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共 232 条记录，以下是41-50 订阅

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Emerging VLSI Technologies for High performance AI and ML Applications 2

Emerging VLSI Technologies for High performance AI and ML Ap...

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2nd international conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Nagar, Prashray Boruah, Sashwat Bhoi, Akash Kumar Patel, Arpita Sarda, Jigar Darjij, Pallavi Charotar University of Science and Technology V T Patel Department of Electronics & Communication Engineering India Sikkim Manipal University Research Gangtok India Charotar University of Science and Technology M & v Patel Department of Electrical Engineering India Dharamsinh Desai Univeristy Department of Electronics & Communication Engineering Nadiad India

ISBN: (纸本)9798350370188

The capabilities of artificial intelligence (AI) and machine learning (ML) algorithms are constantly expanding, necessitating efficient and high-performance hardware systems. We have investigated the creation of hardware accelerators based on VLSI that are intended to effectively manage the heavy workloads of machine learning jobs, also explored low-power VLSI architectures that preserve computing capabilities while reducing power consumption to solve energy efficiency issues in AI and ML systems. To balance performance and energy utilization, power management strategies and circuit design improvements are analysed. The study emphasizes hardware-software co-design techniques, considering the integration of VLSI-based hardware accelerators with software frameworks to obtain optimal performance and flexibility, to address the complexity and scalability of AI and ML systems. We also examined the cutting-edge VLSI technologies that have the potential to support powerful AI and ML applications. The speed and effectiveness of AI and ML algorithms could be improved significantly by these technologies, which include neuromorphic computing, approximation computing, and in-memory computing. The research also discusses the difficulties in designing VLSI for AI and ML algorithms, and its possible solutions for challenges including design complexity, scalability, memory management, and data mobility. © 2024 IEEE.

关键词： Energy efficiency

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FPGA Suitability for Implementation of Pedestrian Detection algorithms for Autonomous Driving Application 2

FPGA Suitability for Implementation of Pedestrian Detection ...

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2nd international conference on Artificial Intelligence and machine learning Applications, AIMLA 2024

作者： Gupta, Isha Kaur, Deepti Prit Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350349221

An emerging field of study called autonomous driving seeks to create self-driving cars that can guarantee the safety of both passengers and other road users. Although several countries have already developed basic autonomous vehicles, more research is required for more sophisticated automation. Numerous different computer vision (CV) algorithms are needed to handle the extensive elements in an autonomous vehicle. Although many computer vision algorithms have already been built as software, they still require a stable hardware platform in order to be tested and used. Due to the many advantages it offers, Field Programmable Gate Array (FPGA) has become the most popular hardware platform. This paper addresses the significance and necessity of autonomous vehicles, highlighting one of their most difficult features - pedestrian detection. The paper also reviews the numerous advantages of FPGAs specifically for the development of autonomous driving challenging features like pedestrian detection and it also documents that how different researchers have used FPGA's as their hardware platform to construct self-driving car's features with an eye towards potential future improvements. © 2024 IEEE.

关键词： Autonomous vehicles

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Design and Development of a Smart Antenna System for Electromagnetic Compatibility 2

Design and Development of a Smart Antenna System for Electro...

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2nd international conference on Artificial Intelligence and machine learning Applications, AIMLA 2024

作者： Goyal, Manish Kumar Malathi, H. Choudhary, Binod Kumar Vivekananda Global University Department of Computer Science & Application Jaipur India School of Sciences Department of Biotechnology and Genetics Karnataka Bangalore India ARKA JAIN University Department of Electrical & Electronics Engineering Jharkhand Jamshedpur India

ISBN: (纸本)9798350349221

This technical abstract introduces the design and development of a smart Antenna gadget for Electromagnetic Compatibility (EMC). The clever antenna device pursuits to lessen unwanted interference in wireless verbal exchange systems, and also to locate time Electromagnetic interference (EMI) alerts and optimize running conditions. This antenna gadget is built on both software and hardware systems. Its design relies on the improvement of antenna arrays, which make use of adaptive sign-processing techniques with the intention of reaping the desired EMC necessities. The antenna array, with its signal-processing architecture, gives the possibility of at the same time optimizing radiation samples and electrical parameters by way of electronically scanning the antenna in more than one direction and taking into consideration disturbances in the environment. The layout additionally incorporates an optimized weight matrix that is used to optimize the antenna's signal-processing structure. Subsequently, the advanced system is simulated and evaluated for its overall performance, and the results are compared with theoretical predictions. © 2024 IEEE.

关键词： Antenna arrays

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Dynamic Power Transfer for Moving Electric Vehicle Charging and Fault Detection Using IoT 2

Dynamic Power Transfer for Moving Electric Vehicle Charging ...

