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Noise-tolerate and adaptive coefficient zeroing neural network for solving dynamic matrix square root

CAAI Transactions on Intelligence Technology 2024年第1期9卷 167-177页

作者： Xiuchun Xiao Chengze Jiang Qixiang Mei Yudong Zhang School of Electronics and Information Engineering Guangdong Ocean UniversityZhanjiangChina School of Cyber Science and Engineering Southeast UniversityNanjingChina School of Computing and Mathematical Sciences University of LeicesterLeicesterUK

The solving of dynamic matrix square root(DMSR)problems is frequently encountered in many scientific and engineering *** the original zeroing neural network is powerful for solving the DMSR,it cannot vanish the influence of the noise perturbations,and its constant-coefficient design scheme cannot accelerate the convergence ***,a noise-tolerate and adaptive coefficient zeroing neural network(NTACZNN)is raised to enhance the robust noise immunity performance and accelerate the conver-gence speed ***,the global convergence and robustness of the pro-posed NTACZNN are theoretically analysed under an ideal environment and noise-perturbed ***,some illustrative simulation examples are designed and performed in order to substantiate the efficacy and advantage of the NTACZNN for the DMSR problem *** with some existing ZNNs,the proposed NTACZNN possesses advanced performance in terms of noise tolerance,solution accuracy,and convergence rate.

关键词： adaptive intelligent systems neural network real-time systems

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An Optimized Hybrid Deep Learning Model for Appliance Energy Prediction in Smart Homes

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Journal of engineering science and Technology Review 2024年第3期17卷 151-158页

作者： Vidhya, S. Balaji, M. Kamaraj, V. Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Department of Electrical and Electronics Engineering Sri Sivasubramaniya Nadar College of Engineering Chennai India

The increased energy consumption due to industrial machines and household appliances affectenergy sustainability as well as economic stability. To meet the energy demand various practices are followed worldwide. Research on sustainable energy production is at peak meanwhile research towards reducing the cost of energy generation is gaining more interest in recent times. one of the key points considered to reduce the power generation cost is energy consumption prediction. Future demands can be predicted, and necessary power can be generated or delivered to meet the demand. Specifically, prediction model is essentially required for smart homes as they utilize multiple devices for smart connectivity. Machine learning algorithms are widely used for energy prediction applications. However, the performance of machine learning models needs manual selection of features and is still complex in data analysis and decision-making process. Recently deep learning (DL) techniques are used in various classification and prediction applications. DL superior performance is incorporated for energy prediction for smart home appliances. This research work presents a hybrid optimized DL model using Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) network. Additionally, to improve the prediction performances, the parameters of the hybrid DL model is fine-tuned using canonical particle swarm optimization algorithm. Experiments of the proposed model utilizes UCI Household power consumption dataset to evaluate the performances in terms of RMSE, MAPE, MAE and R2-score. The proposed model attained RMSE of 26.2154W, MAPE of 2.965%, MAE of 18.65W and R2-Score of 0.9989 which is much better than existing linear regression, extreme learning machine and diverse LSTM models. © 2024 school of science, DUTH. All rights reserved

关键词： Domestic appliances

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An Enhanced Co-Prime Sampler for Effective Coarray-Domain Processing of Underdetermined Direction-of-Arrival Estimation

IEEE Canadian Journal of Electrical and Computer Engineering

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IEEE Canadian Journal of Electrical and Computer engineering 2025年第2期48卷 124-130页

作者： Chandrasekaran, Ashok Venkateswaran, N. Sai University School of Computing and Data Science Chennai603104 India Sri Sivasubramaniya Nadar College of Engineering Department of Electronics and Communication Engineering Chennai603110 India

The design of a sparse sampler for effective coarray-domain processing of underdetermined direction-of-arrival (DOA) estimation is of prime research interest. Toward this, several sparse samplers have been proposed, among which co-prime samplers have garnered significant attention due to their closed-form expression for sampler element positioning and minimal susceptibility to mutual coupling effects. In this article, an enhanced co-prime sampler (ECPS) is proposed by concatenating the two operations on subsamplers, involving the scaling of one subsampler and shifting of another subsampler within the hold of the co-prime property. The proposed ECPS offers several advantages over existing samplers, including increased consecutive lags, reduced holes in the difference coarray, larger physical aperture, enhanced sparsity, and reduced susceptibility to mutual coupling effects. These improvements lead to high consecutive degrees-of-freedom (cDOF) in detecting more targets than the number of physical elements in the sampler with high estimation accuracy. The superiority and efficacy of the proposed ECPS are analyzed and validated with several simulation studies. © 2021 IEEE.

