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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Borade, Monali R. Gupta, Mukur Faiz, Ayaan Acharjya, Kalyan Rajesh, A. Nunciya, P.M. Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Vivekananda Global University Department of Electrical Engineering Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science Engineering Faculty of Engineering and Technology Karnataka Ramnagar562112 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Master of Business Administration Chennai127 India

ISBN: (纸本)9798350319125

The developing call for real-time statistics analysis and selection-making has been a primary driver of improving deep studying models for the device getting to know. These models permit the evaluation of massive statistics sets, the generation of predictions from the statistics, and the speedy studying of new behaviors and patterns. Deep mastering fashions have been efficaciously used for various tasks, including natural language processing, computer imagination and prescient, and reinforcement studying. However, because of their complexity and a massive variety of parameters, they can be unreliable and volatile when uncovered to real-global records. This paper outlines strategies and techniques to lay out deep mastering fashions that can be robust to actual-world records. We also provide high-quality practices for deploying and retaining deep learning models in manufacturing packages. Those strategies include regularization techniques, model architectures, facts pre-processing techniques, and model validation and monitoring techniques. We additionally speak of the numerous strategies for assessing and improving the reliability of deep getting-to-know fashions. Subsequently, we provide a case study of a product that uses deep studying fashions to stumble on and classify objects in near-actual time in a manufacturing place. © 2023 IEEE.

关键词： improving maintaining production regularization techniques

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SafeRoute: Pioneering Online Ride Booking with Pega & ML-Powered Safety Measures

SafeRoute: Pioneering Online Ride Booking with Pega & ML-Pow...

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International conference of Electronics, Communication and Aerospace technology (ICECA)

作者： D. Rajalakshmi P.S. Immaculate S. Senthil Pandi A. Rehash Rushmi Pavitra V. Vijayalakshmi S. Sharanya Data Science and Business Systems SRM Institute of Science and Technology Chennai India Master of Business Administration Sri Sai Ram Institute of Technology Chennai India Computer Science and Engineering Rajalakshmi Engineering College Chennai India

ISBN: (数字)9798350367904

ISBN: (纸本)9798350367911

The advent of online ride booking services has revolutionized the transportation industry, providing users with convenient and efficient means of commuting. However, ensuring the safety of passengers and drivers remains a paramount concern. This project introduces an innovative solution that leverages Pega, a leading business process management platform, coupled with machine learning algorithms to enhance the safety measures within an online ride booking service. The proposed system integrates seamlessly into the existing online ride booking infrastructure and introduces advanced safety mechanisms based on realtime data analysis. Machine learning models are employed to predict and mitigate potential risks, ensuring a secure and reliable experience for both passengers and drivers.

关键词： Electric potential Machine learning algorithms Transportation industry Soft sensors Transportation Traffic control Safety Security Reliability Recruitment

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A Novel Development of Artificial Intelligence Enabled Learning Methodology for Human Depression Prediction Scheme

A Novel Development of Artificial Intelligence Enabled Learn...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Ramkumar, G. Priyadharshini, R. Selvi, K. Kalaiselvi, K. Rathika, S. Kanagavalli, N. Saveetha University Department of Ece Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India R.M.K Engineering College Department of Information Technology Tamil Nadu Chennai India Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences Department of Data Analytics Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Master of Business Administration Tamil Nadu Chennai India Rajalakshmi Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350375237

Human Depression Prediction is essential for several reasons, primarily centered around improving mental health outcomes and providing timely interventions. Firstly, early detection of depression allows for prompt and targeted interventions, enabling individuals to receive appropriate support and treatment before their condition worsens. The Human Depression Prediction Scheme (HDPS) introduces an innovative method for predicting depression by synergizing the capabilities of deep learning through the LeNet architecture with the optimization prowess of the Grey Wolf Optimization (GWO) algorithm. The proposed scheme achieves a commendable accuracy of 95%, attesting to its effectiveness in discerning depressive tendencies. Notably, the HDPS is implemented in Google Colab, emphasizing its accessibility and ease of use. This integration of advanced technologies and optimization techniques positions HDPS as a promising tool for early detection of depression, offering both high accuracy and practical implementation in a widely accessible computing environment. © 2024 IEEE.

关键词： Forecasting

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Safe Transportation System using IoT based Alcohol Detection 7

Safe Transportation System using IoT based Alcohol Detection

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7th International Conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Pradeepkumar, G. Vijayakumar, P. Chandrasekaran, N. Rohith Bhat, C. Senthilkumar, C. Kumar, Neelam Sanjeev KPR Institute of Engineering and Technology Department of Electronics and Communication Engineering Coimbatore India Kongu Engineering College Department of Master of Computer Applications Tamil Nadu Perundurai638060 India PSNA College of Engineering and Technology Department of Electrical and Electronics Engineering Dindigul624622 India Saveetha School of Engineering Department of Computer Science and Engineering SIM ATS Chennai India SIMATS University Saveetha School of Engineering Department of Electronics and Communication Engineering Chennai India Saveetha School of Engineering Department of Biomedical Engineering SIMATS Chennai India

