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Cooperative Channel and Optimized Route Selection in Adhoc Network

Intelligent Automation & Soft Computing 2023年第5期36卷 1547-1560页

作者： D.Manohari M.S.Kavitha K.Periyakaruppan B.Chellapraba Department of Computer Science and Engineering St.Joseph’s Institute of TechnologyChennai600119TamilnaduIndia Department of Computer Science&Engineering SNS College of TechnologyCoimbatore641035TamilnaduIndia Department of Computer Science&Engineering SNS College of EngineeringCoimbatore641107TamilnaduIndia Department of Information Technology Karpagam Institute of TechnologyCoimbatore641032TamilnaduIndia

Over the last decade,mobile Adhoc networks have expanded dramati-cally in popularity,and their impact on the communication sector on a variety of levels is *** uses have expanded in lockstep with its *** to its instability in usage and the fact that numerous nodes communicate data concur-rently,adequate channel and forwarder selection is *** this proposed design for a Cognitive Radio Cognitive Network(CRCN),we gain the conﬁdence of each forwarding node by contacting one-hop and second level nodes,obtaining reports from them,and selecting the forwarder appropriately with the use of an optimization *** that point,we concentrate our efforts on their channel,selection,and lastly,the transmission of data packets via the designated *** simulation work is validated in this section using the MATLAB ***,steps show how the node acts as a conﬁdent forwarder and shares the channel in a compatible method to communicate,allowing for more packet bits to be transmitted by conveniently picking the channel between *** cal-culate the conﬁdence of the node at the start of the network by combining the reliability report for theﬁrst hop and the reliability report for the secondary *** then refer to the same node as the conﬁdent node in order to operate as a *** a result,we witness an increase in the leftover energy in the *** percentage of data packets delivered has also increased.

关键词： Adhoc Network conﬁdent forwarder one-hop optimized route selection secondary report channel selection

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Gradient-enhanced focal-pooling vision transformer with adaptive tuning for robust and accurate vehicle detection in smart environments

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Iran Journal of computer science 2025年 1-18页

作者： Ankireddy, Priyanka Gopalakrishnan, S. Reddy, V. Lokeswara Department of Computer Science and Engineering Hindustan Institute of Technology and Science Tamilnadu Chennai 603103 India Department of Information Technology Hindustan Institute of Technology and Science Tamilnadu Chennai 603103 India Department of Computer Science and Engineering KSRM College of Engineering Andhra Pradesh Kadapa 516003 India

Accurate and efficient vehicle detection is critical to the safety guarantee, traffic optimization, and intelligent transportation development. However, the strong detection under possible occlusion, dynamic illumination, and sizeable intra-class shape and size variation of the vehicles is still very challenging. Conventional object detection systems are not usually accurate in intricate cityscapes, as false alarms and undetected objects tend to render such systems unreliable. This work introduces a new deep learning model, i.e., focal-pooling vision transformer (FoPViT), to address such an issue by combining the advantages of focal transformers and pooling-based vision transformers. To a large degree, it helps integrate the gradient-aware pooling tuner (GAPT), a new mechanism capable of dynamically adjusting the sizes of pooling kernels about gradient signals produced during training. The adaptive policy facilitates the effective extraction of features over scales, thus maintaining fine details and correct detection for vehicles with different sizes and orientations. The innovation of the new model is that it is two-sided: on the one hand, focal attention excludes the non-vital areas, and on the other hand, GAPT optimizes spatial feature pooling for more accurate results with less computational cost. The proposed system expands the vehicle detection models’ functionality and provides a new standard for intelligent detection systems in moving scenes. The proposed model reflects improved accuracy in detecting vehicles. The experimental outcomes show that FoPViT is 98% accurate with precision, recall, and F1-score rates at 97.5%, 97.8%, and 98%, respectively. Regarding efficiency, the model processes inference in 15 ms and can be used in real time. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Deep learning Image processing Object detection Optimization Smart city Vehicle detection

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Implementing Machine Learning Techniques for Hair Health Prediction in Follicle Futures 2

Implementing Machine Learning Techniques for Hair Health Pre...