关键词： Estimation Direction-of-arrival estimation Mutual coupling Apertures Multiple signal classification Interpolation Accuracy Vectors Distance measurement Closed-form solutions

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Development of a diacritic-aware large vocabulary automatic speech recognition for Hausa language

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International Journal of Speech Technology 2024年第3期27卷 687-700页

作者： Abubakar, Abdulqahar Mukhtar Gupta, Deepa Vekkot, Susmitha Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Department of Electronics & amp Communication Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India

Research on voice recognition for African languages is limited due to the scarcity of digital resources for training and adaptation, despite its broad usefulness. The Hausa language, spoken by almost fifty million inhabitants in West and Central Africa, is an example of a linguistic domain that has not been thoroughly studied. The Hausa language employs diacritics, which are symbols located above alphabetical characters to convey further information. By removing diacritics, the number of homographs increases, making it difficult to distinguish between similar words. This paper presents a study on speech recognition in the Hausa Language, specifically focusing on diacritized words. The study utilises the state-of-the-art wave2vec2.0 and Whisper deep learning architecture models, for transcribing audio signals into corresponding Hausa text. According to the results obtained in the study, the Whisper-large deep model emerged as the best, achieving a word error rate of 4.23% representing a considerable improvement of 43.9% when compared to the existing state-of-the-art model for Hausa language speech recognition. Additionally, the Whsiper-large model demonstrated a diacritic coverage of 92%, precision of 98.87%, with a diacritic error rate of 2.1%. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep learning

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Multi-response Optimization of Process Parameters in Stretch Blow Molding of PET Plastic Bottles

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Journal of The Institution of Engineers (India): Series D 2024年 1-15页

作者： Merga, Henok Hailu Sinha, Devendra Kumar Getachew, Aklilu Workneh, Melese Rathee, Devender Singh Department of Mechanical Engineering School of Mechanical Chemical and Materials Engineering Center of Excellence Advanced Manufacturing Engineering Adama Science and Technology University Adama Ethiopia Department of Electronics Engineering School of Electrical Engineering and Computing Adama Science and Technology University Adama Ethiopia

Plastic waste in Polyethylene Terephthalate (PET) bottles is creating new challenges, which in today’s scenario is a major concern in every industry. The defect in PET bottles mainly occurs due to improper selection of levels of process parameters during the production process. Therefore, optimizations of process parameters are very important to minimize the defects produced due to improper stretch blow molding process parameter selections. The study is carried out on 2 L bottle since they have the highest demand and the highest defect rate in the PET bottling sector. The PET bottles have been developed from the preform at various blowing temperature, blowing speed and blowing angle on commercially available SBM setup. The design and plan of experiments have been done according to L9 orthogonal array of Taguchi method. The optimization of process parameters has been done through the hybrid Taguchi grey relational analysis optimization technique on four factors namely: blowing temperature, first blow angle, second blow angle and blowing speed. Each value of parameter was taken at three levels in combination. The testing of all samples such as tensile test, wall thickness measurement and hoop stretch ratio measurement have been carried out as per ASTM standards. The morphology of the samples has been carried out through scanning electron microscope. A maximum tensile strength of 65 MPa, minimum hoop stretch ratio of 5.45 and a maximum wall thickness 0.24 mm was found at varying process parameter combinations. It has been observed that among the process parameters: blowing temperature was the most significant factor for minimizing defects while second blow angle was the least significant parameter. It was found that optimized values of the process parameters yielded 15% reduction in defects on PET bottles. The present study is expected to contribute significantly for the industries working in this field for carrying out production at optimum levels of process parameter

