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A Real-Time System for Classification of Pakistan Sign Language using Machine Learning 17

A Real-Time System for Classification of Pakistan Sign Langu...

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17th International Conference on Open Source Systems and Technologies, ICOSST 2023

作者： Rahman, Muhammad Zia Ur Akbar, Muhammad Azeem Usman, Muhammad Tahir, Muhammad Hurrirah Shahzaib Riaz, Muhammad Tanveer Khan, Muhammad Abbas Lahore Faisalabad Campus Mechatronics and Manufacturing Engineering Dept. of Mechanical Faisalabad Pakistan Lappeenranta University of Technology Department of Information Technology Lappeenranta53851 Finland Lahore Faisalabad Campus Dept. of Mechatronics and Control Engineering Faisalabad Pakistan Balochistan University of Information Technology Engineering and Management Sciences Department of Electrical Engineering Quetta87300 Pakistan

ISBN: (纸本)9798350381320

Sign language is used by the deaf to interact with hearing individuals in the community. Despite the fact that sign language is often used, the general population is unaware of it, and only hearing-impaired people are familiar with its usage. In this article, we have created a system for real-Time sign language recognition in order to communicate with folks who are deaf or hard of hearing by using Pakistan Sign Language (PSL) as its sign language. Ten types or indications of PSL are differentiated in this study. Flex sensors in the glove can detect the position of certain fingers. The MPU-6050, a micro-electro-mechanical device is used to capture the unique characteristics of the hand's movement in a 3D space. In this setup, a Raspberry Pi 3B serves as the microcontroller. MPU-6050 and Flex sensors performance are integrated with additional elements including mean, energy, variance, and correlation for classification. In our investigation, we have collected information from over 3,000 movement feature samples from different people. To increase the precision of the proposed methodology, we have incorporated several machine learning classification algorithms. © 2023 IEEE.

关键词： Support vector machines

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Multi-Point Positional Insertion Tuning for Small Object Detection

Multi-Point Positional Insertion Tuning for Small Object Det...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Kanoko Goto Takumi Karasawa Takumi Hirose Rei Kawakami Nakamasa Inoue Dept. of Computer Science Institute of Science Tokyo Tokyo Japan Dept. of Systems and Control Engineering Institute of Science Tokyo Tokyo Japan

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Small object detection aims to localize and classify small objects within images. With recent advances in large-scale vision-language pretraining, finetuning pretrained object detection models has emerged as a promising approach. However, finetuning large models is computationally and memory expensive. To address this issue, this paper introduces multi-point positional insertion (MPI) tuning, a parameter-efficient finetuning (PEFT) method for small object detection. Specifically, MPI incorporates multiple positional embeddings into a frozen pretrained model, enabling the efficient detection of small objects by providing precise positional information to latent features. Through experiments, we demonstrated the effectiveness of the proposed method on the SODA-D dataset. MPI performed comparably to conventional PEFT methods, including CoOp and VPT, while significantly reducing the number of parameters that need to be tuned.

关键词： Computational modeling Object detection Signal processing Feature extraction Encoding Acoustics Speech processing Tuning

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Detecting Schizophrenia through EEG signals using Deep Learning

Detecting Schizophrenia through EEG signals using Deep Learn...

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International Conference on Biosignals, Images and Instrumentation (ICBSII)

作者： Ashish Make Swarnalata Bollavarapu Radhika Baid Devansh Gupta Dept. of Computer Engineering Mukesh Patel School of Technology Management and Engineering NMIMS Deemed-to-be-University Mumbai India

