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International Conference on Parallel Distributed and Grid Computing (PDGC)

作者： D. saraswathi A. Murugavel M. Malathi Irina Pavlenko shanmuga Priya s Gourav Kalra Department of Computer Science PSG College of Arts & Science Coimbatore Tamilnadu India Department of Computer Science and Engineering V. S. B. Engineering College (An Autonomous Institution) Karur Tamilnadu India Department of Mathematics Bannari Amman Institute of Technology Erode Tamilnadu India Department of Enterprise Economics Regional and Personnel Management Kuban State University Krasnodar Russia Department of Artificial Intelligence and Data Science Saveetha Engineering College Chennai Tamilnadu India Department of Mechanical Engineering Maharishi Markandeshwar (Deemed to be University) Mullana Haryana India

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

Job role recommendation systems are essential for effectively connecting job searchers with possible employment prospects, particularly in aligning candidates' talents with job needs. Conventional methods, such support vector Machines (svM) and Logistic Regression, demonstrate limitations in addressing non-linear connections and extensive, unbalanced datasets. This study presents a skill-oriented job position recommendation system employing the XGboost model, a sophisticated gradient-boosted decision tree technique. The suggested system effectively manages extensive datasets, executes feature extraction from skill vectors, and enhances matching precision using a non-linear methodology. Experimental findings demonstrate that the suggested model attains an accuracy of 92.3%, markedly surpassing current methodologies, such as svM (89.5%) and Logistic Regression (86.1%). The suggested model achieves an F1 score of 0.91 and a recall of 0.90, demonstrating its effectiveness in reducing false positives and false negatives. This study presents an innovative resolution to the issue of skill-based job matching, with applicable implications in recruiting, talent acquisition, and job recommendation systems. Future endeavours may concentrate on refining feature extraction techniques and integrating more layers in neural networks to further enhance scalability and accuracy.

关键词： support vector machines Training Logistic regression Accuracy Computational modeling scalability Feature extraction vectors Recommender systems standards

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Machine Learning-Driven Hybrid Adaptive beamforming for Efficient Interference Mitigation in Dense Wireless sensor Networks

Machine Learning-Driven Hybrid Adaptive Beamforming for Effi...

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2025 International Conference on visual Analytics and Data visualization, ICvADv 2025

作者： Loganathan, P. Priyeshkumar, A.T. Kodeeswari, K. Reena, K Jose Poonguzhali, I. balaji, M. Department of Electronics and Communication Engineering Namakkal India Mahendra College of Engineering Department of Electronics and Communication Engineering Salem India Department of Electronics and Communication Engineering Erode India B. S. Abdur Rahman Crescent Institute of Science and Technology Department of Computer Applications Vandalur India Panimalar Engineering College Department of Electronics and Communication Engineering Chennai India Vivekananda College of Technology for Women Department of Electronics and Communication Engineering Tiruchengode India

ISBN: (纸本)9798331521394

The Adaptive beamforming Algorithm (AbA) represents a promising approach to suppress interference in dense wireless sensor networks (WsNs), enhancing signal fidelity and network throughput. In dense WsNs, the proximity of sensor nodes leads to high interference levels, which degrade energy efficiency and communication reliability. This is exacerbated by static beamforming methods that oversimplify time and frequency utilization. This work proposes a set of adaptive beamforming solutions, including digital, machine learning (ML)-empowered, and hybrid beamforming approaches. The Adaptive Array signal Processing (AAsP) method introduces real-time interference analysis with null steering to suppress unwanted signals and maximize the signal-to-interference-plus-noise ratio (sINR). Hybrid beamforming, combining analog and digital domains, balances computational cost with performance. Additionally, a deep learning-based beam steering module dynamically adjusts beam patterns based on interference forecasts. Extensive simulations demonstrate the proposed methods' superiority over conventional beamforming, achieving significant sINR improvement, enhanced network throughput, and improved energy efficiency. In ultra-dense environments, the results indicate a 10-fold reduction in interference power alongside better scalability. Proactive interference mitigation using ML models further ensures adaptability to varying network conditions. These techniques establish a baseline for deploying adaptive beamformers in dense WsNs, addressing scalability and real-time computational challenges. They also pave the way for implementing advanced 5G/6G-capable sensor systems, enabling resilient communication in high-interference environments. Key-words: signal to interference plus noise ratio(sINR), adaptive beamforming, interference mitigation, hybrid beamforming, null steering, beam steering, computational efficiency. © 2025 IEEE.

关键词： beamforming

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A Novel Approach to Feature Extraction in MI - based bCI systems

A Novel Approach to Feature Extraction in MI - Based BCI Sys...