ISBN: (纸本)9798350397253

Every year, drunk driving injures and sometimes kills a number of people. It is critical to investigate an IoT-based alcohol detection system for safe mobility. Because it uses sensors and a microprocessor to monitor the driver's BAC and cuts off the car's ignition if it suspects that the driver has been drinking while driving, this system can detect and prevent drunk driving. The proposed system will use IoT to remotely monitor and analyze the driver's Blood Alcohol Level (BAC). A communication module, a microprocessor, and an alcohol sensor comprise the system. The communication component sends real-time data to a BAC server for monitoring and analysis. The proposed strategy reduces the likelihood of accidents, improves driver and passenger safety, and lowers the likelihood of transportation companies having to pay for injuries or damages. IoT-based technologies that detect alcohol can aid in the prevention of drunk driving. By monitoring drivers' BACs in real time and discouraging drunk driving, this device has the potential to significantly reduce traffic-related injuries and fatalities. © 2023 IEEE.

关键词： Internet of things

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A Survey on Brain Tumor Segmentation Using Deep Learning Techniques

A Survey on Brain Tumor Segmentation Using Deep Learning Tec...

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International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES)

作者： Swarnalata Garapati Sangram Keshari Swain M. Vamsi Krishna Department of Computer Science and Engineering Centurion University of Technology & Management Bhubaneswar Odisha India Department of Master of Computer Applications Aditya University Surampalem Andhra Pradesh India

ISBN: (数字)9798331506452

ISBN: (纸本)9798331506469

Abnormal growth of cells is a characteristic of brain tumors, which can cause organ failure and adult mortality, brain tumors present serious risks to health. Because tumors are rare and can take a number of forms, predicting a tumor is difficult task. Tumors are rare and can present in a number of ways, making it challenging to estimate the survival rate of a patient with cancer. Magnetic resonance imaging (MRI) will recognize the tumor, which is important in identifying the tumor site. Although MRI is an essential tool for classifying cancers, its limited application requires advanced methods for accurate diagnosis. A survey on Brain tumor segmentation using deep learning is presented in this analysis. Analyzing previous brain tumor segmentation methods and identifying research gaps is the primary goal of this survey. In this survey explores various techniques for detecting, segmenting, and classifying brain tumors.

关键词： Surveys Deep learning Image segmentation Accuracy Image recognition Magnetic resonance imaging Brain tumors Mortality Manuals Signal processing

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Improving SVM Performance in Determining Cost and Kernel Parameter Values Through Grid Search

Improving SVM Performance in Determining Cost and Kernel Par...

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technology Innovation and Its Applications (ICTIIA), International Conference on

作者： Visca Sylvia Maya Silvi Lydia Benny Benyamin Nasution Master of Informatics Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Department of Computer Engineering and Informatics Politeknik Negeri Medan Medan Indonesia

ISBN: (数字)9798350351613

ISBN: (纸本)9798350351620

The purpose of this study is to improve the performance of Support Vector Machine (SVM) algorithm in sentiment analysis of trainee reviews through parameter optimization using Grid Search. Trainee reviews were taken from the Vocational and Productivity Training Center (BBPVP) Medan, which is the context of this study. The method used includes collection and preprocessing of review data, followed by the application of SVM for sentiment analysis. Grid Search is used to optimize the cost and kernel parameters of SVM. The results show that after optimization, the resulting SVM model shows significant performance improvement compared to the model before optimization. Accuracy increased from 68.75% to 71.96%, with improvements in other metrics such as precision, recall, and F1 score. These findings suggest that Grid Search is an effective method to optimize SVM parameters in sentiment analysis, providing a more accurate evaluation of the effectiveness of training programs at BBPVP Medan. This research makes an important contribution by applying hyperparameter optimization to SVM and suggests the application of this method for similar analysis in the future.

关键词： Support vector machines Training Productivity Sentiment analysis Analytical models Accuracy Costs Reviews Kernel Optimization

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A Survey on Brain Tumor Segmentation Using Deep Learning Techniques

A Survey on Brain Tumor Segmentation Using Deep Learning Tec...

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2nd International Conference on Signal Processing, Communication, Power and Embedded Systems, SCOPES 2024

作者： Garapati, Swarnalata Swain, Sangram Keshari Krishna, M. Vamsi Centurion University of Technology & Management Department of Computer Science and Engineering Odisha Bhubaneswar India Aditya University Department of Master of Computer Applications Andhra Pradesh Surampalem India

ISBN: (纸本)9798331506452

关键词： Brain

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Improved Accuracy of Decision Tree with XGBoost Technique in Cat Skin Disease Diagnosis

Improved Accuracy of Decision Tree with XGBoost Technique in...

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science and Information technology in Smart Administration (ICSINTESA), International Conference of

作者： Nanda Safrina Maya Silvi Lydia Benny Benyamin Nasution Master of Informatics Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Department of Computer Engineering and Informatics Politeknik Negeri Medan Medan Indonesia

ISBN: (数字)9798350376111

ISBN: (纸本)9798350376128

This research aims to develop an expert system for initial diagnoses of skin diseases in cats using the Decision Tree method. It assists cat owners in identifying skin diseases based on observed symptoms. Data from expert veterinarian drh. Ismi Azima, including various types of cat skin diseases and their symptoms, were used. The research employs 80% training data and 20% testing data to develop the classification model. Testing results indicate the Decision Tree model achieves up to 90% average accuracy in diagnosing cat skin diseases based on 50 datasets. This expert system aids in diagnosis and provides treatment and prevention solutions, beneficial for cat owners, especially in areas with limited veterinary access. In conclusion, this Decision Tree-based expert system effectively provides initial diagnoses of skin diseases in cats with high accuracy.