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2nd International Conference on Data science and Information System, ICDSIS 2024

作者： Duraisamy, Premkumar Vidhya, V. Preetha, T. Niranjani, V. Kpr Institute Of Engineering And Technology Department Of Computer Science And Engineering Coimbatore India Sri Eshwar College Of Engineering Department Of Computer Science And Engineering Coimbatore India

ISBN: (纸本)9798350381665

This study explores the effectiveness of various machine learning algorithms in forecasting hair health using a comprehensive dataset incorporating individual traits and lifestyle elements. Logistic regression, random forest, and AdaBoost were assessed, with random forest notably achieving a remarkable 94.6% accuracy. These results emphasize machine learning's potential for precise, non-invasive personalized hair health predictions. Key factors such as genetics, diet, and stress were pinpointed, informing targeted care approaches. The research underscores machine learning's promise in accurately anticipating hair loss and suggests future avenues for expanding datasets, integrating diverse data sources, and developing user-friendly tools for improved hair care management. © 2024 IEEE.

关键词： Support vector machines

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Adaptive solar power generation forecasting using enhanced neural network with weather modulation

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 10955-10968页

作者： Sujeeth, T. Ramesh, C. Palwe, Sushila Ramu, Gandikota Basha, Shaik Johny Upadhyay, Deepak Chanthirasekaran, K. Sivasankari, K. Rajaram, A. Department of CSE Siddartha Educational Academy Group of Institutions Andhra Pradesh Tirupati India Department of Mechanical Engineering M. Kumarasamy College of Engineering Karur India Department of Computer Engineering and Technology MIT-WPU Maharashtra India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Telangana Hyderabad India Andhra Pradesh Mylavaram India Department of Computer Science and Engineering Graphic Era hill univeristy Dehradun India Department of Electronics and Communication Engineering Saveetha Engineering College Chennai India Department of Electronics and Communication Engineering Akshaya College of Engineering and Technology Coimbatore India Department of Electronics and Communication Engineering E.G.S. Pillay Engineering College Nagapattinam India

Solar power generation forecasting plays a vital role in optimizing grid management and stability, particularly in renewable energy-integrated power systems. This research paper presents a comprehensive study on solar power generation forecasting, evaluating traditional and advanced machine learning methods, including ARIMA, Exponential Smoothing, Support Vector Regression, Random Forest, Gradient Boosting, and Physics-based Models. Moreover, we propose an innovative Enhanced Artificial Neural Network (ANN) model, which incorporates Weather Modulation and Leveraging Prior Forecasts to enhance prediction accuracy. The proposed model is evaluated using real-world solar power generation data, and the results demonstrate its superior performance compared to traditional methods and other machine learning approaches. The Enhanced ANN model achieves an impressive Root Mean Square Error (RMSE) of 0.116 and a Mean Absolute Percentage Error (MAPE) of 36.26%. The integration of Weather Modulation allows the model to adapt to changing weather conditions, ensuring reliable forecasts even during adverse scenarios. Leveraging Prior Forecasts enables the model to capture short-term trends, reducing forecasting errors arising from abrupt weather changes. The proposed Enhanced ANN model showcases its potential as a promising tool for precise and reliable solar power generation forecasting, contributing to the efficient integration of solar energy into the power grid and advancing sustainable energy practices. © 2024 – IOS Press.

关键词： Solar power generation

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An optimal detection of android malware using dynamic attention-based LSTM classifier

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Journal of Intelligent and Fuzzy Systems 2023年第1期44卷 1425-1438页

作者： Jebin Bose, S. Kalaiselvi, R. Department of Computer Science and Engineering Noorul Islam Centre for Higher Education Kumaracoil India Department of Computer Science and Engineering R.M.K College of Engineering and Technology Puduvoyal Gummidipoondi India

In today's world, Android has become the most significant and standard operating system for smartphones. The acceptance of the rapidly growing android system has outcome in a significant enhancement in the number of malware on comparing earlier days. There were several antimalware programs that are designed efficiently for protecting the sensitive data of the user in a mobile system from the occurrence of such attacks. Detection of malware system based on deep learning model along with the use of optimization technique is presented in this work. Initially, android malware dataset input is acquired and the normalization process is done. The feature selection is carried along with the optimization technique Recurrent Tuna Swarm Optimization. By this, an optimal selection of features can be attained. © 2023 - IOS Press. All rights reserved.

