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57th Annual Conference on Information Sciences and Systems, CISS 2023

作者： Bhatnagar, Shalabh Prashanth, L.A. Indian Institute of Science Department of Computer Science and Automation Bengaluru India Indian Institute of Technology Madras Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9781665451819

We present in this paper a family of generalized simultaneous perturbation stochastic approximation (G-SPSA) estimators that estimate the gradient of the objective using noisy function measurements, but where the number of function measurements and the form of the gradient estimator is guided by the desired estimator bias. In particular, estimators with more function measurements are seen to result in lower bias. We provide an analysis of convergence of generalized SPSA, and point to possible future directions. © 2023 IEEE.

关键词： Approximation theory

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Machine Learning For Smart School Selection And Admissions

Machine Learning For Smart School Selection And Admissions

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Gupta, Anish Tyagi, Lalit Kumar Kumar, Sandeep Chandigarh Group of Colleges Chandigarh Engineering College Department of Computer Science & Engineering Mohali India Aryan Institute of Technology Ghaziabad India

ISBN: (纸本)9798331508685

Selecting the appropriate school for their child is one of the decisions that parents are faced with in life, this process can be stressful and uncertain at times. Thus, machine learning is noted to be a possible solution that can be deployed to enhance and optimize the admissions and school selection process in the current periods. The aim of this research is to explore the framework and applicability of machine learning techniques in view of selective admissions, and smart school selection. In the early stages of our investigation, we suggest a simple to use, web based tool where with the aid of different learning machines, all the parents and guardians will be able to find the appropriate school for their child. To help the clients to select particular schools, the website collects and consolidates various information including, but not limited to the following: season activity involvement, academic performance, geographical preferences, and school ranking. This makes the process of selecting a school to attend easier and also helps parents to make right decisions for their children to attend the best school. The second section of this paper describes the procedures of implementing the machine learning techniques in detail, the overviews of the steps of feature selection, data preprocessing, and model evaluation. We also point out the challenges and ethical concerns of employing machine learning for school choice and the problem of an objective machine over a bias one. © 2025 IEEE.

关键词： Adversarial machine learning

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Blockchain-Enabled Secure and Fair Online Lottery Systems Using Smart Contracts

Blockchain-Enabled Secure and Fair Online Lottery Systems Us...

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2024 IEEE Silchar Subsection Annual Conference, SILCON 2024

作者： Datta, Suseta Mondal, Rituparna Roy, Rajdeep Banerjee, Sourav Biswas, Utpal Narula Institute of Technology Department of Computer Application West Bengal Kolkata India Techno India University Department of Computer Applications West Bengal Kolkata India University of Kalyani Department of Computer Science and Engineering West Bengal Kalyani India Kalyani Government Engineering College Department of Computer Science and Engineering West Bengal Kalyani India

ISBN: (纸本)9798331540821

In the capricious digital dominion, conventional online lottery systems come across consequential impediment regarding transparency, fairness, and security. This paper presents an inventive solution by merging Blockchain technology and smart contracts into online lotteries to take forward these challenges. The preferred disperse implementation capitalizes on Blockchain's decentralized category to covenant the translucency and immutability of lottery transactions, thereby undoing the requisite for trusted interceders. Smart contracts are immersed to automate the entire lottery process-from ticket provisioning to winner selection and payout-certifying agreement to preconceived rules and reinforcing contender trust. Through meticulous perusal and enactment, illustration of how Blockchain technology can transform online lottery systems, providing users with a secure, transparent, and fair platform have been performed. Furthermore, the suggested system's resistance to recurrent deceit and its prospective impression on the online gambling industry are talked about, foregrounding the welfares of a Blockchain-drove technique to online lotteries. © 2024 IEEE.

