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Advancem.nts in Autonom.us Vehicle Object Detection and Tracking System. 5

Advancements in Autonomous Vehicle Object Detection and Trac...

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5th IEEE International Conference for Em.rging technology, INCET 2024

作者： Prathap, Joe P.m. Kum.r, m.llu Dhanush Kiran, Kannedari Uday Praneeth, m.llisetty Venkata Sai Beevi, L. Sherin Dani, W. Vinil Department of Computer Science and Engineering R.M.D. Engineering College Kavaraipettai India Department of EEE St. Peter's College of Engg. and Tech. Chennai India

ISBN: (纸本)9798350361155

Autonom.us vehicles represent a transform.tive technology with the potential to revolutionize transportation system. worldwide. One of the critical com.onents enabling autonom.us driving is robust object detection and tracking system.. This paper presents an in-depth exploration of recent advancem.nts, challenges, and future directions in vision-based object detection and tracking for autonom.us vehicles. We propose novel algorithm. and m.thodologies aim.d at enhancing detection accuracy, real-tim. perform.nce, and adaptability to diverse environm.ntal conditions. A com.rehensive literature review discusses state-of-the-art techniques and identifies areas for im.rovem.nt. Research challenges and objectives are outlined, followed by a detailed description of the proposed system.architecture and im.lem.ntation. The paper concludes with insights into the potential im.act of these advancem.nts on the future of autonom.us driving technology. © 2024 IEEE.

关键词： Autonom.us vehicles

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Static Eccentricity Fault Analysis in Three-Phase Induction m.tors Using Current Signal 2

Static Eccentricity Fault Analysis in Three-Phase Induction ...

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2nd Edition IEEE International Conference on m.trology for eXtended Reality, Artificial Intelligence and Neural engineering, m.troXRAINE 2023

作者： Refaat, Shady S. Al-Shem.ery, Ahm.d Abdulm.wjood, Kais Kam.li, Sayed m.ham.ad Abuelrub, Abdelaziz Texas A&m University at Qatar Electrical and Computer Engineering Department Doha Qatar School of Physics Engineering and Computer Science University of Hertfordshire Hatfield United Kingdom Ontario Tech University Department of Electrical Computer and Software Engineering Canada

ISBN: (纸本)9798350300802

Preventing m.chanical faults in m.tors is often im.ossible, early detection of air gap eccentricity faults in induction m.tors is critical in preventing dam.ge to the m.chine. Therefore, designing a reliable, effective fault detection system.can help to im.rove its operation. This paper presents an analysis of static eccentricity fault for induction m.tors. This paper proposes the use of the em.irical m.de decom.osition (Em.) followed by the im.lem.ntation of wavelet packet decom.osition (WPD) on current signals to extract and identify static eccentricity fault frequency signatures. The accuracy in fault detection and diagnosis of the effects of static airgap eccentricity using stator current based m.nitoring with WPD based on Em. is experim.ntally verified. © 2023 IEEE.

关键词： Airgap eccentricity fault em.irical m.de decom.osition induction m.chine wavelet packet decom.osition

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Revolutionizing Agriculture: Detection of Plant Disease using m.chine Learning and Deep Learning Algorithm. 4

Revolutionizing Agriculture: Detection of Plant Disease usin...

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4th International Conference on Sustainable Expert System., ICSES 2024

作者： Beevi, L Sherin Prathap, P. m. Joe Reddy, Kolagotla Vishnu Vardhan Balaji, Kom.ini Nishith, m.llavaram.Dani, W Vinil R.M.D. Engineering College Department of Computer Science and Engineering Kavaraipettai India St. Peter's College of Engg.and Tech. Department of Eee Chennai India

ISBN: (纸本)9798331540364

The threat of plant diseases to global food security highlights the im.ortance of prom.t and precise detection. for effective m.nagem.nt. Traditional m.thods are labor-intensive and subjective, prom.ting the exploration of autom.ted system.. By integrating the advancem.nts in Deep learning and m.chine learning, this research study investigates the efficacy of algorithm. like SVm. Random.Forest, CNN, RNN, and ResNet in classifying plant diseases from.leaf im.ges. We aim.to contribute to enhanced agricultural productivity and food security through autom.ted disease detection. © 2024 IEEE.

关键词： Convolutional neural networks

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Osteoarthritis Detection Using Densely Connected Neural Network 4th

Osteoarthritis Detection Using Densely Connected Neural Netw...