关键词： Accuracy Prevention and mitigation Training data Skin Decision trees Medical diagnosis Expert systems Diseases Testing Python

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Implementation and Evaluation of 0.6 µm CMOS Complex RCPF for Accurate Frequency Response Measurement

IEEJ Transactions on Electronics, Information and Systems

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IEEJ Transactions on Electronics, Information and Systems 2025年第1期145卷 25-37页

作者： Sato, Lina Fukagawa, Kanta Shouno, Kazuhiro Tanimoto, Hiroshi Muto, Cosy Moriyama, Seijiro Takahashi, Chikau Yoshino, Michitaka Graduate School of Science and Technology Degree Programs in Systems and Information Engineering Master’s Program in Computer Science University of Tsukuba Japan Division of Information Engineering University of Tsukuba 1-1-1 Tennoudai Ibaraki Tsukuba305-8573 Japan Kitami Institute of Technology 165 Koen-cho Hokkaido Kitami090-8507 Japan Faculty of Education Nagasaki University 1-14 Bunkyo-machi Nagasaki852-8521 Japan Anagix Corporation 3-22-27 Numama Kanagawa Zushi249-0004 Japan Takamori Corporation Japan Research Center for Micro-Nano Technology Hosei University 3-11-15 Midori-cho Koganei Tokyo184-0003 Japan

This paper describes the implementation and evaluation of an RC polyphase filter (RCPF) and circuitry for measuring its frequency characteristics. The integrated circuit is fabricated on a 0.6 µm CMOS process and its supply voltage is 5 V. The integrated circuit includes a filter under test (FUT), a four-phase square wave generator, several analog switches and differential amplifiers to amplify the high-impedance signals of the RCPF. This FUT is a third-order complex RCPF whose passband is 8 − 40 MHz. This paper describes the design of these circuits in detail. This integrated circuit can select two measurement methods by means of external control signals. The first method uses a four-phase square wave. The frequency characteristics are measured by using an on-chip four-phase square wave generator driven by an external clock generator and by a spectrum analyzer tuned to the fundamental frequency. The second method uses an external network analyzer. Furthermore, this integrated circuit is equipped with a calibration circuit to measure the frequency characteristics of the circuitry except FUT by passing through the input / output signals of the FUT. As a result, the frequency characteristics of the FUT itself can be accurately measured. The effectiveness is confirmed through evaluation of the fabricated integrated circuit. © 2025 The Institute of Electrical Engineers of Japan.

关键词： Differential amplifiers

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Improved Piano Music Recognition Algorithm Based on Artificial Intelligence 2

Improved Piano Music Recognition Algorithm Based on Artifici...

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2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Sahaya Sheela, M. Muthu Krishnammal, P. Shetty, Surendra Surendran, S. Vinothkumar, K. Venkataramanaiah, B. Department of ECE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Avadi India School of Electronics Engineering VIT-AP University Andhra Pradesh Amaravati India Department of Master of Computer Application NMAM Institute of Technology Nitte Deemed University Karnataka India Department of Computer Science and Engineering Tagore Engineering College Tamil Nadu Chennai India Department Of ECE PSNA College Of Engineering And Technology Tamilnadu Dindigul India Department Of ECE Veltech rangarajan Dr sagunthala R&D institute of science and Technology Chennai Avadi India

ISBN: (纸本)9798350305418

Not only do people enjoy listening to piano music, but it may also be differentiated from vocal music in the realm of digital music. With its ongoing development, the piano music recommendation algorithm has found solutions to a great number of practical issues, and its technical path has also progressed from its initial recommendation based on the behavioral preferences of users to the correlation recommendation among users to the mining of users' potential preferences in recent years. The birth of The ONE smart piano means that a new teaching mode will be produced, that is, piano learning and user experience will be integrated, and the latest achievements of modern science and technology will be applied and rooted in the field of music education and teaching, so that the piano, a large musical instrument, can be easily combined with a variety of intelligent devices. This is significant in terms of the combination of offline artificial intelligence and music education because it means that a new teaching mode will be produced. Additionally, this new teaching mode will The improved piano music recognition system is investigated in this study, which uses artificial intelligence as its foundation. In the beginning, both the overall architecture of the system and the functional module design plan are shown. It is well known that the feature matrix of a piano is distinct from that of other musical instruments. This distinction was discovered when a digital recognition approach was used to extract the music timbre feature matrix of a piano. The findings of the experiments indicate that the suggested system has a high recognition accuracy for the purpose of timbre recognition of piano arrangements, which demonstrates that it is deserving of further promotion and application. © 2023 IEEE.

关键词： Artificial intelligence improved recognition algorithm piano music

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