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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Ashok, Kumar K. Anathajothi, K. Kcg College of Engineering Department of Computer Science and Engineering Karapakkam India Rajalakshmil Engineering College Department of Computer Science and Engineering Thandalam India

ISBN: (数字)9798350384277

ISBN: (纸本)9798350384277

The preface of Privacy based decentralized application and massive information Analytics has been illustrate the consequence of block chain tools to the industry. Blockchain skill as a policy allows creating a scattered and mock ledger of transactions. Blockchain provide a promise of tamper conflict used for the data generate from various process to assemble enormous datasets. In view of the fact that the data is colossal in mass and grows exponentially by means of point in time, the data management should be proficient to stock up or course of action it. We survey the unusual standards of Merkle Tree Generation works in numerous research instructions for bring the blockchain implementation more rapidly to the variety of domain of database and its operation. in addition, the projected research work also discusses regarding the privacy, challenges, and future research direction by applying Merkle Tree Generation involves Data Slicing and Encryption, Generating Hash value. © 2024 IEEE.

关键词： Differential privacy

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A Novel Fuzzy Marine White Shark Optimization Based Efficient Routing and Enhancing Network Lifetime in MANET

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WIRELESS PERSONAL COMMUNICATIONS 2023年第4期132卷 2363-2385页

作者： Devi, K. Lalitha Bhat, C. Rohith Devi, K. Lalitha KSR College of Engineering Tiruchengode Namakkal India Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai Tamil Nadu 602105 India Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India

A mobile ad hoc network (MANET) is an independent wireless temporary network established by employing a set of mobile nodes (i.e. laptops, smartphones, iPods, etc.) appropriate for the environment in which the network infrastructures are not fixed. The most common problems faced by MANET are energy efficiency, high energy consumption, low network lifetime as well as high traffic overhead which create an impact on overall network topology. Hence, it is necessary to provide an energy-effective CH election to take steps against such issues. Therefore, this paper proposes a novel model to enhance the network lifetime and energy efficiency by performing a routing strategy in MANET. In this paper, an optimal CH is selected by proposing a novel Fuzzy Marine White Shark optimization (FMWSO) algorithm which is obtained by integrating fuzzy operation with two optimization algorithms namely the marine predator algorithm and white shark optimizer. The proposed approach comprises three diverse stages namely Generation of data, Cluster Generation and CH selection. A novel FMWSO algorithm is proposed in such a way to determine the CH selection in MANET thereby enhancing the network topology, network lifetime and minimizing the overhead rate, and energy consumption. Finally, the performance of the proposed FMWSO approach is compared with various other existing techniques to determine the effectiveness of the system. The proposed FMWSO approach consumes minimum energy of 0.62 mJ which is lower than other approaches.

关键词： MANET Cluster head Fuzzy Marine White Shark Cluster generation Network lifetime Energy consumption

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Weed Classification Using Particle Swarm Optimization and Deep Learning Models

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computer Systems science & engineering 2023年第1期44卷 913-927页

作者： M.Manikandakumar P.Karthikeyan Department of Computer Science and Engineering Thiagarajar College of EngineeringTamil NaduIndia Department of Information Technology Thiagarajar College of EngineeringTamil NaduIndia

Weed is a plant that grows along with nearly allfield crops,including rice,wheat,cotton,millets and sugar cane,affecting crop yield and *** and accurate identification of all types of weeds is a challenging task for farmers in earlier stage of crop growth because of *** address this issue,an efficient weed classification model is proposed with the Deep Convolutional Neural Network(CNN)that implements automatic feature extraction and performs complex feature learning for image *** this work,weed images were trained using the proposed CNN model with evolutionary computing approach to classify the weeds based on the two publicly available weed *** Tamil Nadu Agricultural University(TNAU)dataset used as afirst dataset that consists of 40 classes of weed images and the other dataset is from Indian Council of Agriculture Research–Directorate of Weed Research(ICAR-DWR)which contains 50 classes of weed *** effective Particle Swarm Optimization(PSO)technique is applied in the proposed CNN to automa-tically evolve and improve its classification *** proposed model was evaluated and compared with pre-trained transfer learning models such as GoogLeNet,AlexNet,Residual neural Network(ResNet)and Visual Geometry Group Network(VGGNet)for weed *** work shows that the performance of the PSO assisted proposed CNN model is significantly improved the success rate by 98.58%for TNAU and 97.79%for ICAR-DWR weed datasets.