ISBN: (数字)9798331513863

ISBN: (纸本)9798331513870

Millions of people between the ages of 16 to 30 are diagnosed with Schizophrenia every year. Schizophrenia is a serious mental disorder characterized by abnormalities in the individual’s cognition, emotional as well as physical responses. This study examines the detection of schizophrenia using electroencephalogram (EEG) signals collected from the human brain. EEG signals provide valuable information about cognitive responses of an individual and have the potential to serve as a biomarker for diagnosing schizophrenia. This study analyzes a large dataset of EEG signals from people with and without schizophrenia using advanced machine learning and signal processing techniques. We aim to demonstrate a robust and accurate classification model for distinguishing schizophrenic patients from those not affected by it, based on data recorded through Electroencephalogram signals. Various features are extracted from EEG signals, including frequency domain measurements, event-related potentials, and connectivity patterns. These features are then used to train and evaluate various machine learning as well as deep learning algorithms such as convolutional neural networks (CNNs), Ensemble Methods, Support Vector Machines (SVMs), etc. Performance of the deep learning model was evaluated using various statistical metrics like accuracy, sensitivity, specificity, and the area under receiver operating curve (AUC-ROC). It is found that the potential of EEG-based methods for early schizophrenia detection and monitoring, improving clinical diagnosis and treatment strategies.

关键词： Deep learning Support vector machines Electric potential Accuracy Schizophrenia Brain modeling Feature extraction Electroencephalography Convolutional neural networks Testing

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Plants Diseases Prediction Framework: A Image-Based System Using Deep Learning

Plants Diseases Prediction Framework: A Image-Based System U...

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2022 IEEE World Conference on Applied Intelligence and Computing, AIC 2022

作者： Kirola, Madhu Joshi, Kapil Chaudhary, Sumit Singh, Neha Anandaram, Harishchander Gupta, Ashulekha Uttaranchal University Dept. of Computer Engineering Dehradun India Indrashil University Dept. of Computer Engineering Kadi Rajpur India Amrita Vishwa Vidyapeetham Centre for Excellence in Computational Engineering and Networking Tamil Nadu Coimbatore India Graphic Era University Dept. of Management Studies Dehradun India

ISBN: (数字)9781665479882

ISBN: (纸本)9781509050017

Plant diseases mostly harm the leaves, resulting in a loss in agricultural output's quality and quantity. Plant disease is the most common cause of large-scale crop mortality. India is a country where people's livelihoods are heavily reliant on agriculture. The disease has caused chaos in the agricultural industry. The human eye's perception is not quite as sharp as it needs to be to notice minute variations in the sick leaf region. It needs a complex process that requires both plant expertise and a large amount of processing time. As a result, plant diseases can be detected using machine learning. The disease detection method includes image acquisition, image pre-processing, image segmentation, feature extraction, and classification. To prevent crops at the initial stage from diseases, it is essential to develop an automatic system to diagnose plant diseases and identify its category. The goal of the proposed research is to examine several machine algorithms for plant disease prediction. The paper proposed a framework for disease and healthiness detection in plants and the classification of diseases based on symptoms appearing on a leaf. The diseases are grouped into three categories in the paper: bacterial, viral, and fungal. To conclude, the research paper investigates all of these factors and uses several machine learning(DL) techniques and deep learning(DL) techniques. The machine learning(ML) techniques used in the research work are SVM, KNN, RF(Random Forest), LR (Logistic Regression), and the deep learning(DL) technique used is-Convolutional Neural Network(CNN) for disease prediction in the plants. Following that, a comparison of machine learning and deep learning methodologies was conducted. The RF(Random forest) has the highest accuracy of 97.12 % among machine learning classifiers, however, in comparison to the deep learning model mentioned in the study, the CNN classifier has the highest accuracy of 98.43 % © 2022 IEEE.

关键词： Image segmentation

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Performance Evaluation of Diverse ML Techniques in Disasters Twitter classification 2

Performance Evaluation of Diverse ML Techniques in Disasters...