关键词： Android malware

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Secure Software Development Life Cycle: An Approach to Reduce the Risks of Cyber Attacks in Cyber Physical Systems and Digital Twins 1

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2nd International Conference on Computational Intelligence in Machine Learning, ICCIML 2022

作者： Seelaboyina, Radha Vadla, Sai Prakash Chary Teerthala, Sree Alekhya Pedduri, Veena Vani Department of Computer Science and Engineering Geethanjali College of Engineering and Technology Hyderabad India Department of Information Technology Geethanjali College of Engineering and Technology Hyderabad India

ISBN: (数字)9789819979547

ISBN: (纸本)9789819979530

Digital Twin is an implementation of a physical object or system into a digital space for efficient monitoring and better scalability. It is a virtual representation which acts as a digital counterpart for Cyber Physical Systems or processes in real-time and addresses every instance of its total life cycle. Nevertheless, it also has huge prospects even in the conventional and well-developed industries like agriculture, construction, medicine and automation. By large, it serves as a key example of how the Industry4.0 concept is revolutionizing the industries by facilitating vast numbers of opportunities and efficiencies in them. In this paper, we dig a deep dive into the various risks of cybersecurity associated with Cyber Physical Systems, Digital Twin technology and explore the opportunities of twins protecting themselves. Additionally, we look at the implementation of Secure Software Development Life-Cycle (SSDLC) on Digital Twins and Cyber Physical Systems for mitigating the proposed risks from the very roots. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cybersecurity

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Dynamics analysis and cryptographic implementation of a fractional-order memristive cellular neural network model

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Chinese Physics B 2024年第4期33卷 418-433页

作者：周新卫蒋东华 Jean De Dieu Nkapkop Musheer Ahmad Jules Tagne Fossi Nestor Tsafack 吴建华 Department of Information Engineering Gongqing CollegeNanchang UniversityJiujiang 332020China School of Computer Science and Engineering Sun Yat-Sen UniversityGuangzhou 511400China Department of Electrical Engineering and Industrial Computing University Institute of TechnologyDoualaCameroon Department of Computer Engineering Jamia Millia IslamiaNew Delhi 110025India Department of Physics Faculty of ScienceUniversity of YaoundeCameroon Electrical Engineering Department and Industrial Computing of ISTAMA University of DoualaDoualaCameroon

Due to the fact that a memristor with memory properties is an ideal electronic component for implementation of the artificial neural synaptic function,a brand-new tristable locally active memristor model is first proposed in this ***,a novel four-dimensional fractional-order memristive cellular neural network(FO-MCNN)model with hidden attractors is constructed to enhance the engineering feasibility of the original CNN model and its ***,its hardware circuit implementation and complicated dynamic properties are investigated on multi-simulation ***,it is used toward secure communication application *** it as the pseudo-random number generator(PRNG),a new privacy image security scheme is designed based on the adaptive sampling rate compressive sensing(ASR-CS)***,the simulation analysis and comparative experiments manifest that the proposed data encryption scheme possesses strong immunity against various security attack models and satisfactory compression performance.

关键词： cellular neural network memristor hardware circuit compressive sensing privacy data protection

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Transfer Function Based Feature Selection Using Harris Hawk Optimization for Biomedical Applications

Transfer Function Based Feature Selection Using Harris Hawk ...

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2024 International Conference on Smart Electronics and Communication Systems, ISENSE 2024

作者： Jayachitra, S. Prasanth, A. Prasannakiruba, G.S. Abbineni, Srichandana PSNA College of Engineering and Technology Department of Electronics and Communication Engineering Tamilnadu India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India Bannari Amman Institute of Technology Department of Computer Technology Sathyamangalam India Hyderabad India

ISBN: (纸本)9798331528126

Feature Selection plays key role to truncate the high dimensionality in the datasets through eradicating the unwanted and noisy attributes to improve the performance of classification. The Meta-heuristic technique is one of the most optimistic search techniques to travel the feature space to locate finest feature subset. The Harris Hawk Optimization (HHO) excited from hunting approach of hawk in nature. The cost effectiveness and simplicity formulates the algorithm prominent and superior in solving optimization problems. The binary version of the optimization is accountable for delineating continuous searching space to discrete feature space. Here, 6 transfer functions with S-Shaped and V-Shaped families were interposed and analyzed. Twelve benchmark datasets from UCI repository has been introduced to examine the transfer functions and choose the best feature pertaining to mitigate local minima and high speed of convergence. To analyze the efficacy of transfer function, the proposed method was compared over exiting algorithms and the proposed results attain higher classification accuracy in many of the datasets. © 2024 IEEE.