关键词： Deep learning convolutional neural network weed classification transfer learning particle swarm optimization evolutionary computing Algorithm 1:Metrics Evaluation

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A Hybrid Approach for Diabetic Retinopathy Classification Using EfficientNetB5 and ResNet50 Models 2

A Hybrid Approach for Diabetic Retinopathy Classification Us...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Abirami, A. Lakshmi Priya, S. Lekshmi Prriya, T. Madhanraj, J. Computer Science and Engineering Easwari Engineering College Chennai India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505745

Diabetic retinopathy (DR), a severe diabetes-related eye complication that can result in blindness if left untreated, progressively damages retinal blood vessels with varying degrees of severity. Traditional screening methods predominantly rely on manual expert evaluation, which can be time-intensive, prone to human error, and subject to interpretation variability due to inconsistent image quality. To address these challenges, our research presents an innovative hybrid deep learning solution that seamlessly combines EfficientNetB5 and ResNet50 architectures for automated DR classification. This advanced model strategically harnesses EfficientNetB5 superior feature extraction capabilities alongside ResNet50 sophisticated deep layer processing power to accurately classify DR into mild, moderate, severe, and proliferative stages. Through extensive training on a comprehensive dataset of retinal fundus images, our hybrid approach demonstrates significant improvements in early detection accuracy, reduced diagnostic inconsistency, and enhanced reliability compared to conventional methods. The results validate that this integrated approach provides healthcare professionals with a robust, efficient, and reliable automated screening tool for clinical applications, ultimately addressing the current limitations in DR diagnosis and management while potentially improving patient outcomes through earlier intervention. © 2025 IEEE.

关键词： Electrotherapeutics

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Leveraging Satellite Imagery to Analyze Large-Scale Economic Trends

Leveraging Satellite Imagery to Analyze Large-Scale Economic...

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2024 International Conference on science, Technology, engineering and Management, ICSTEM 2024

作者： Lavanya, Raja Indira, K. Santhiya, C. Chakkaravarthy, G. Vinoth Abhinav, S. Department of Computer Science and Engineering Thiagarajar College of Engineering Madurai India Department of Computer Science and Engineering Velammal College of Engineering Madurai India

ISBN: (纸本)9798350376913

Poverty is still one of the factors that have been affecting the lives of humans over hundreds of decades in the world but still, it is not entirely eradicated. One of the main reasons is the lack of identification of the economical pattern in a particular area that has been affected by poverty over a long period which has not been noted by any NGOs or government. So, that is the main reason why poverty eradication is still the first goal of UN sustainable development goals.[1] Understanding economical patterns over a region can lead us in providing informed policy-making, targeted NGO, and government-aided efforts. If we could track them down in an easy method at the earlier stage or in the advanced method and predict this fatal enigma on the region, the poverty can surely prevent or at least save lives. The above dilemma leads us to the essential method of tracking the reliable and timely measurements of economic activities, which are key for understanding economic development and designing government policies. But still many countries, especially developing countries, lack reliable data. Though data is available, the lack of quality in the data remains a huge challenge. In this paper, we propose a low-cost yet efficient approach to predict the economical pattern in a rural region from satellite imagery, which is globally available, in the meantime, satellite images are continuously updating to the changeable environmental conditions. Since the satellite image is way more advantageous than traditional methods, they play a huge role in the model. Traditional methods like surveys are expensive to conduct and include a lot of manpower. [2]This contributes to infer socioeconomic indicators from large-scale, remotely-sensed data. The available dataset is used to extract the luminous intensity from the night-time satellite images and added along with the other features of the particular region that determines the socio- economic conditions. Then with machine learning al

关键词： Forecasting

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Streamlining Financial Predictions: A Web Application for Stock, Cryptocurrency, and Market Sentiment Analysis 4

Streamlining Financial Predictions: A Web Application for St...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Vivekanand, Chettiyar Vani Kumar, P. Amizhthini, A. Rajalakshmi Engineering College Computer Science and Engineering Chennai India

