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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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Wind-PV based Isolated Hybrid Generation for Smart Irrigation Management and Supplying other Critical Loads 2

Wind-PV based Isolated Hybrid Generation for Smart Irrigatio...

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2nd IEEE International Conference on Sustainable Energy and Future Electric Transportation, SeFeT 2022

作者： Chatterjee, Arunava Ghosh, Soumyajit Mitra, Arindam Raghunathpur Govt. Polytechnic Department of Electrical Engineering West Bengal Purulia India Indian Institute of Technology Kanpur Department of Electrical Engineering Uttar Pradesh Kanpur India

ISBN: (数字)9781665480574

ISBN: (纸本)9781665480574

Wind and solar hybrid generation is now being used as a prospective generation scheme especially for remote and grid isolated areas. This paper presents an analysis of wind-photovoltaic (PV) hybrid generation scheme to be used in isolation from the grid for managing irrigation and supplying some other critical loads. The proposed generation scheme can be used to generate power with varying wind speeds. The load voltage will be maintained using availability of power from wind or PV sources. The proposed scheme can be installed in a hilly region or in any onshore areas which is isolated from the grid. The proposed scheme is also flexible to be connected to grid if required. The control is done using a smart controller. The simulation and experimental results validate that the proposed hybrid generation can be suitably used, installed and employed for isolated power generation effectively especially for irrigation management. © 2022 IEEE.

关键词： Irrigation

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Efficacy of indian government welfare schemes using aspect-based sentimental analysis

Lecture Notes on Data Engineering and Communications Technol...

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Lecture Notes on Data engineering and Communications Technologies 2021年 66卷 981-988页

作者： Kaur, Maninder Girdhar, Akshay Singh, Inderjeet Department of Computer Science and Engineering Guru Nanak Dev Engineering College Ludhiana India Department of Information Technology Guru Nanak Dev Engineering College Ludhiana India Information Technology Govt. Polytechnic College Bathinda India

One of the simplest methods to understand people’s thoughts using images or text is commonly given as sentiment analysis. Sentiment analysis is used mostly in products advertisement and promotion depends on the user’s opinion. The process is based on the aspect-based sentiment analysis and it is used to understand and find out what someone is speaking about, and likeness and dislikeness. One of the real-world models of the perfect realm of this subject is the huge number of available Indian welfare plans like Swachh Bharat Abhiyan and Jan Dhan Yojna. In this paper, labeled data is used on the basis of polarity. Tweets are preprocessed and unigram features are then extracted. In the initial steps, tokenization process, stop word removal process, and stemming process are performed as preprocessing to remove duplicate data. The unigram features and labels trained by support vector machine (SVM), K-nearest neighbor (KNN), and a combination of SVM, KNN, and random forest as a proposed model are used in the presented work. Implementation of experimental proposed approach demonstrates that better results in accuracy and precision than SVM and KNN. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.

关键词： Sentiment analysis

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A CNN Approach to Detect The Anomalies In BGP Traffic

A CNN Approach to Detect The Anomalies In BGP Traffic

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International Conference on Computing and Networking Technology (ICCNT)

作者： Rahul Deo Verma Pankaj Kumar Keserwani Vinesh Kumar Jain Department of Computer Science and Engineering National Institute of Technology Sikkim Sikkim India Department of Computer Engineering Govt. Engineering College Ajmer Ajmer India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The stability and security of the Internet’s routing infrastructure, largely dependent on the Border Gateway Protocol (BGP), are critical for ensuring reliable global connectivity. Anomalous routing behavior can disrupt this stability, making effective anomaly detection essential. Traditional methods face significant challenges due to the dynamic and complex nature of BGP networks. To address these challenges, we propose a Convolutional Neural Network (CNN)-based approach for detecting BGP anomalies. CNNs are well-suited for capturing spatial dependencies and patterns within data, making them an ideal choice for this application. We evaluate the performance of our CNN model using the RIPE and BCNET datasets, demonstrating superior accuracy in anomaly detection compared to previous methods. Our results highlight the potential of CNNs to enhance the robustness of BGP anomaly detection systems.