关键词： Plastic bottles

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Effortless learning: leveraging summarization and tabulation for automated note making

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Multimedia Tools and Applications 2024年 1-26页

作者： Arivarasi, A. Srinivasan, R. Rajeswari, D. Govindasamy, Alagiri School of Electronics Engineering Vellore Institute of Technology Tamil Nadu Chennai600127 India Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur603203 India Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur603203 India Future Connect Technologies Pvt. Ltd Tamil Nadu Kanchipuram600034 India

In this fast-paced Digital Age, Natural Language Processing (NLP) can prove beneficial in consuming quality information efficiently. With the ever-growing number of learning resources, it is becoming onerous for students to choose the best-suited learning resource. Furthermore, many students find it difficult to understand and retain highly factual information. To overcome these issues, this paper proposes a novel Automated TExt Summarization of scholastic text in Table format (Auto-TEST) to provide information at a glance by mimicking the student’s style of note-making. The Auto-TEST framework is achieved through two steps which is Summarization and Tabulation. Initially, the Extractive Single Document Text Summarization (ESDTS) method is adopted to identify the salient information from the non-fictional (scholastic) text which is extracted and grouped together to form a concise summary. A Named Entity Recognition (NER) method is implemented to extract noun chunks and then arrange them in a tabulation format that aids in memorizing essential facts and figures by enabling effortless retrieval. The performance of the Auto-TEST framework is gauged by using scholastic domain-specific dataset using ROUGE as an evaluation metric. The extracted entities gives an average Recall of 71.40%, an average Precision score of 75.80% and an average F-score of 73.54% for the dataset respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Students

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Enhancing Security: A Hybrid Encryption Algorithm for Effective Key Generation 2

Enhancing Security: A Hybrid Encryption Algorithm for Effect...

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2nd International Conference on Recent Trends in Microelectronics, Automation, computing, and Communications Systems, ICMACC 2024

作者： Jacob, Jeevan John Kiran, P.S. Shenoy, Jaigovind B Basavaraju, Sagar Amrita School of Computing Department of Computer Science and Engineering Bengaluru India Amrita School of Engineering Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350366570

The abstract Data security is of most importance, in the present day- the introduction of high-level wireless communication channels has led to generation and transfer of massive amounts of data. Data security must keep up with it. Data breach may cause disastrous outcomes. Previously, hardware security modules (HSM) were used to perform cryptographic actions- storing keys and for authentication. This is no longer feasible as HSMs are physical devices and become inconvenient to carry around all the time. The unavailability of HSM poses a serious security threat (for cryptographic keys). This paper aims at a novel software solution that compensates for hardware. As the algorithm focuses on securing keys used for encryption. It may be referred to as key security scheme (KSS). The paper proposes a way to generate keys instead of storing it and assumes performing of cryptographic actions on the available hardware in the system. The proposed method can secure data, both in transit and while at rest. © 2024 IEEE.

关键词： Encryption algorithms

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Unveiling the invisible struggles: Exploring student perspectives on mental health in universities

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IEEE Potentials 2024年第3期43卷 31-35页

作者： Murali, Ritwik Avudaiappan, Veeramanohar Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amrita School of Computing Coimbatore Coimbatore641112 India Amrita Vishwa Vidyapeetham Department of Electrical and Electronics Engineering Amrita School of Engineering Coimbatore Coimbatore641112 India

In the current academic environment, the increasing prevalence of stress among students has become a critical concern, significantly affecting both mental health and academic outcomes. Factors such as heavy academic workloads, interpersonal conflicts, financial challenges, and societal pressures contribute to cognitive impairments, procrastination, and declining academic performance. Unaddressed, this stress can escalate into severe mental health conditions, including anxiety, depression, and self-harming tendencies, with marginalized or overwhelmed students being particularly vulnerable. Familial expectations, financial instability, imposter syndrome, fear of missing out, cyberbullying, and unsupportive institutional cultures further intensify these challenges. While mental health support mechanisms exist within institutions, they often remain reactive, addressing issues only after they manifest. To address this growing crisis, a shift toward proactive measures is essential, encompassing counseling, meditation, peer support systems, and engagement in extracurricular activities to foster emotional resilience. A holistic and preventative approach that emphasizes early intervention, open communication, and supportive academic environments is imperative to promote students' well-being and academic success. © 1988-2012 IEEE.