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2nd international conference on Artificial Intelligence and machine learning Applications, AIMLA 2024

作者： Sridevi, A. Loheshkumar, S. Krishnaraj, D. Muthuprakash, A. Kawaskar, R. M. Kumarasamy College of Engineering Department of Electronics and Communication Engineering Tamilnadu Karur639 113 India

ISBN: (纸本)9798350349221

The most crucial component of any electric vehicle (EV) is its battery storage, which holds the energy required for the vehicle to run. Therefore, an effective battery management system must exist in order to maximize a battery's output and guarantee its power *** keeps an eye on the parameters, assesses the hardware and software, and offers the required services to guarantee the battery operates safely.. The proposed system only monitors the battery and Power transfer but also protect it to avoid accidents from occurring. The proposed model has following functions current, voltage measurement, , protection, battery Fault detection, liquid crystal display (LCD) etcAutomobiles with one or more electric motors that get their power from rechargeable batteries as opposed to internal combustion engines (ICEs) that run exclusively on fossil fuels are known as electric vehicles (EVs). It monitors and controls the operation of the battery pack, ensuring its optimal performance, safety, and longevity. It indicates how much charge is available in the battery at a given time, allowing users to estimate the remaining range or usage time before recharging is required. © 2024 IEEE.

关键词： Fault detection

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Application of Computer software in Chemical Data Processing using Spider Monkey Optimization Algorithm 2

Application of Computer Software in Chemical Data Processing...

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2nd IEEE international conference on Networks, Multimedia and Information Technology, NMITCON 2024

作者： Rana Veer Samara Sihman Bharattej, R. Rupavath, Rana Veer Samara Sihman Brahmatej Hussein, Abbas Hameed Abdul Anjaneyulu, Madugula Upendra Roy, B.P. Saint Leo University Department of Computer Science St Leo United States Indiana Wesleyan University Marion United States Ahl Al Bayt University College of Pharmacy Karbala Iraq Gokaraju Rangaraju Institute of Engineering and Technology Bachupally Department of Ai&ml Hyderabad India Channabasaveshwara Institute of Technology Gubbi Department of Electronics and Communication Engineering Tumkur India

ISBN: (纸本)9798350372892

The development of chemical-based products and functional materials is crucial for modern technologies, yet it often entails significant costs and time delays. Artificial intelligence and machine learning present novel approaches to leverage data for addressing these challenges;however, they encounter specific hurdles in the chemical domain. To address these issues, a software platform has been introduced to facilitate training and development of Recurrent Neural Networks (RNN). This platform incorporates the Spider Monkey Optimization (SMO) algorithm to optimize model's hyperparameters, including neurons number, rate of learning, and activation function. This software package goes beyond neural network training and development by encompassing design and operating-parameter optimization, essential aspects of chemical engineering processes. Within this framework, the weights that were optimized and bias obtained from neural network were stored and utilized in other SMO process for optimizing important process variables, like swiftness and temperature, according to user specifications. © 2024 IEEE.

关键词： software packages

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A Comparative Evaluation for False Data Injection Attacks using Bagging Classifier and XGB Classifier in enhancing the Control System Security 2

A Comparative Evaluation for False Data Injection Attacks us...

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2nd international conference Computational and Characterization Techniques in engineering and Sciences, IC3TES 2024

作者： Niharika, G. Sivasankar, C. Saveetha University Saveetha School of Engineering Saveetha Institution of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350364699

To secure control systems against false data injection attacks using Bagging Classifier compared with XGB Classifier. The open-source lab at Saveetha School of engineering served as the study site for this project. The necessary data has been gathered, preprocessed, and comparison metrics acquired for the research. The Bagging Classifier and XGB Classifier are the two groups involved in this study. We compared the accuracy of each of these machine-learning algorithms using various sample sizes. For the Identification of False Data Injection Attacks utilizing Bagging Classifier and XGB Classifier to increase accuracy, Bagging Classifier of value of 98.9% is more accurate when compared to XGB Classifier value of 97.2%. As the values have been discussed earlier, the significance value is yielded by the software as p=0. Values of p © 2024 IEEE.

关键词： Cyber attacks

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From Data to Diagnosis: Investigating Approaches in Mental Illness Detection 2

From Data to Diagnosis: Investigating Approaches in Mental I...

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2nd international conference of Intelligent Methods, Systems and Applications, IMSA 2024

作者： Montasser, Reem Kadry Mazen, Sherif A. Helal, Iman M. A. October University for Modern Sciences and Arts Faculty of Computer Science Software Engineering Department Giza Egypt Cairo University Faculty of Computers and Artificial Intelligence Information Systems Department Giza Egypt

ISBN: (纸本)9798350362633

Mental illness is a considerable global public health problem, impacting both individual well-being and society's health. The growing popularity of social media and the increase of other data sources led to more research interest in detecting mental illness with a particular focus on utilizing and fusing emotional information. Artificial intelligence techniques like natural language processing and others, have shown encouraging improvements in recognizing complex relationships in a wide range of textual data, including clinical notes, social media posts, and interviews. In this paper, we assess the different branches of artificial intelligence based on previous research in mental illness detection, discussing methods, trends, challenges, and future research directions. © 2024 IEEE.