关键词： Training Accuracy Neural networks Routing Stability analysis Robustness Border Gateway Protocol Convolutional neural networks Security Anomaly detection

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Climate Data Forecasting Using ARIMA and Holt Winter Methods

Climate Data Forecasting Using ARIMA and Holt Winter Methods

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Remote Sensing Symposium (InGARSS), 2020 IEEE India Geoscience and

作者： Surendra Singh Choudhary Rashmi Saini Rahul Raj Choudhary Laxman Singh Khangarot Department: Computer Science & Engineering Govt. Engineering College Bikaner Computer Science & Engineering G. B. Pant Institute of Engineering and Technology Uttarakhand Department: Electronics and Instrument & Control Engineering Govt. Engineering College Bikaner Computer Science & Engineering Indian Institute of Technology Roorkee Uttarakhand

ISBN: (数字)9798350390346

ISBN: (纸本)9798350390353

The Artificial Neural Networks (ANNs) models have provided mixed results to solve linear problems, so it is not widely used for any data types. The autoregressive integrated moving average (ARIMA) model is a massive, very popular, and connivance linear model for long time series forecasting. Mainly used to construct different hybrid models for forecasting other time series data in the last decade. The study analyzes ARIMA and Holt-Winters (HW) forecasting procedure analysis. The machine learning and deep learning model have good frameworks that can apply to multiple forecasting processes with good precision and accuracy. Autoregressive Integrated moving average (ARIMA) and Holt-Winters (HW) approaches have been used to forecasting time series climatic variables. The two different forecasting methods are generated, and the output of both methods is compared by the climate variables rainfall and temperature for monthly data of 2001-2021.

关键词： Temperature sensors Temperature distribution Rain Time series analysis Stochastic processes Predictive models Data models Autocorrelation Forecasting Meteorology

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Wear Study of Boron Carbide Particulate Reinforced Aluminium Alloy Composites

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Journal of The Institution of Engineers (India): Series D 2025年 1-11页

作者： Christiyan, K. G. Jaya Rajanish, M. Pradeep, K.V. Ashwini, M.V. Nagaraj, P. Raghavendra, N. Revanna, K. Mallesh, G. Manjunatha, B. Raju, T. Hemanth Udayashankar, S. Department of Mechanical Engineering M S Ramaiah Institute of Technology Karnataka Bangalore560054 India Department of Mechanical Engineering Dayananda Sagar Academy of Technology and Management Karnataka Bangalore560082 India Department of Automobile Engineering Smt. L V Govt. Polytechnic Hassan573201 India School of Mechanical Engineering Reva University Karnataka Bangalore560064 India Department of Chemistry Yenepoya Institute of Technology Karnataka Mangalore574225 India Department of Mechanical Engineering BNM Institute of Technology Karnataka Bangalore560070 India Department of Mechanical Engineering Government Engineering College Krishnarajapet571426 India Mechanical Engineering Department Sri Jaya Chamarajendra College of Engineering JSS Science and Technology University Mysuru India Mechanical Engineering Department New Horizon College of Engineering Karnataka Bengaluru560103 India Department of Mechanical Engineering VTU Karnataka Belgaum590018 India

There are numerous engineering applications that make extensive use of aluminum metal matrix composites that are reinforced with ceramic particles. Some examples of these applications include automotive, aerospace, and marine sectors. In this research, Al6061 is employed as the base material, and B4C particulates are used as reinforcement. The fabrication of Al6061-9%B4C composite is achieved through stir casting technique. The microstructure of the resulting composites is examined using SEM and it was discovered that, B4C particulates were spread evenly in the Al6061 matrix. Wear testing of the Al6061-Boron carbide composites is conducted employing pin-on-disc instrument, with load, speed, and the sliding distance selected as the process parameters for wear optimization. These parameters are varied at three levels. Through the utilization of the Taguchi Approach, the optimization of wear process parameters for Al6061-B4C composites is carried out. According to the findings of the ANOVA conducted, the load has the most significance, accounting for 83.999% of the total, followed by the other factors. Experimental and Taguchi analysis results of wear response for optimum conditions of wear process variables for Al6061-B4C composite are 137 microns and 134 microns, respectively. The error between experimental and Taguchi analysis value is 2.18% and fall under permissible range. The produced Al6061-Boron carbide particulate composites are employed in automotive parts like pistons, brake drums and cylinders. © The Institution of Engineers (India) 2025.

关键词： Boron carbide

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A global study of α-clusters decay in heavy and superheavy nuclei with half-life and preformation factor

arXiv

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arXiv 2024年

作者： Saxena, G. Sharma, P.K. Saxena, Prafulla Govt. Women Engineering College Ajmer305002 India Department of Physics Faculty of Science University of Zagreb ZagrebHR-10000 Croatia Govt. Polytechnic College Rajsamand313324 India Department of Computer Science and Engineering Malviya National Institute of Technology Jaipur302016 India

A detailed study of α-clusters decay is exhibited by incorporating crucial microscopic nuclear structure information into the estimations of half-life and preformation factor. For the first time, using the k-cross validation approach, two semi-empirical formulas for (i) α-decay half-life and (ii) α-particle preformation factor, are picked out and subsequently modified by including shell, odd-nucleon blocking, and asymmetry effects along with the usual dependence on α-decay energy (Qα) and angular momentum of α-particle. Both the formulas are fitted for the two different regions separated by neutron number N=126, as from the experimental systematics the role of N=126 shell closure is found decisive in determining the trends of Qα, α-decay half-life, and α-particle preformation factor. It is found that the inclusion of the above-mentioned degrees of freedom significantly reduces the errors in the estimations when compared with several other similar modified/refitted semi-empirical relations indicating the robustness of the proposed formulas. The predictions of α-decay half-life throughout the periodic chart have been made including the unknown territory, future probable decay chain of self-conjugate nucleus 112Ba terminated on 100Sn, decay chain of 208Pa through new isotope 204Ac as well as decay chains of awaiting superheavy nuclei 298Og and 299120. This article is expected to provide a systematic approach to selecting the formula by which reliable predictions can be made. © 2024, CC0.