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International Conference on Computation system and Information Technology for sustainable solutions (CsITss)

作者： Arun Kumar s L Anand Anil Kannur Department of Computer Science and Engineering School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of Networking and Communications School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of Computer Science and Engineering BLDEA'S V.P. Dr. P.G. Halakatti College of Engineering and Technology Vijayapura

ISBN: (数字)9798331505462

ISBN: (纸本)9798331505479

brain-computer interfaces based on motor imagery (MI-bCI) enable communication between the brain and the outside world. It accomplishes this by capturing and examining the EEG data generated when a person visualizes moving a certain limb. based on this investigation; to enhance categorization precision, we enhance the attribute extraction process. Once the EEG signals have been pre-processed and deconstructed into distinct bands of frequencies, we apply similar spatial algorithms for every sub-band. After that, we use blind source separation (bss) and spatial filter techniques to extract attributes directly from the sources of the brain instead of only from channels. Ultimately, by choosing the most relevant attributes as inputs for the categorization stage, we distinguish between the envisioned movements. To assess our recommended approach, we utilized data set Iva from the bCI competition III. The outcomes from diverse experiments indicate that the system achieves an average accuracy of 98.8%, with 100% sensitivity across all subjects. These results illustrate that the suggested approach effectively improves MI-based bCI.

关键词： Training sensitivity Accuracy system performance spatial filters Electroencephalography brain-computer interfaces blind source separation Reliability standards

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9T sRAM Cell for Computation-In-Memory Architectures: Proposal & Investigation

9T SRAM Cell for Computation-In-Memory Architectures: Propos...

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Devices for Integrated Circuit (DevIC)

作者： Aman Kumar Gupta Prashant Joshi shobhit srivastava sourabh Panwar Rajendra singh shekhawat Abhishek singh Kilak shivendra Yadav Deepak Joshi Abhishek Acharya Department of Electronics Engineering S. V. National Institute of Technology Surat India Electronics & Communication Engineering Govt. Polytechnic College Bikaner India Electronics & Communication Engineering Govt. Engineering College Bikaner India Computer Science Engineering Govt. Engineering College Bikaner India

by keeping data-intensive applications into consideration, we have investigated the robustness of digital logic circuit design for Computation In-Memory (CiM) using 9T sRAM cells at 22 nm technology nodes. PvT analysis (Process corner, voltage, and Temperature) is also presented to check the robustness of the circuit. We have proposed a latch-based sense amplifier and voltage reference for 9T sRAM-based CiM architecture. The proposed design is good for demonstrating the logical operation with the memory. A comparison of 250 nm technology against 22 nm is also demonstrated in this work for 9T sRAM-based CiM.

关键词： Temperature sensors Monte Carlo methods computer architecture voltage sRAM cells Robustness system-on-chip

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Artificial Neural Network (ANN) With Chan-vese(Cv) Algorithm-based Plant Disease Detection And Classification

Artificial Neural Network (ANN) With Chan-Vese(CV) Algorithm...

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Applied Intelligence and sustainable Computing (ICAIsC), International Conference on

作者： Avr Mayuri v Rashmi bappaditya Das Amitabha Mandal s. Neelakandan School of Computing Science and Engineering VIT Bhopal University Bhopal India Department of Information Technology Prasad V. Potluri Siddhartha Institute of Technology Vijayawada India Department of computer science & engineering Dr. B. C. Roy engineering college Durgapur India Department of computer science & engineering R.M.K Engineering College Kavaraipettai India

A country’s economy is influenced by a wide range of factors, with the agriculture, service, and food processing sectors. In our country, agriculture is one of the most significant economic sectors. The agriculture sector, however, has several difficulties, such as the different climatic conditions in other regions of our country that give rise to various infectious diseases in different plant species, resulting in a significant drop in crop productivity and income generation. Early and accurate plant disease diagnosis is crucial in improving the foundation for harvest formation. The traditional method of plant disease detection through eye observation is unsuccessful since it necessitates knowledge. Machine learning (ML) techniques are being utilized to identify and categorize plant ailments to address this issue. In this work, plant diseases can be classified using machine learning methods. These involve classifier training, classification, and stages like dataset construction, image loading, pre-processing, segmentation, and feature extraction. An artificial neural network is initially used to distinguish and find leaves in a challenging environment. The Chan-vese (Cv) technique states that symptoms can be found in images that have been segmented depending on the outcomes of the ANN algorithm. A transfer learning model trained on a dataset of ill leaves on an essential background is then given the segmented leaves. The model is also checked for rust, black rot, and bacterial plaque conditions. The findings demonstrate that the methodology is more accurate—98.22%—than the previous approach, minimizing the adverse effects of illness on agricultural productivity and promoting long-term agrarian growth. Utilizing data from Plantvillage, the model is trained and validated. The dataset includes images of plant diseases afflicting pepper, tomato, and potato plants. Fifteen different plant disease categories are used to train and evaluate the classifier. The results of t