关键词： Smart contract

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WLARS: Workload-Aware Recharge Scheduling Mechanism for Improving Surveillance Quality in Wireless Rechargeable Sensor Networks

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IEEE Sensors Journal 2023年第11期23卷 12237-12250页

作者： Dande, Bhargavi Chang, Chih-Yung Wu, Shih-Jung Roy, Diptendu Sinha National Taiwan University Department of Electrical Engineering Taipei10617 Taiwan Tamkang University Department of Computer Science and Information Engineering New Taipei25137 Taiwan National Institute of Technology Meghalaya Department of Computer Science and Engineering Shillong793003 India

With radio frequency (RF), the mobile charger (MC) can wirelessly transmit energy to the sensor nodes in the network. The wireless energy transfer enhances the lifetime of sensor nodes in wireless rechargeable sensor networks (WRSNs). Most of the studies improved the efficiency of MC or perpetuated the lifetime of WRSNs. At the same time, none of the studies focused on considering the spatial and temporal surveillance qualities (STSQs) of each sensor recharged. Therefore, this article proposed an efficient energy recharge scheduling for MC, aiming to maximize the STSQ of the given network. Initially, sensor routing load (RL) is considered to partition the network to distribute the charging load of the MCs evenly. Second, the busy level of the MC is considered to send the recharging requests to the MC. Based on the busy level of MC, the sensors adjust the sensing rate frequency to manage their energy. Finally, cooperation between the neighboring MCs is proposed to reduce the waiting time for recharging requested sensors. The simulation results present that the proposed work yields the literature in terms of STSQ, traveling distance, average waiting time (AWT), and energy usage efficiency. © 2001-2012 IEEE.

关键词： computer architecture

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Machine Learning for Data Fusion:A Fuzzy AHP Approach for Open Issues

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computers, Materials & Continua 2023年第12期77卷 2899-2914页

作者： Vinay Kukreja Asha Abraham K.Kalaiselvi K.Deepa Thilak Shanmugasundaram Hariharan Shih-Yu Chen Chitkara University Institute of Engineering and Technology Chitkara UniversityPunjab140401India Department of Networking and Communications SRM Institute of Science and TechnologyKattankulathurChengalpattuTamil Nadu603203India Department of Networking and Communications Faculty of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathurChengalpattuTamil Nadu603203India Department of Computer Science and Engineering Vardhaman College of EngineeringHyderabad501218India Department of Computer Science and Information Engineering National Yunlin University of Science and TechnologyYunlin64002Taiwan Intelligence Recognition Industry Service Research Center National Yunlin University of Science and TechnologyYunlin64002Taiwan

Data fusion generates fused data by combining multiple sources,resulting in information that is more consistent,accurate,and useful than any individual source and more reliable and consistent than the raw original data,which are often imperfect,inconsistent,complex,and *** data fusion methods like probabilistic fusion,set-based fusion,and evidential belief reasoning fusion methods are computationally complex and require accurate classification and proper handling of raw *** fusion is the process of integrating multiple data *** filtering means examining a dataset to exclude,rearrange,or apportion data according to the *** sensors generate a large amount of data,requiring the development of machine learning(ML)algorithms to overcome the challenges of traditional *** advancement in hardware acceleration and the abundance of data from various sensors have led to the development of machine learning(ML)algorithms,expected to address the limitations of traditional ***,many open issues still exist as machine learning algorithms are used for data *** the literature,nine issues have been identified irrespective of any *** decision-makers should pay attention to these issues as data fusion becomes more applicable and successful.A fuzzy analytical hierarchical process(FAHP)enables us to handle these *** helps to get the weights for each corresponding issue and rank issues based on these calculated *** most significant issue identified is the lack of deep learning models used for data fusion that improve accuracy and learning quality weighted *** least significant one is the cross-domain multimodal data fusion weighted 0.076 because the whole semantic knowledge for multimodal data cannot be captured.

关键词： Signal level fusion feature level fusion decision level fusion fuzzy hierarchical process machine learning

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Sustainable Agriculture in Food Security Integrating Satellite Data Risk Assessment by Cyberattack Detection: AI Applications

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Remote Sensing in Earth Systems Sciences 2025年第2期8卷 435-443页

作者： Kumar, M. Prema Krishnammal, N. Gupta, Manu Begum, Mairaj Unisa Sultana, Sameera Degala, Divya Priya Dept. of ECE Shri Vishnu Engineering College for Women (A) Andhra Pradesh Bhimavaram 534202 India Department of Computer Science and Engineering Dr. N.G.P. Institute of Technology Tamilnadu Coimbatore India Department of Electronics and Communication Engineering Sreenidhi Institute of Science and Technology Telangana Hyderabad India Department of CSE-AIML MALLAREDDY UNIVERSITY Telangana Hyderabad India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

