic *** that in mind,the purpose of this work is to create an Integrated Model for Exudate and Diabetic Retinopathy Diagnosis(IM-EDRD)with multi-level *** model uses Support Vector Machine(SVM)-based classification to separate normal and abnormal fundus images at thefirst *** input pictures for SVM are pre-processed with Green Channel Extraction and the retrieved features are based on Gray Level Co-occurrence Matrix(GLCM).Furthermore,the presence of Exudate and Diabetic Retinopathy(DR)in fundus images is detected using the Adaptive Neuro Fuzzy Inference System(ANFIS)classifier at the second level of *** detection,blood vessel extraction,and Optic Disc(OD)detection are all processed to achieve suitable ***,the second level processing comprises Morphological Component Analysis(MCA)based image enhancement and object segmentation processes,as well as feature extraction for training the ANFIS classifier,to reliably diagnose ***,thefindings reveal that the proposed model surpasses existing models in terms of accuracy,time efficiency,and precision rate with the lowest possible error rate.

关键词： Retinal fundus images exudate diabetic retinopathy SVM ANFIS morphological component analysis

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A Review of Image Steganography Based on Multiple Hashing Algorithm

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computers, Materials & Continua 2024年第8期80卷 2463-2494页

作者： Abdullah Alenizi Mohammad Sajid Mohammadi Ahmad A.Al-Hajji Arshiya Sajid Ansari Department of Information Technology College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Computer Science College of Engineering and Information TechnologyOnaizah CollegesQassim56312Saudi Arabia

Steganography is a technique for hiding secret messages while sending and receiving communications through a cover *** ancient times to the present,the security of secret or vital information has always been a significant *** development of secure communication methods that keep recipient-only data transmissions secret has always been an area of ***,several approaches,including steganography,have been developed by researchers over time to enable safe data *** this review,we have discussed image steganography based on Discrete Cosine Transform(DCT)algorithm,*** have also discussed image steganography based on multiple hashing algorithms like the Rivest–Shamir–Adleman(RSA)method,the Blowfish technique,and the hash-least significant bit(LSB)*** this review,a novel method of hiding information in images has been developed with minimal variance in image bits,making our method secure and effective.A cryptography mechanism was also used in this *** encoding the data and embedding it into a carry image,this review verifies that it has been ***,embedded text in photos conveys crucial signals about the *** review employs hash table encryption on the message before hiding it within the picture to provide a more secure method of data *** the message is ever intercepted by a third party,there are several ways to stop this operation.A second level of security process implementation involves encrypting and decrypting steganography images using different hashing algorithms.

关键词： Image steganography multiple hashing algorithms Hash-LSB approach RSA algorithm discrete cosine transform(DCT)algorithm blowfish algorithm

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Improving Maritime Data: A Machine Learning-based Model for Missing Vessel Trajectories Reconstruction

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IEEE Transactions on Vehicular technology 2025年第6期74卷 9565-9576页

作者： Chen, Jin Liang, Maohan Peng, Chang Zhang, Jixin Huo, Shengxu Sanya Institute Hunan University of Science and Technology China Department of Civil and Environmental Engineering National University of Singapore Singapore Singapore College of Computer Science and Electronic Engineering Hunan University China School of Computer Science Hubei University of Technology China

Advancements in maritime satellite technology have significantly impacted the maritime industry, enhancing both communication and safety at sea. These technological improvements have enabled Automatic Identification Systems (AIS) to transmit data through robust maritime communication networks. However, despite these advancements, AIS data often contain significant missing data due to limitations in both devices and network coverage. To overcome these limitations, this research presents an innovative approach for reconstructing missing points. In this paper, we first extracted the geometric shape and motion characteristics of vessels and constructed a decision tree using an adaptive sparse constraint mechanism to classify four types of vessel trajectories. Then, the vessel classification findings serve as input and the vessel acceleration features are constructed using the heading and velocity features of the trajectories at both ends. Finally, We employ a bidirectional Gate Recurrent Unit (GRU) network to simultaneously train on historical trajectory data. The method progressively shortens the reconstructed trajectory, thereby enhancing the accuracy of the reconstruction. In this paper, comparative experiments are conducted with several algorithms across various dimensions, including algorithm performance, trajectory types, and navigation scenarios. It is indicated by the experimental results that the algorithm discussed in this study outperforms baselines. Significant methodological advancements in vessel trajectory reconstruction are represented by this study, which also shows promising potential for application in intelligent maritime transportation systems. © 1967-2012 IEEE All rights reserved.