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Enhanced Fire Detection for Industrial and Environmental safety Using IoT-Integrated vision sensors and Residual Clutch Attention Network and Depthwise Distinguishable Convolutional Neural Networks

Enhanced Fire Detection for Industrial and Environmental Saf...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： D.Christy sujatha Priyanshu singh C. vallikannu Ravi varman skanda M G T. Gavaskar Department of Software Engineering Periyar Maniammai Institute of Science & Technology Thanjavur Tamil Nadu India Department of Computer Science and Engineering R.V college of engineering Bangalore Karnataka India Department of Humanities and Sciences Rajalakshmi Engineering College Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Karpagam academy of Higher Education Coimbatore Tamil Nadu India Department of Industrial and Production Engineering SJCE JSS Science and Technology University Mysore Karnataka India Department of Mechanical Engineering St. Joseph's College of Engineering Chennai Tamil Nadu India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Fire detection is an important factor in improved safety in industrial and environmental conditions. The conventional flame detectors, largely relying on heat and smoke detectors, have drawbacks of time lag, low sensitivity, and low coverage when compared with our requirement in large industrial scenes and vast natural terrains vulnerable to fire occurrence. These systems are highly unsuitable for dynamic and high-risk situations as they require a higher accuracy and a faster reaction time for the alarms to be efficient as is now the case due to the aforementioned reasons. In an attempt to remedy these limitations, this research introduces a fire detection system using IoT and vision sensors that combines a RCAN with DDCNN. This architecture is tuned using the Crayfish Optimization Algorithm (COA), which seeks to improve model parameter to improve response time and accuracy. by incorporating feedbacks from sensors placed at its different parts and reading from the environment, the RCAN-DDCNN model can quickly identify the fire patterns and respond by activating the sprinkler systems as well as setting off alarms. The proposed system presents a superior option for fire detecting because it is scalable and adaptive and it provides high degree of precision, making it an optimal development in fire detection technology for industry and environment. The introduced approach attains higher accuracy as 99%.

关键词： support vector machines Accuracy vision sensors sensor systems stability analysis safety Convolutional neural networks Time factors Optimization smoke detectors

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sentiment Analysis Using an Ensemble Approach on Flipkart societal Media Data

Sentiment Analysis Using an Ensemble Approach on Flipkart So...

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IEEE students' Conference on Electrical, Electronics and computer science (sCEECs)

作者： Neetesh Kumar Nema vivek shukla s. R. Tandan Amit Pimpalkar Rohit Miri vishnukant soni Dr. C. V. Raman University Bilaspur C.G. India Department of Computer Science and Engineering Dr. C. V. Raman University Bilaspur C.G. India Department of Computer Science Government R. V. R. S. Kanya Mahavidyalaya Kawardha C.G. India Department of Computer Science and Engineering (AIML) Shri Ramdeobaba College of Engineering and Management Nagpur India Department of Computer Science and Engineering UTD CSVTU Bhilai C.G. India

The analysis of sentiments, especially with social media data, has become increasingly important over the years. It offers insights into opinions, consumer behaviour and societal trends. However, existing methods for sentiment analysis primarily focus on identifying negative sentiments rather than categorizing the complex range of distinct and diverse emotions that humans experience. This study introduces an approach to categorizing sentiments in social media data to overcome the limitations of existing methods and enhance the accuracy of sentiment classification. by filling this gap in research, our proposed method provides an understanding of the sentiments expressed on the Flipkart E-commerce website. Our research effectively addresses existing limitations through an ensemble learning model and achieves an impressive 99% accuracy on our test dataset.

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IoT-based solar Panel Tracking system to Enhance the Output Power 3

IoT-based Solar Panel Tracking System to Enhance the Output ...