Globally, chronic food insecurity persists and is made worse by shocks brought on by climate change, like floods and droughts. In order to guarantee prompt assistance delivery, humanitarian programming prioritizes predicting levels of food insecurity and identifying homes that are at risk. Using complicated and diverse data, machine learning (ML) models have been widely used to promote food security. This research proposes a novel technique in sustainable agriculture-based food security analysis and satellite data cyberattack detection using machine learning models. Here, the input is collected based on satellite data, and its cyber detection has been analyzed using a convolutional spatio adversarial encoder neural network. Then, from this satellite data, the food security analysis data has been collected and processed for noise removal and normalization. Processed data has been classified using the fuzzy Q-Boltzmann ResNet transfer neural network. Experimental analysis is carried out for the food security analysis dataset in terms of data integrity, detection accuracy, average precision, F-1 score, and recall. The proposed approach yielded 97% detection accuracy, data integrity of 98%, 96% average precision, 93% recall, and 94% F-1 score. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Cyberattack detection Food security analysis Machine learning models Satellite data Sustainable agriculture

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A Novel Approach to Twins Identification and Classification Using Machine Learning 6th

A Novel Approach to Twins Identification and Classification ...

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6th International Conference on Smart Computing and informatics, SCI 2024

作者： Digumarthi, Suseela Diddi, Sattibabu Potla, Lavanya Pantham, Siva Jyothi Venkatesan, K.G.S. Rajamahendravaram India Department of Computer Science and Engineering Megha Institute of Engineering and Technology for Women Telengana Hyderabad India

ISBN: (纸本)9789819619801

Identifying and classifying twins pose challenges, yet hold significant applications in medical research, forensic science, and social science. In recent years, machine learning has emerged as a promising approach to twins identification and classification. Leveraging the unparalleled genetic similarity of identical twins, it is unsurprising that their fingerprints exhibit the highest degree of resemblance, rendering DNA ineffective for discrimination. Face recognition, a burgeoning field of image analysis and understanding, has garnered significant interest, especially in recent years. This study delves into the potential of both traditional decision trees and cutting-edge Convolutional Neural Networks (CNNs) within machine learning (ML) for accurate twin identification and classification. Key metrics like specificity, sensitivity, and accuracy are employed to gauge performance. The results unveil the superiority of CNNs over decision trees in every aspect, achieving exceptional accuracy in twin recognition. MATLAB development tool will be used to demonstrate the. The ultimate objective is to establish a robust system that capitalizes on inherent characteristics to distinguish between individual twins with unparalleled precision. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Maximizing Crop Yields and Water Efficiency through Tailored Irrigation System Design and Crop Selection

Maximizing Crop Yields and Water Efficiency through Tailored...

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2023 IEEE Global Conference on Information Technologies and Communications, GCITC 2023

作者： Musale, Prajakta Jadhav, Balasaheb Bahir, Suvarna A Thopate, Kaushalya Anarase, Dipali Jaiswal, Krishna Vishwakarma Institute of Technology Department of Engineering Sciences and Humanities Pune India Vishwakarma Institute of Technology Department of Artificial Intelligence and Data Science Pune India Trinity Academy of Engineering Department of Computer Engineering India Vishwakarma Institute of Technology Department of Computer Engineering Pune India Wipro Technology India

ISBN: (纸本)9798350308167

Today various water-pump operating systems are available in the market, which allows the user/farmer to operate his water-pump from a distant location using mobile app or on a phone call to the system which is integrated with the water-pump. While studying the existing system we came across a problem that, existing systems does not provide any solution for optimizing the usage of water and power. Optimizing the use of power and water in farming has become a real-time problem these days. To overcome this problem, we have proposed an IOT based system "Advanced Irrigation System". This system provides automation for operating the water pump in the farm by automatically turning ON and OFF the water-pump depending on the type of the crop and its moisture requirements. Also, proposed system provides farmer interface via a mobile application in his regional language, where he can check the moisture, humidity and temperature of his farm. Along with this proposed system has a Google Assistant integrated, using which famer can operate the water-pump remotely over a voice command. © 2023 IEEE.