关键词： Waterway transportation

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HMM-Based Secure Framework for Optical Fog Devices in the Optical Fog/Cloud Network

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Journal of Optical Communications 2023年第4期44卷 475-483页

作者： Sood, Sandeep K. Deep Singh, Kiran Department of Computer Science & Engineering Khalsa College of Engineering & Technology Amritsar India Department of Computer Science & Engineering Guru Nanak Dev University Regional Campus Punjab Gurdaspur India

In the present optical fog/cloud computing environment, optical line terminals and optical network units are used as the most promising optical fog devices (OFDs). The inherent characteristics of fog computing provide certain granted privileges to the attacker to hack devices and make them malicious. Also, existing security solutions generate false alarms that affect the performance of the underlying network. In this paper, a secure framework is proposed that not only predicts the malicious OFDs but also reduces false alarms. Hidden Markov model and intrusion detection system are used to detect the malicious one by computing the probability of shifting (POS) and then shift it to the virtual honeypot which is kept hidden by deploying it at the optical fog layer. In addition, it also reduces the generation of false alarm and logs all malicious activities for further analysis. In the experiment section, Python is used to validate the proposed framework. Further, HMM is simulated and tested in the MATLAB to reduce the false alarm rate. Results show that the proposed framework effectively reduces the false alarms and detects the malicious one and then shifts it onto the virtual honeypot efficiently. © 2019 Walter de Gruyter GmbH, Berlin/Boston.

关键词： Fog

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A Novel Machine Learning-Enhanced Swarm Intelligence Algorithm for Cost-Effective Cloud Load Balancing

A Novel Machine Learning-Enhanced Swarm Intelligence Algorit...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Satheesh, Nimmati Sakthivel, K. Psna College of Engineering and Technology Department of Computer Applications Tamil Nadu Dindigul India K.S Rangasamy College of Technology Department of Computer Science and Business Systems Tamil Nadu Tiruchengode India

ISBN: (纸本)9798331543617

Cloud computing refers to the instant accessibility of shared computer resources over the internet on-demand, thereby revolutionizing the way organizations deploy and manage their IT infrastructure. Efficient load balancing in a cloud environment scales up, utilizing resources with optimized cost-effectiveness. This paper introduces a new Machine Learning-guided Improved Swarm Intelligence Algorithm for reliable load balancing in cloud computing. Methodology It spans on three main elements. The first is the data center controller for the management of tasks. VM allocation uses an Improved Beetle Swarm Optimization Algorithm and one-dimensional convolutional probabilistic neural network is used for host load prediction. To propose an approach that looks to optimize the key metrics such as make span, average cost of VM, resource utilization, and value response time through iterative optimization process. The results of the experiments reflect the success of the proposed algorithm, because an improved performance of load balancing is obtained in comparison with those achieved using PSO, Grey Wolf Optimization, and Genetic Algorithm. The convergence diagram displays the iterative optimization process achieved by the proposed algorithm in obtaining a balance among various performance metrics. Overall, the methodology proposed in this work will provide promising facilitation in the increased efficiency, scalability, and cost-effectiveness of environments based on cloud computing with a reliable load balancing. © 2024 IEEE.