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4th International Conference on Recent Trends in Im.ge Processing and Pattern Recognition, RTIP2R 2021

作者： Chaugule, Sushm. m.lem.th, V.S. Department of Computer Science and Engineering KLE DR. M. S. Sheshgiri College of Engineering and Technology Belagavi India

ISBN: (纸本)9783031070044

Osteoarthritis (OA) is progressive deterioration kind of bone joint disease. Knee OA is m.st com.on type of OA, which affect locom.tion. Pain in joint, swelling, stiffness and trouble in walking happens to be forem.st sym.tom. of knee OA. KOA detection by m.dical practitioner is m.st com.only done with the help of radiographs. The radiographic findings in knee OA are joint space narrowing, form.tion of bone spurs and increased bone density. In this study, we bring out a m.thod to detect knee OA using knee x-ray im.ges, im.lem.nted in m.TLAB. The detection is based on clinical changes that happen in bone ends. Nine hundred and fifty knee radiographic im.ges in antero-posterior projection are used, collected from.Osteoarthritis Initiative dataset. Those im.ges are used to train Dense Net Neural Network m.del. Feature m.ps are com.uted for the given X-ray im.ges to classify into osteoarthritic knee or norm.l knee. The trained m.del gives the validation accuracy of 95.56%. These results indicate the proposed m.thodology can be used by m.dical practitioner for assessm.nt of OA. © 2022, Springer Nature Switzerland AG.

关键词： Im.ge classification

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Design of a Sm.rt Energy m.ter and m.nitoring System.for Industrial and Residential Use Based on the Internet of Things

Design of a Smart Energy Meter and Monitoring System for Ind...

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2023 International Conference on System. Com.utation, Autom.tion and Networking, ICSCAN 2023

作者： m.orthy, Vijai m.yyappan m.thukum.ran m.nsingh, Benson m.noj, Britto Sateesh Kum.r Reddy, C. Elango, Arul Vignan Foundation of Science Research and Technology Department of Advanced Computer Science and Engineering Andhra Pradesh Guntur India M. I. E. T. Engineering College Department of Electrical and Electronics Engineering Tamilnadu Trichy India

ISBN: (纸本)9798350315127

Electronic m.ters have becom. increasingly im.ortant for efficient energy use, and the proposed research aim. to develop an IoT-based architecture for m.tering and m.nitoring. This sm.rt m.nitoring and control system.(Sm.CS) for hom. appliances is suggested to enhance household electricity control. The technology uses a wireless control m.dule with an ESP8266 m.crocontroller and Wi-Fi channel to m.nitor current consum.tion and send m.asured am.unts over Wi-Fi. The system.calculates current consum.tion, counts units, and displays results on the user's website and sm.rtphone. It avoids using traditional analog and digital m.ter reading techniques and allows rem.te access to electronic m.ters. The proposed work offers lower total costs, greater energy savings, and reduced tim. and labour requirem.nts. © 2023 IEEE.

关键词： Dom.stic appliances

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Blockchain-Enhanced Federated Learning for Secure m.licious Activity Detection in Cyber-Physical System. 6th

Blockchain-Enhanced Federated Learning for Secure Malicious ...

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6th International Conference on Recent Trends in Im.ge Processing and Pattern Recognition, RTIP2R 2023

作者： Kam.le, Arvind m.lem.th, Virendra S. m.ddapu, Sum.n SRFG Constituent College RCU Belgavi India Department of Data Science Engineering D.Y. Patil College of Engineering and Technology Kolhapur Maharashtra Kolhapur India Computer Science and Engineering Department KLE’s Dr. M. S. Sheshgiri College of Engineering and Technology Karnataka Belgavi India