关键词： Degrees of freedom (mechanics)

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Artificial Level Language: A Library of Computing Engine for Natural Languages*

Artificial Level Language: A Library of Computing Engine for...

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International Conference on Control Communication & Computing India (ICCC)

作者： K. S. Sunil Muhammed Anas Sayu Sajeev Vishnu Suresh Julien Joseph Thomas Department of Computer Science and Engineering Govt. Engineering College Idukki Kerala India

Programming with natural language is an area of research with different kinds of applications including introductory programming, robotics, etc. Factors like meaning preservation, ambiguity handling, etc, have to be considered while writing programs in natural language. Natural language programming is evolved over the past years through various developments. Here we introduce a system that can convert sentences written in natural language (we considered English as an example) to python statements. The main feature of our system is that the English sentences need not be grammatically correct. We named our system as Artificial Level Language (ALL) as it can be thought of as a new level, above the high-level language, in the programming language hierarchy. The main component of ALL is a tranSMachine comprised of two parts namely tranScripter and transCompiler. The tranScripter introduced here is named as JAVS-tranScripter which includes a set of protocols that are expected to be improved in the future. ALL take English statements as input and it generates Python code in the background to carry out the instructions and outputs the results.

关键词：

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Chatbot-An Intelligent Virtual Medical Assistant 8th

Chatbot-An Intelligent Virtual Medical Assistant

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8th International Conference on Cognition and Recognition, ICCR 2021

作者： Krishna, A.N. Anitha, A.C. Naveena, C. Department of Computer Science and Engineering SJB Institute of Technology Bengaluru560 060 India Department of Computer Science and Engineering Govt. SKSJ Technological Institute Bengaluru560 001 India

ISBN: (纸本)9783031224041

Hospitals and health care centers play a major role in our day to day life. From simple prescription to major surgeries, we all depend on hospitals to maintain our health and this is the protocol followed by almost all the people over the world. What if there is a place where people could interact with the machine as they interact with doctors and discuss their health conditions and can also access through simple web application anytime through internet. This paper deals with an automated bot called Chatbot where people can interact with the bot for their health queries than visiting the clinics personally and save their time. This model is developed using NLP and Recurrent Neural Network (RNN) algorithm which provides an accuracy of 88% and compared with Artificial Neural Network (ANN) and Convolution Neural Network (CNN) concepts. The chatbot gives simple prescriptions to users based on the diagnosis of disease. Through this application, user can book an appointment with specialists if necessary. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Recurrent neural networks

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Federated Averaging Optimization for E ffi cient Skin Cancer Image Analysis

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Procedia computer Science 2025年 258卷 3794-3803页

作者： Pranav Vashistha Deepali Vashistha Varun P. Saxena Malaram Kumhar Jitendra Bhatia Rajesh Gupta Nilesh Kumar Jadav Sudeep Tanwar Electronics and Instrumentation Engineering Department Institute of Technology Nirma University Ahmedabad 382481 India Department of Computer Engineering Govt. Women Engineering College Ajmer 305002 India Computer Science Engineering Department Institute of Technology Nirma University Ahmedabad 382481 India

A skin cancer diagnosis has greatly improved with machine learning (ML), but data privacy and computational e ffi ciency issues still exist. With a primary focus on Federated Averaging (FedAvg), this study presents an optimal approach to Federated Learning (FL) to increase e ffi ciency and accuracy for skin cancer picture processing. FedAvg uses diverse datasets to train institutional mod- els jointly without exchanging sensitive data, protecting patient privacy. In this work, we present an in-depth analysis of FedAvg’s performance over the datasets of skin cancer images, outperforming conventional approaches in terms of diagnostic accuracy while requiring less communication overhead. The findings show that FL can revolutionize medical diagnosis by providing a safe, pre- cise, and e ff ective framework for model training across numerous institutions. This work has improved the ability to diagnose skin cancer and paved the way for future FL systems that take privacy and high computing e ffi ciency into account for the analysis of medical images.

关键词： Skin Cancer Federated Learning Federated Averaging Machine Learning Convolutional Neural Network Unbalanced Data stochastic gradient descent

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