关键词： Adversarial machine learning

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Ensemble of Deep learning Models and machine learning Classifiers for the Classification of Brain Tumors 2

Ensemble of Deep Learning Models and Machine Learning Classi...

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2nd international conference on Computer, Communication and Control, IC4 2024

作者： Bhagyalaxmi, K. Dwarakanath, B. Reddy, P. VijayaPal School of Computing Srm Ist Department of Computer Science and Engineering Tamilnadu Kattankulathur India Srm Ist It Department Tamilnadu Ramapuram India Matrusri EngineeringCollege Cse Department Hyderabad India

ISBN: (纸本)9798350387933

Brain tumors are typically one of the more complex health issues in the world. It could result in death if not addressed in the beginning stages. It is important to adopt an automated system to diagnose tumors. Here this article proposes a model to automate the system. The proposed model unites two separate Convolutional Neural Network (CNN) architectures, VGG16 and ResNet-50, to improve the accuracy and robustness of brain tumor detection. The model is trained and evaluated on a comprehensive dataset of brain tumors for encompassing three distinct tumor types: pituitary, meningiomas, and glioma tumors. The proposed model uses MRI images for preprocessing and feature extraction through pre-trained CNNs. Various machine learning classifiers like SVM, KNN, LR, and RF are applied. Finally, an ensemble of classifiers, including LR, RF, and XGBoost, is used for tumor detection and classification. The model is evaluated on brain tumor dataset which is publicly available. The output demonstrates that the ensemble approach outperforms the individual classifiers, achieving an overall accuracy of 93.43%. The primary aim is to equip doctors with high-quality software for tumor identification and understanding their origins. This software is designed to save patients' time, offer timely solutions during the early stages, and enable swift consultations. © 2024 IEEE.

关键词： Tumors

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Prediction of Brain Tumor Severity with Magnetic Resonance Imaging using Ridge Regression Over Recurrent Neural Network 2

Prediction of Brain Tumor Severity with Magnetic Resonance I...

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2nd IEEE international conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Bilal, D. Mohamed Tamilselvan, S. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350306415

The goal of the research work is to predict Brain tumors using the Ridge Regression algorithm and compare it with the Recurrent Neural Network. The research study involves two groups, Ridge Regression and Recurrent Neural Network, which were both examined using a sample size of 10 calculated with an alpha value of 0.05 and a confidence interval of 95% using the ClinCalc software. The image analysis dataset used in this study contains 82,347 tuples and was obtained from ***. The Ridge Regression algorithm obtained an accuracy of 96.47%, while the Recurrent Neural Network classifier achieved an accuracy of 90.11%. An independent sample t-test conducted in SPSS statistical analysis indicated a statistically significant difference between the two algorithms with a p-value of 0.002 (p © 2024 IEEE.

关键词： Brain Tumor Human machine learning Magnetic Resonance Imaging Recurrent Neural Network Ridge Regression

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Agribot: An IoT and Deep learning-Based System Empowering Precision Farming 2

Agribot: An IoT and Deep Learning-Based System Empowering Pr...

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2nd international conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Thangammal, C. Bennila Poorna Saai, M. Muhammad Taufeeq Umar, N. Nithissh, S.S. Murugesh, K. R. M. D. Engineering College Electronics and Communication Engineering Kavaraipettai India R. M. D. Engineering College Artificial Intelligence and Machine Learning Engineering Kavaraipettai India

ISBN: (纸本)9798350328202

Farmers frequently encounter issues such as irregular weather patterns and ineffective resource management, particularly in irrigation practices. Offering them a centralized information platform equipped with vital data for monitoring and managing their fields has the potential to be a game changer. This paper describes an advanced smart agricultural system that combines both cutting-edge software and hardware modules to transform current farming practices. The hardware components, which include data collecting and soil quality assessment sensors, work together with machine learning models to provide accurate weather forecasts and crop recommendations. Our technology offers a high degree of precision in predicting and advising suitable crop choices based on real-time soil conditions by utilizing closely trained models. The user-friendly online interface offers safe authentication mechanisms, complete irrigation status updates, and crop-specific information. The system's distinctive strength comes in its ability to handle soil data, allowing it to provide individualized crop suggestions and exact planting directions. This research not only provides farmers with actionable knowledge but also represents a big step toward more sustainable and efficient farming practices. The study finishes with suggestions for potential future refinement avenues, highlighting the ongoing commitment to enhancing precision farming technologies. © 2024 IEEE.

关键词： Crops

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