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2nd IEEE International Conference on Networks, Multimedia and Information Technology, NMITCON 2024

作者： Nath, Basudev Sahoo, Deepak Biswal, Bhramara Bar Patra, Sudhansu Shekhar Ray, Rakesh Kumar Sri Sri University Faculty of Emerging Technologies Cuttack India Giet University Dept. of Computer Science & Engineering Gunpur India Kiit Deemed to be University School of Computer Applications Bhubaneswar India Centurian University of Technology and Management Mca Department Khordha India

ISBN: (纸本)9798350372892

Social media has grown to be an essential and significant aspect of our lives in the modern day. Due to the widespread usage of laptops, mobile phones, and other devices, individuals find it simpler to communicate with one another and to post their opinions, concerns, and other information on social media sites like Facebook, Instagram, and tweeter. Several government and commercial organizations use these social media platforms to gather text and assess people's sentiments during times of crisis. Tracking the tweets relating to the accident is too tough for them to do. To categories tweets as 'disaster' or 'non-disaster,' the paper implemented three models, the Naïve-Bayes, KNN and support vector machine classification models. The Naïve-Bayes algorithm shows the highest accuracy of 86.591. © 2024 IEEE.

关键词： Tweets

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Forecasting Residential EV Charging Behavior: A Transfer Learning-Enabled Approach to Address Data Scarcity in the Residential Sector

Forecasting Residential EV Charging Behavior: A Transfer Lea...

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Smart Grid Conference (SGC)

作者： Mohammad Rastegar Mehrdad Ebrahimi Ali Forootani Dept. of Power and Control Engineering School of Electrical and Computer Engineering Shiraz University Shiraz Iran Dept. of Electrical and Software Engineering University of Calgary Calgary Canada

ISBN: (数字)9798331511333

ISBN: (纸本)9798331511340

Charging a significant number of electric vehicles (EVs) can pose challenges for power systems, and a viable solution is smart charging. Reliable smart charging can be achieved by forecasting the required energy and plug-in duration of a charging event. However, despite the availability of data from public charging stations, the lack of sufficient real data for residential EV charging has consistently been a barrier, particularly when using deep learning methods. Moreover, enhancing the accuracy of forecasting residential EV charging behavior is crucial due to its significant impact on power systems. To address these challenges, this paper proposes an intelligent deep learning-based framework to forecast the required energy and plug-in duration of residential EV charging events by leveraging transfer learning. Additionally, the proposed framework utilizes an auxiliary deep learning model that forecasts charging duration to improve the accuracy of required energy forecasting. Various case studies have been conducted, and the results show that the proposed framework significantly improves the accuracy of residential charging behavior forecasting.

关键词： Deep learning Accuracy Transfer learning Predictive models Power system reliability Reliability State of charge Forecasting Energy management Smart charging

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Is Gamification a Practical Solution for Increasing Repurchase Intention of Online Customers?

Is Gamification a Practical Solution for Increasing Repurcha...

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2023 Congress in computer Science, computer engineering, and Applied Computing, CSCE 2023

作者： Safari, Khalil Safari, Zohreh Keshavarzi, Fatemeh Payam Noor University Dept. of Business Management and Information Technology Tehran Iran Elizabeth City State University Dept. Computer Science Math Engineering Technology Elizabeth City United States Qazvin Islamic Azad University Dept.Information Technology Engineering Qazvin Iran

ISBN: (纸本)9798350327595

With the growth of online shopping, organizations should update their portals and websites in response to changing conditions and competitors' actions. To attract and retain online customers, various tools such as reputation building, trust en-hancement, and gamification are utilized and reinforced. This study has investigated 243 users of DigiKala company through paper and electronic questionnaires. Due to the non-normality of the data, non-parametric statistical methods using SPSS and Smart-PLS3.0 software were employed to analyze structural relationships. Additionally, machine learning tools were utilized to optimize the analysis of customer behavior and identify patterns for effective customer retention strategies. The results show that contrary to the research background, the use of gamification on DigiKala's website has not had a positive and significant impact on customers using this company's website and their repurchase intention. Thus, it seems that DigiKala should focus on context and infrastructures, while simultaneously strengthening and revamping gamification dimensions. © 2023 IEEE.

关键词： Gamification intention to use repurchase intention Reputation Trust in vendor

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Synergistic Integration of Photovoltaic and the Grid: An Intelligent ANFIS-Governed Interleaved Boost Converter Approach

Synergistic Integration of Photovoltaic and the Grid: An Int...