关键词： Genetic algorithms

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Enhanced DDoS Detection Using Advanced Machine Learning and Ensemble Techniques in Software Defined Networking

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computers, Materials & Continua 2024年第11期81卷 3003-3031页

作者： Hira Akhtar Butt Khoula Said Al Harthy Mumtaz Ali Shah Mudassar Hussain Rashid Amin Mujeeb Ur Rehman Department of Computer Science University of Management and TechnologySialkot51040Pakistan Department of Computer Science and Creative Technologies Global College of Engineering and TechnologyMuscat2546Sultanate of Oman Department of Computer Science University of WahWah Cantt47040Pakistan Department of Computer Science and IT University of ChakwalChakwal48800Pakistan

Detecting sophisticated cyberattacks,mainly Distributed Denial of Service(DDoS)attacks,with unexpected patterns remains challenging in modern *** detection systems often struggle to mitigate such attacks in conventional and software-defined networking(SDN)*** Machine Learning(ML)models can distinguish between benign and malicious traffic,their limited feature scope hinders the detection of new zero-day or low-rate DDoS attacks requiring frequent *** this paper,we propose a novel DDoS detection framework that combines Machine Learning(ML)and Ensemble Learning(EL)techniques to improve DDoS attack detection and mitigation in SDN *** model leverages the“DDoS SDN”dataset for training and evaluation and employs a dynamic feature selection mechanism that enhances detection accuracy by focusing on the most relevant *** adaptive approach addresses the limitations of conventional ML models and provides more accurate detection of various DDoS attack *** proposed ensemble model introduces an additional layer of detection,increasing reliability through the innovative application of ensemble *** proposed solution significantly enhances the model’s ability to identify and respond to dynamic threats in *** provides a strong foundation for proactive DDoS detection and mitigation,enhancing network defenses against evolving *** comprehensive runtime analysis of Simultaneous Multi-Threading(SMT)on identical configurations shows superior accuracy and efficiency,with significantly reduced computational time,making it ideal for real-time DDoS detection in dynamic,rapidly changing *** results demonstrate that our model achieves outstanding performance,outperforming traditional algorithms with 99%accuracy using Random Forest(RF)and K-Nearest Neighbors(KNN)and 98%accuracy using XGBoost.

关键词： Table 1(continued)OSI layer Possible DDoS attack Data link MAC Address Flooding Physical Cable disconnection JaMMING physical impersonation

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Secure Multi-Party Computation for Collaborative Data Analysis in Network Security 2

Secure Multi-Party Computation for Collaborative Data Analys...

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2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Tamilselvi, P. Lathika, V. Jayachitra, S. Arunkumar, S. Balasubramani, M. Kalaichelvi, V. Vels Institute of Science Technology and Advanced Studies Department of Computer Science and Engineering Chennai India Department of Cse Salem India Psna College of Engineering and Technology Department of Ece Dindigul India Coimbatore Institute of Engineering and Technology Department of Ece Coimbatore India Vsb Engineering College Department of Computer Science and Engineering Karur India Sastra Deemed to Be University Srinivasa Ramanujan Centre Department of Cse Kumbakonam India

ISBN: (纸本)9798350306415

Within the scope of this research, we provide a Secure Multi-Party Computation (SMPC) architecture that has been adapted specifically for the purpose of collaborative data analysis in network protection. Significant improvements have been made to the framework in terms of performance metrics, security, and applicability in the real world. When measured against a baseline, comparative analysis showed a notable reduction of 25% in execution time, a decrease of 20% in communication overhead, and a drop of 16.67% in computational complexity. These enhancements highlight the framework's efficiency advantages, which are vital for maximising responsiveness and scalability in collaborative efforts to improve network security. As a result of the security evaluation, which demonstrates a shift from moderate to high privacy preservation, strong resilience to assaults, and low sensitivity to parameters, the suggested framework has been shown to be reliable. Real-world application scenarios further emphasise its adaptability, showing that its influence on threat intelligence sharing is moderate, while its impact on collaborative incident response is significant, and its impact on vulnerability analysis is low. These findings, when taken as a whole, establish the SMPC framework as a useful resource for organisations that want to improve the effectiveness of their joint efforts to secure networked environments. The research places a strong emphasis on the practical importance of the framework, which helps to build enhanced trust, collaboration, and resilience against developing cyber threats. Assessments of scalability, improvements to cryptographic protocols, and interaction with newly developing technology are some potential future topics for study. Advanced threat analysis may also benefit. © 2024 IEEE.

关键词： Collaborative Data Analysis Cryptographic Protocols Network Security Privacy Preservation Secure Multi-Party Computation (SMPC)

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