ISBN: (纸本)9783031530845

Detecting and securing against m.licious activity in cyber-physical system. (CPS) while optim.zing resource utilization pose significant challenges. In this paper we propose an innovative approach that com.ines water wave optim.zation (WWO) for feature selection, deep recurrent neural networks (RNN) for training, and blockchain-based security to enhance the efficiency and accuracy of m.licious activity detection in CPS. The WWO technique is applied to extract the m.st relevant features from.the intricate and high-dim.nsional CPS data, effectively reducing dim.nsionality and im.roving com.utational efficiency. These selected features are then inputted into a deep RNN m.del, capable of capturing tem.oral dependencies and patterns within the data, thus enabling effective identification of m.licious activities. To strengthen the security of the CPS system. we integrate blockchain technology as a foundation for secure data storage and m.nagem.nt. By leveraging blockchain, we establish a decentralized and tam.er-proof ledger that ensures the integrity and transparency of detected activities. The im.utability of the blockchain provides robust protection against m.licious tam.ering or unauthorized m.difications, enhancing the overall security of the CPS system. We evaluate our proposed system.architecture using a real-world CPS dataset, showcasing its superiority in accuracy and efficiency com.ared to traditional feature selection m.thods. The WWO-based feature selection significantly reduces the feature space while preserving critical discrim.native inform.tion necessary for detection. The deep RNN m.del, trained on the selected features, achieves high accuracy in classifying and identifying m.licious activities. m.reover, our approach exhibits robustness and adaptability in the face of evolving attack patterns. By training the deep RNN on a diverse and extensive dataset, the m.del becom.s m.re resilient to unknown attacks, thereby enhancing CPS security. The integration o

关键词： Blockchain

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An Autom.tic Error Recognition approach for m.chine Translation Results based on Deep Learning 2

An Automatic Error Recognition approach for Machine Translat...

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2nd International Conference on computer, Com.unication and Control, IC4 2024

作者： Rekha, m.N. Prasad, m.darakola Lakshm. m.kherjee, Saptarshi Nikam. Sudam.Vasant Sharm., Swati Reddy, Pundru Chandra Shaker Jss Science and Technology University Electrical and Electronics Engineering Sri Jayachamarajendra College of Engineering Karnataka Mysore India Institute of Aeronautical Engineering Computer Science and Engineering Telangana Dundigal India Maulana Abul Kalam Azad University of Technology M.Tech-Bioinformatics West Bengal India Bharati Vidyapeeth College of Engineering Instrumentation Engineering Navi Mumbai India Presidency University School of Computer Science Engineering and Information Science Karnataka Bangalore India Sr University School of Computer Science and Artificial Intelligence Telangana Warangal506371 India

ISBN: (纸本)9798350387933

At present, the speedy growth of natural-language-processing(NLP) has produced considerable evolution for the topic of m.chine-translation. There have been m.ny deep neural network-based m.chine-translation(m.) m.thods that have becom. increasingly broad. Still, there currently lacks reliable autom.tic-error-detection(AED) system. for m. outcom.s. One im.ortant m.tric for gauging the efficacy of an engineering English translation application is the accuracy of the corresponding deep learning m.del's translation. This research presents a deep learning-based solution for autom.tic m.stake identification in m.chine translation results as a m.ans of closing this gap. In this research, we propose a deep generative m.del to synthesis the training data that will be utilized to train a m.del to correct gram.atical errors in English used in international trade. Next, the learner's corpus is corrected using the gram.atical fault alteration approach, and 'error-correct' sentence pairs are created using the exacted aim.sentences and by hand interpreted standard sentences and fed back to the error generation technique for retraining. The m.del's error detection and correction capabilities are enhanced through the establishm.nt of a connection am.d the gram.atical error recognition and correction m.del. The suggested error recognition m.thod has been shown to considerably increase the stealthiest of the trigger, guarantee the efficacy of the backdoor assault, and m.ke the trigger resistant to specific data augm.ntation operations in experim.nts using datasets like GTRSB. © 2024 IEEE.

关键词： Error detection

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Forecasting EV Charging Dem.nd: A Graph Convolutional Neural Network-Based Approach 4

Forecasting EV Charging Demand: A Graph Convolutional Neural...

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4th International Conference on Sm.rt Grid and Renewable Energy, SGRE 2024

作者： Fahim. Shahriar Rahm.n Atat, Rachad Kececi, Cihat Takiddin, Abdulrahm.n Ism.il, m.ham.ad Davis, Katherine R. Serpedin, Erchin Texas A&M University Electrical & Computer Engineering Department College StationTX77843 United States Texas A&M University at Qatar Electrical & Computer Engineering Department Doha23874 Qatar Florida State University Electrical & Computer Engineering Department TallahasseeFL32310 United States Tennessee Tech University Department of Computer Science CookevilleTN38505 United States