关键词： Heuristic methods

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Cnn models aided with a metaclassifier for lung Carcinoma classification using histopathological images

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Multimedia Tools and Applications 2024年 1-25页

作者： Gautam, Nandita Ghosh, Sohini Sarkar, Ram Department of Computer Science & amp Engineering Jadavpur University Kolkata India Department of Information Technology Netaji Subhash Engineering College Kolkata India

Lung cancer is a dangerous disease with differing treatment plans based on types and location of the cancerous cells. The overall 5-year survival rate for all stages of lung cancer is around 15%. People who smoke are at the highest risk of developing lung cancer. Early detection of lung cancer is crucial for starting early treatment and preventing the disease from spreading. Hence, it can improve people’s chances of survival. Imaging tests, such as a chest computed tomography (CT) scan, can detect lung cancer by providing a more detailed picture. However, the examination of chest CT scans is a challenging task and is prone to subject variability. For this, researchers have developed many computer-aided diagnostic (CAD) systems for the automatic detection of cancer using CT scan images. Misdiagnoses can occur in manual interpretation of images. An automated trained neural network on lung images from healthy and malignant lung cells helps lower the problem. Convolutional neural network (CNN)-based pretrained deep learning models have been used successfully to detect lung cancer. The accuracy of classification is significant to avoid false prediction. This research presents a metalearning based approach for identifying the common types of lung cancer tissues namely, Benign tissue, Squamous Cell Carcinoma, and Adenocarcinoma using LC25000 dataset. All the experiments have been conducted on a publicly available benchmark dataset for lung histopathological images. The features extracted from the penultimate layer (global average pooling) of the transfer learning-based CNN models, namely InceptionResNetV1, EfficientNetB7, and DenseNet121, have been fused together, and the dimensionality reduction has been applied to them before passing to the metaclassifier, which is the Support Vector Machine (SVM) classifier in our case. A quantitative analysis of the proposed algorithm has been conducted through classification accuracy and confusion matrix computation. When compared wit

关键词： Lung cancer

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Sensors Based Assistive technology for Corona Healthcare Insights 1

Sensors Based Assistive Technology for Corona Healthcare Ins...

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1st International Conference on Computational Intelligence for Security, Communication and Sustainable Development, CISCSD 2024

作者： Rajendra Prasad, K. Narasimhulu, K. N Santhosh Kumar, C.H. Hyderabad India Rajeev Gandhi College of Engineering and Technology Department of Computer Science and Engineering Nandyal India Anurag Engineering College Department of Computer Science and Engineering Telengana Kodad India

ISBN: (数字)9798350365405

ISBN: (纸本)9798350365405

Nano-assistive technology is essential to scientific study in the medical sciences. It offers more substantial options for examining macro-ailments, such as the coronavirus illness (COVID-19). Nanotechnology can sense intrusive sensing of the macro-molecule size of nano-sensors and analyze antibody systems for COVID-19 patients during a pandemic. Traditional big data clustering methods, such as single-pass k-means (spkm), and mini-batch k-means (mmbk), can classify gene data based on molecule similarity structures. However, they suffered with the initial assessment of gene cluster tendency. This paper presents the novel similarity method, Multiview modeling cosine similarities (MMCS), for a better assessment of cluster tendency, combined with the visual technique of visual assessment of cluster tendency (VAT) for the clustering results. The invasive sensors collect data on the internal gene structure of the patient, and the proposed MMCS-VAT makes it easy to make visual classifications. There was a big change in the area under the curve (AUC) of the performance parameters in the experiment with COVID datasets. MMCS-VAT made the AUC better than mbkm by 22-23% and better than spkm and mbkm by 10-16% for all five performance parameters. It also proved that MMCS-VAT is 8 to 10 times and 10 to 15 times faster than spkm, and mbkm methods respectively for the big COVID 19 datasets. © 2024 IEEE.

关键词： COVID-19

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