关键词： Students

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Timer Entrenched Baited Scheme to Locate and Remove Attacks in MANET

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Intelligent Automation & Soft computing 2023年第1期35卷 491-505页

作者： S.Padmapriya R.Shankar R.Thiagarajan N.Partheeban A.Daniel S.Arun Department of Computing Science and Engineering Prathyusha Engineering CollegeThiruvallur602025India Department of Electronics and Communication Engineering Teegala Krishna Reddy Engg.College500097TelanganaIndia School of Computing Science and Engineering Galgotias UniversityGreater Noida203201India Department of Electronics and Communication Engineering Prathyusha Engineering CollegeThiruvallur602025India

The Mobile Ad-hoc Network(MANET)is a dynamic topology that provides a variety of executions in various *** most sticky topic in organizationalfields was MANET *** is helpless against various threats that affect its usability and *** dark opening assault is considered one of the most far-reaching dynamic assaults that deteriorate the organi-zation's execution and reliability by dropping all approaching packages via the noxious *** Dark Opening Node aims to deceive any node in the company that wishes to connect to another node by pretending to get the most delicate ability to support the target ***-hoc On-demand Distance Vector(AODV)is a responsive steering convention with no corporate techniques to locate and destroy the dark opening *** improved AODV by incorporating a novel compact method for detecting and isolating lonely and collaborative black-hole threats that utilize clocks and *** recommended method allows MANET nodes to discover and segregate black-hole network nodes over dynamic changes in the network *** implement the suggested method's performance with the help of Network Simulator(NS)-3 simulation ***,the proposed approach comes exceptionally near to the original AODV,absent black holes in terms of bandwidth,end-to-end latency,error rate,and delivery ratio.

关键词： Mobile ad-hoc network(MANET) wireless ad hoc network ADOV attacks denial of service

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Falcon Optimization Algorithm-Based Energy Efficient Communication Protocol for Cluster-Based Vehicular Networks

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Computers, Materials & Continua 2024年第3期78卷 4243-4262页

作者： Youseef Alotaibi B.Rajasekar R.Jayalakshmi Surendran Rajendran Department of Software Engineering College of ComputingUmm Al-Qura UniversityMakkah21955Saudi Arabia Department of Electronics and Communication Engineering Sathyabama Institute of Science and TechnologyChennai600119India Department of Computer Science and Engineering Panimalar Engineering CollegeChennai600123India Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical ScienceChennai602105India

Rapid development in Information Technology(IT)has allowed several novel application regions like large outdoor vehicular networks for Vehicle-to-Vehicle(V2V)*** networks give a safe and more effective driving experience by presenting time-sensitive and location-aware *** communication occurs directly between V2V and Base Station(BS)units such as the Road Side Unit(RSU),named as a Vehicle to Infrastructure(V2I).However,the frequent topology alterations in VANETs generate several problems with data transmission as the vehicle velocity differs with ***,the scheme of an effectual routing protocol for reliable and stable communications is *** research demonstrates that clustering is an intelligent method for effectual routing in a mobile ***,this article presents a Falcon Optimization Algorithm-based Energy Efficient Communication Protocol for Cluster-based Routing(FOA-EECPCR)technique in *** FOA-EECPCR technique intends to group the vehicles and determine the shortest route in the *** accomplish this,the FOA-EECPCR technique initially clusters the vehicles using FOA with fitness functions comprising energy,distance,and trust *** the routing process,the Sparrow Search Algorithm(SSA)is derived with a fitness function that encompasses two variables,namely,energy and distance.A series of experiments have been conducted to exhibit the enhanced performance of the FOA-EECPCR *** experimental outcomes demonstrate the enhanced performance of the FOA-EECPCR approach over other current methods.

关键词： Vehicular networks communication protocol clustering falcon optimization algorithm routing

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