ISBN: (纸本)9798331523923

Among the high-degree lines of study in this area, one of the most interesting ones is stock price prediction, particularly in the context of machine learning and time series forecasting models. The traditional past ones include ARIMA, and SARIMAX, while deep learning models have been recently applied for predicting the stock prices based upon historical data. However, these models do hold much promise and also come with volatility and non-linearity in financial markets. Causes for such inaccuracies in predictions include sudden market shifts, external economic events, and investor sentiment. All these factors are difficult to model well. This study will propose a web application that uses advanced time series models to try and overcome such challenges. The system is implemented with Streamlit and Flask, so it gives users a smooth interface. It has five critical modules: Nifty 50, which deals with the analysis and forecasting of stocks using ARIMA and SARIMAX models;Global Market Data, which fetches real-time data from the Yahoo Finance API for granular analysis;Cryptocurrency, which relies heavily on Ethereum data with historical analysis and price forecasting;Twitter Sentiment Analysis, which focusses on real-time analysis of market sentiment through tweets;and the prediction module, which evaluates the accuracy of the models using Root Mean Square Error to measure prediction quality. This solution will provide a straightforward yet potent platform for analyzing stocks, cryptocurrency, and sentiment in real-time. The goal is to raise precision, reliability, and overall user experience concerning financial prediction, while more valuable insights become available to the investor and the researcher, having multiple sources integrated into the model. © 2025 IEEE.

关键词： Ethereum

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Event-based nonsingular fixed-time containment control for nonlinear multiagent systems with dynamic uncertainties

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science China(Information sciences) 2025年第5期68卷 413-414页

作者： Yuanbo SU Qihe SHAN Tieshan LI C.L.Philip CHEN Navigation College Dalian Maritime University School of Automation Engineering University of Electronic Science and Technology of China School of Computer Science and Engineering South China University of Technology

Owing to the extensive applications in many areas such as networked systems,formation flying of unmanned air vehicles,and coordinated manipulation of multiple robots,the distributed containment control for nonlinear multiagent systems (MASs) has received considerable attention,for example [1,2].Although the valued studies in [1,2] investigate containment control problems for MASs subject to nonlinearities,the proposed distributed nonlinear protocols only achieve the asymptotic *** a crucial performance indicator for distributed containment control of MASs,the fast convergence is conducive to achieving better control accuracy [3].The work in [4] first addresses the backstepping-based adaptive fuzzy fixed-time containment tracking problem for nonlinear high-order MASs with unknown external ***,the designed fixedtime control protocol [4] cannot escape the singularity problem in the backstepping-based adaptive control *** is well known,the singularity problem has become an inherent problem in the adaptive fixed-time control design,which may cause the unbounded control inputs and even the instability of controlled ***,how to solve the nonsingular fixed-time containment control problem for nonlinear MASs is still open and awaits breakthrough to the best of our knowledge.

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Smart Portable Device for Trekking People in Forest 2

Smart Portable Device for Trekking People in Forest

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2nd International Conference on computer, Communication and Control, IC4 2024

作者： Gowri, B. Shyamala Rajalakshmi, B. Rohith, R.V. Roshan, P. Ram Prasath, V. Easwari Engineering College Computer Science and Engineering India

ISBN: (纸本)9798350387933

The increasing concern regarding animal attacks within rural communities and among forestry workers highlights the pressing need for effective wild animal tracking systems. Surveillance cameras and drones have become standard tools for monitoring in response to this challenge. However, ensuring the safety of individuals in these areas still requires precise location data, tracking of animal movements, and accurate animal detection. While traditional computer vision and machine learning techniques have shown promise, their practical implementation has been hindered by their cost and complexity. To address this, this study introduces an innovative approach: a hybrid model that combines the Visual Geometry Group (VGG)-19 architecture with bidirectional long short-term memory (Bi-LSTM) networks. Beyond animal identification, this approach generates real-time alerts based on animal behavior, swiftly transmitting them to the nearby forest office via SMS for immediate action. By integrating these systems, the risk of wildlife encounters is reduced, and safety measures in forested regions are significantly enhanced. Importantly, the hybrid model exhibits outstanding real-time data processing capabilities with remarkable performance metrics, including an impressive accuracy of 98%, a mAP of 77%, and an exceptional FPS rate of 170. Rigorous testing was conducted using a dataset comprising 40,000 images from 25 distinct animal classes, consistently demonstrating precision and accuracy levels exceeding 97.9%. In summary, the 'hybrid VGG-19 and Bi-LSTM model' offers a reliable and efficient solution for addressing animal attacks, safeguarding lives, and enabling rapid communication with local authorities, ultimately improving safety and minimizing the harm caused by encounters with wildlife. © 2024 IEEE.