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2024 International Conference on Advancement in Renewable Energy and Intelligent Systems, AREIS 2024

作者： Balaji, V. Ganga, M. Barathi, K. Gouda, Pramod Kumar Prasad, Rajesh Venkatesh, Mammula MAI-NEFHI College of Engineering and Technology Department of Electrical and Computer Engineering Asmara Eritrea Hindusthan College of Engineering and Technology Department of Biomedical Engineering Coimbatore India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Dept. of Eee Tamil Nadu India Aditya Institute of Technology and Management Dept. of Eee Andhra Pradesh Srikakulam India Stella Mary's College of Engineering Dept. of Eee Tamil Nadu India Avanthi's Research and Technological Academy Dept. of Eee A. P Vizianagaram531162 India

ISBN: (纸本)9798350387230

The transition towards Renewable Energy Sources (RES), mainly solar power is crucial in addressing the depletion of fossil fuel-based energy supplies and mitigating environmental concerns. This paper proposes an innovative approach to enhance efficiency of PhotoVoltaic (PV) systems by integrating a two-phase Interleaved Boost (IB) converter with an Adaptive Neuro Fuzzy Inference System (ANFIS) controller. The ANFIS controller optimizes the operation of the IB converter by adjusting the duty cycle based on two key input parameters: solar irradiation and temperature. This adaptive control method effectively transforms solar energy into electrical power, maximizing the PV system's power production. By using the ANFIS mechanism, the suggested system improves total output power, decreases overshoot, and produces a higher voltage output than conventional techniques, all of which lead to increased performance and dependability. The IB converter operates with an efficiency of 96.15% and minimized settling time. © 2024 IEEE.

关键词： Adaptive control systems

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Fine-Tuning of Large Language Models for Solving DSA Problems Using QLoRA

Fine-Tuning of Large Language Models for Solving DSA Problem...

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2025 International Conference on Computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Pavan Kalyan, S. Arun Kumar, B.R. Satish Kumar, T. Prem Raj, S. Patil, Pratik B. Harshini, Pothireddy Bms Institute of Technology and Management Dept. Computer Science and Engineering Karnataka Bangalore5600119 India

ISBN: (纸本)9798331538538

Fine-tuning large language models (LLMs) for domain specific tasks is often an expensive resource intensive procedure requiring large computing and memory resources. In this paper, We introduce finetuned-leetcode-CodeLlama-7b, a version of CodeLlama that was fine tuned for solving LeetCode problems, specifically. Using QLoRA (Quantized Low Rank Adaptation) techniques, we show that smaller models achieve high performance when solving Data Structure and Algorithm (DSA) problems, providing a cost effective alternative to larger models. The proposed incremental approach reduces memory and computational needs, keeping high accuracy while providing advanced problem-solving in resource constrained environments. © 2025 IEEE.

关键词： Digital differential analyzers

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Mobile Application for Visualizing the Measurements 5

Mobile Application for Visualizing the Measurements

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5th IEEE International Conference on Modern Electrical and Energy System, MEES 2023

作者： Kiresova, Simona Guzan, Milan Sobota, Branislav Melnykov, Viacheslav Yatsiuk, Rostyslav Peryvertailo, Bohdan Technical University of Košice Dept. of Theoretical and Industrial Electrical Engineering Košice Slovakia Technical University of Košice Dept. of Computers and Informatics Košice Slovakia Kremenchuk Mykhailo Ostrohradskyi National University Dept. of Systems of Automatic Control and Electric Drive Kremenchuk Ukraine National University Dept. of Computer Engineering and Electronics Kremenchuk Mykhailo Ostrohradskyi Kremenchuk Ukraine

ISBN: (纸本)9798350359787

This paper deals with the design of an Android mobile application and visualization of the measured values of particulate matter and meteorological factors from the measurement stations. The application can in principle be effectively used in any field of data visualization. The architecture of the mobile application, the security, the use of AWS services to access the data in the InfluxDB databases, as well as the user interface and graphical visualizations of the measured data are described and illustrated. The application is user tested and the paper documents their first experiences using the mobile application. © 2023 IEEE.

关键词： Visualization

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