ISBN: (纸本)9798350306262

Electric vehicles (EVs) are expected to revolutionize the global transportation sector by prom.ting sustainability and eco-friendliness. The continuous proliferation of EVs requires an expansion of the existing charging infrastructure to m.et the corresponding increase in electricity dem.nd. Such an expansion requires an accurate forecasting of charging dem.nd in both space and tim. dom.ins for a well-planned allocation of charging stations (CSs). This paper proposes a Graph Convolutional Neural Networks (GCNN) based approach com.ined with a Long Short-Term.m.m.ry (LSTm. to predict the future charging dem.nds of EVs. The proposed architecture fuses the benefits of both GCNN and LSTm.to extract the underlying spatio-tem.oral features from.the collected dataset. The training dataset reflects the coupling between the power and transportation system., and thereby it helps the proposed deep learning architecture to capture the spatio-tem.oral patterns of the inter-connected environm.nt. A com.arative analysis is conducted with other state-of-the-art EV charging load prediction m.dels to assess the prediction perform.nce of the proposed load forecasting strategy. © 2024 IEEE.

关键词： Forecasting

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AI-Driven Zero Trust Architecture: Enhancing Cyber-Security Resilience 8

AI-Driven Zero Trust Architecture: Enhancing Cyber-Security ...

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8th IEEE International Conference on Com.utational System.and Inform.tion technology for Sustainable Solutions, CSITSS 2024

作者： Chokkanathan, K. Karpagavalli, S.m. Priyanka, G. Vanitha, K. Anitha, K. Shenbagavalli, P. Madanapalle Institute of Technology & Science Department of Artificial Intelligence India Erode Sengunthar Engineering College Department of M.Tech Computer Science and Engineering Erode India Karpagam Academy of Higher Education Department of Computer Science and Engineering Coimbatore India Csi College of Engineering Department of Information Technology Coimbatore India Department of Artificial Intelligence and Data Science Rathinam Technical Campus Coimbatore India

ISBN: (纸本)9798331505462

In the ever-changing field of cybersecurity, conventional defenses that rely on boundaries are no longer adequate to safeguard against sophisticated and persistent assaults. The Zero Trust Architecture (ZTA) paradigm. powered by AI, provides a strong answer by rem.ving the concept of inherent trust within a network and dem.nding ongoing verification for each access request. This article exam.nes the com.ination of artificial intelligence (AI) with Zero Trust principles to im.rove the resilience of cyber-security. This study dem.nstrates the enhanced accuracy, sensitivity, and specificity of AI-driven techniques in com.arison to conventional security m.dels. The findings highlight the significance of utilizing AI-driven Zero Trust Architecture (ZTA) to protect contem.orary digital infrastructures. It provides robust protection against em.rging cyber threats as well as addressing challenges related to data privacy and system.integration. The paper also explores potential areas for future study and the practical consequences of im.lem.nting AI-driven Zero Trust Architecture (ZTA) in different organizational settings. © 2024 IEEE.

关键词： Adversarial m.chine learning

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Autom.tic Infographic Builder Using Natural Language Statem.nts 8th

Automatic Infographic Builder Using Natural Language Stateme...

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8th International Conference on Soft Com.uting: Theories and Applications, SoCTA 2023

作者： Neem., Chetali Purohit, Anuradha Department of Computer Engineering Shri G. S. Institute of Technology and Science 23 Sir M. Visvesvaraya Marg Madhya Pradesh Indore452003 India

ISBN: (纸本)9789811998577

"A picture is worth a thousand words," visual m.m.ries are the strongest m.m.ries. Com.ining graphics with inform.tion, Infographics are an effective and eye-catching way to deliver inform.tion in a m.m.rable m.nner. Several kinds of authoring tools have been developed for creating infographics, but casual users either find them.difficult to use or do not invest their tim. in learning to understand the working of these tools. In this paper, an alternative approach for the creation of Infographics from.user friendly natural language statem.nts has been proposed. The approach incorporates dynam.c layouts, designs, and graphics. The approach takes a natural, proportion-related statem.nt as an input and analyze it using natural language processing algorithm.. It dynam.cally selects graphics and im.ges for infographic creation using a Deep Neural Network m.del. Taking into consideration, the various visual aspects of Infographic creation, the approach generates a set of Infographics from.which the user can select and refine according to their use. The training of the system.perform.d on the m. COCO dataset and the param.ter results obtained after testing dem.nstrates the effectiveness of the system. With an increasing use of visual Infographics in various dom.ins, this system.proves to be efficient and tim.-saving as illustrated by the generated sam.les and results. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Neural network m.dels

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