关键词： Cameras

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Exploring Multi-Task Learning for Forecasting Energy-Cost Resource Allocation in IoT-Cloud Systems

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computers, Materials & Continua 2024年第6期79卷 4603-4620页

作者： Mohammad Aldossary Hatem A.Alharbi Nasir Ayub Department of Computer Engineering and Information College of EngineeringPrince Sattam Bin Abdulaziz UniversityWadi Al-Dawasir11991Saudi Arabia Department of Computer Engineering College of Computer Science and EngineeringTaibah UniversityMadinah42353Saudi Arabia Department of Creative Technologies Air University IslamabadIslamabad44000Pakistan

Cloud computing has become increasingly popular due to its capacity to perform computations without relying on physical infrastructure,thereby revolutionizing computer ***,the rising energy consumption in cloud centers poses a significant challenge,especially with the escalating energy *** paper tackles this issue by introducing efficient solutions for data placement and node management,with a clear emphasis on the crucial role of the Internet of Things(IoT)throughout the research *** IoT assumes a pivotal role in this study by actively collecting real-time data from various sensors strategically positioned in and around data *** sensors continuously monitor vital parameters such as energy usage and temperature,thereby providing a comprehensive dataset for *** data generated by the IoT is seamlessly integrated into the Hybrid TCN-GRU-NBeat(NGT)model,enabling a dynamic and accurate representation of the current state of the data center *** the incorporation of the Seagull Optimization Algorithm(SOA),the NGT model optimizes storage migration strategies based on the latest information provided by IoT *** model is trained using 80%of the available dataset and subsequently tested on the remaining 20%.The results demonstrate the effectiveness of the proposed approach,with a Mean Squared Error(MSE)of 5.33%and a Mean Absolute Error(MAE)of 2.83%,accurately estimating power prices and leading to an average reduction of 23.88%in power ***,the integration of IoT data significantly enhances the accuracy of the NGT model,outperforming benchmark algorithms such as DenseNet,Support Vector Machine(SVM),Decision Trees,and *** NGT model achieves an impressive accuracy rate of 97.9%,surpassing the rates of 87%,83%,80%,and 79%,respectively,for the benchmark *** findings underscore the effectiveness of the proposed method in optimizing energy efficiency and enhancing the predictive

关键词： Cloud computing energy efficiency data center optimization internet of things(IoT) hybrid models

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Integrated Privacy Preserving Healthcare System Using Posture-Based Classifier in Cloud

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Intelligent Automation & Soft Computing 2023年第3期35卷 2893-2907页

作者： C.Santhosh Kumar K.Vishnu Kumar Department of Computer Science and Engineering Priyadarshini Engineering College635751TamilnaduIndia Department of Computer Science and Engineering KPR Institute of Engineering&TechnologyCoimbatore641407India

Privacy-preserving online disease prediction and diagnosis are critical issues in the emerging edge-cloud-based healthcare *** patient data pro-cessing from remote places may lead to severe privacy ***,the existing cloud-based healthcare system takes more latency and energy consumption during diagnosis due to ofﬂoading of live patient data to remote cloud *** the privacy *** proposed research introduces the edge-cloud enabled privacy-preserving healthcare system by exploiting additive homomorphic encryption *** can help maintain the privacy preservation and conﬁdentiality of patients’medical data during diagnosis of Parkinson’s *** addition,the energy and delay aware computational ofﬂoading scheme is proposed to minimize the uncertainty and energy consumption of end-user *** proposed research maintains the better privacy and robustness of live video data processing during prediction and diagnosis compared to existing health-care systems.

关键词： Peer-to-peer computing energy and delay aware ofﬂoading edge-cloud enabled healthcare system parkinson’s disease prediction

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