关键词： Advanced Irrigation System Crop Selection Maximizing Crop Yields power Optimization Water Efficiency water-pump operating systems

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Modelling of Fuzzy Logic Based System for the Implementation of Model for Healthcare System with Accurate Prediction 4

Modelling of Fuzzy Logic Based System for the Implementation...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Kumar, Vikram Saini, Dimple Rastogi, Shailesh Raman, Ramakrishnan Verma, Apurv Meenakshi, R. Md Srv Media Pvt. Ltd India India Pune Department of Cse Symbiosis Institute of Business Management Nagpur India Department of Cse India Parul Institute of Technology Parul University Computer Science and Engineering Faculty of Engineering and Technology India Chennai Institute of Technology Computer Science and Engineering India

ISBN: (纸本)9798350360165

Budgetary restrictions force healthcare institutions to endure ongoing deterioration, which calls for better building capital deterioration modeling. A more sophisticated predictive model is essential to implementing a proactive maintenance approach and lowering unexpected failures. Hospitals now make subjective deterioration estimates based just on an individual's age, which creates uncertainty. This research addresses the shortcomings of existing approaches by putting forward a novel stochastic degradation prediction model. The model, a fuzzy Markovian technique based on hybrid genetic algorithms, reduces subjectivity by anticipating based on the most recent observed circumstances. The model uses a nonhomogeneous transition matrix of probabilities (TPM) with fuzzy membership functions of condition, maturity, and relative degradation rate due to the paucity of data on the condition evaluation of important infrastructure. Genetic algorithm calibration improves TPM accuracy by addressing the limitations of expert-based models. A sensitivity analysis checks for changes in output when parameters are changed, ensuring the resilience of the model. Real hospital datasets are used for validation against a cutting-edge stochastic model, and the results show improved performance over existing approaches. In order to provide the best possible administration of healthcare facilities, this approach offers to assist decision-makers in creating effective maintenance of data-driven capital renewal strategies. © 2024 IEEE.

关键词： Prediction models

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Machine learning based Development and Evaluation of a Hand Exoskeleton Rehabilitation Robot for Patients with Partial Paralysis 2

Machine learning based Development and Evaluation of a Hand ...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Singh, Durgesh Kumar Srivastava, Anand Prakash Singh, Shivanshu Paliwal, Sanjay Singh, Anupam Singh, Jagendra Hi-Tech Institute of Engineering & Technology Mechanical Engineering Department Ghaziabad India Nitra Technical Campus Department of Computer Science Ghaziabad India R D Engineering College Mechanical Engineering Department Ghaziabad India Graphic Era Hill University Department of Computer Science and Engineering Dehradun India School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (纸本)9798350371055

This work focuses on the estimation of elbow joint angles from electromyography (EMG) signals and investigates the use of machine learning models in rehabilitation robotics. Four distinct machine learning algorithms were tested for their capacity to precisely translate EMG inputs into actuator commands: Artificial Neural Network (ANN), Naive Bayes (NB), Linear Regression (LR), and Decision Trees (DT). The models were trained and tested using a dataset generated by a wireless EMG sensor and motion capture system that recorded the dynamic relationship between elbow movements and related muscle activations. The results show the remarkable 98.7% accuracy of the ANN model, indicating that it can be a valuable tool for real-time rehabilitation robotics applications. Additionally, NB, LR, and DT displayed competitive results that demonstrated their unique capacities to manage a range of sensor inputs. The discussion highlights how important these models' interpretability, computational efficiency, and continuous adaptation are to their successful integration into real-world rehabilitation settings. This work contributes to the field of rehabilitation robotics by applying machine learning models for accurate actuation control based on EMG signals. The findings demonstrate how these models could enhance the accuracy and responsiveness of assistive technology, offering individuals with partial paralysis customized and adaptable rehabilitation regimens. © 2024 IEEE.

关键词： assistive devices electromyography joint angle prediction machine learning Rehabilitation robotics

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