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3rd International Conference on smart Electronics and Communication, ICOsEC 2022

作者： Mani, P.K. sunagar, Prashant Madhuri, N.s. Rajah, A. Joseph Lourdu Ramya, D. Kathir, I. Vel Tech Multitech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai600062 India M S Ramaiah Institute of Technology Department of Civil Engineering Karnataka Bengaluru560054 India SreeVidyanikethan Engineering College Department of Electrical and Electronics Engineering Andhra Pradesh Tirupati517102 India Psna College of Engineering and Technology Department of English Tamil Nadu Dindigul624622 India Sathyabama Institute of Science and Technology Department of Electrical and Electronics Engineering Tamil Nadu Chennai600119 India V.S.B. Engineering College Department of Electrical & Electronics Engineering Tamil Nadu Karur639111 India

ISBN: (数字)9781665497640

ISBN: (纸本)9781665497640

In today's world, power takes an important role in day-to-day life. Power is generated by using different natural resources like light, wind, and water. From the sun, light energy is converted into power with the use of solar panels. Wind energy is converted into power by using a windmill. Hydro energy from water can be converted into power. so, power can be generated with the use of natural resources. by using the solar panel, the light emitting from the sun is converted into power and that power can be used for many purposes. solar panels can be seen in tall buildings like schools, colleges, offices, and industries. And solar panels can be implemented where the sunlight falls like a terrace in houses, terraces in buildings, and can be on Highway roads. but the sunlight cannot be falling all the time at the same place where the solar panel is implemented in a stable state. so, to observe the light from the sun all the time, solar Panel Tracking system is implemented based on the domain of the Internet of Things (IoT). With the use of Light Dependent Resistors (LDR), voltage sensor, Current sensor, servo motor, NodeMCU, and Thingspeak Cloud is applied for solar Panel Tracking system. LDR senses the light from the sun, the servo motor is used to rotate the panel,and the voltage and Current sensorssense the voltage and current from the panel for converting to power. NodeMCU configures the sensors and motor and to the cloud. Thingspeak cloud displays the values of the data. © 2022 IEEE.

关键词： Wind power

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Fast and stable Zinc Anode‑based Electrochromic Displays Enabled by bimetallically Doped vanadate and Aqueous Zn^(2+)/Na^(+)Hybrid Electrolytes

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Nano-Micro Letters 2023年第12期15卷 231-241页

作者： Zhaoyang song bin Wang Wu Zhang Qianqian Zhu Abdulhakem YElezzabi Linhua Liu William WYu Haizeng Li College of Chemistry and Chemical Engineering Qingdao UniversityQingdao 266071People’s Republic of China Optics and Thermal Radiation Research Center Institute of Frontier and Interdisciplinary ScienceShandong UniversityQingdao 266237People’s Republic of China School of Chemistry and Chemical Engineering Shandong UniversityJinan 250100People’s Republic of China Ultrafast Optics and Nanophotonics Laboratory Department of Electrical and Computer EngineeringUniversity of AlbertaEdmontonAB T6G 2V4Canada

vanadates are a class of the most promising electrochromic materials for displays as their multicolor ***,the slow switching times and vanadate dissolution issues of recently reported vanadates significantly hinder their diverse practical ***,novel strategies are developed to design electrochemically stable vanadates having rapid switching *** show that the interlayer spacing is greatly broadened by introducing sodium and lanthanum ions into v_(3)O_(8)interlayers,which facilitates the transportation of cations and enhances the electrochemical *** addition,a hybrid Zn^(2+)/Na^(+)electrolyte is designed to inhibit vanadate dissolution while significantly accelerating electrochemical *** a result,our electrochromic displays yield the most rapid switching times in comparison with any reported Zn-vanadate electrochromic *** is envisioned that stable vanadate-based electrochromic displays having video speed switching are appearing on the near horizon.

关键词： vanadates Hybrid electrolytes Displays Electrochromic

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Machine Learning based Intercropping Recommender

Machine Learning Based Intercropping Recommender

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North Karnataka subsection Flagship International Conference (NKCon), IEEE

作者： sujata Desai Kanchan Wangi Department of Computer Science and Engineering BLDEA's V. P. Dr. P. G. Halakatti College of Engineering and Technology Vijayapura Karnataka India

Intercropping is the agricultural technique for growing two or more crops in the same area simultaneously. It is a popular cropping method that tries to effectively match crop demands with the people and growth resources that are available. Using crops with different rooting abilities, canopy structures, heights, and nutrient needs to increase production on a specific piece of ground by more effectively utilizing the available growing resources. The crops are combined based on how effectively they each use growth resources to achieve this. Intercrops can boost pasture's output of crude protein, which lowers bug occurrence and raises the quality of fodder production. In places susceptible to inclement weather like fog, drought, and flooding, intercropping provides protection against crop failure and shifting market values for a specific item. It is therefore better for financial security than lone cropping and is perfect for small farms that require a lot of effort.

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