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Iraqi Journal for computer Science and Mathematics 2022年第1期3卷 64-70页

作者： Al-Mashhadani, Mohammed Ibrahim Hussein, Kilan M. Khudir, Enas Tariq Ilyas, Muhammad Department of Electrical and Electronics Engineering Altinbas University Istanbul Turkey Diyala University Electronic Computer Center Iraq University of Technology Electrical and Electronics Engineering Altinbas University Turkey

Nowadays, in many real-life applications, sentiment analysis plays a vital role in the automatic prediction of human activities, especially on online social networks (OSNs). Since the last decade, the research on opinion mining and sentiment analysis has been growing with the increase in volume of online reviews available over the social media networks, such as Facebook OSNs. Sentiment analysis falls under the data mining domain research problem. Sentiment analysis is a type of text mining process to determine subjective attitude, such as sentiments from written texts, and hence has become a main research interest in domains of natural language processing and data mining. The main task of sentiment analysis is to classify human sentiments with the objective of classifying the sentiment or emotion of end users based on their specific text on the OSNs. Several research approaches have been designed for sentiment analysis, in which the factors of accuracy, efficiency and speed have been used to evaluate the effectiveness of sentiment analysis methods. The Map-Reduce framework under the domain of big data is used to minimise the speed of execution and efficiency recently with many data mining methods. The sentiment analysis of messages on Facebook OSNs is more challenging compared with those of other sites because of the misspellings and slang words in their Twitter dataset. In this study, the different solutions that have been recently presented are discussed in detail. Then, a new approach for sentiment analysis based on hybrid feature extraction and multi-class support vector machine methods is proposed. The algorithms have been designed using big data techniques in view of optimising the performance of sentiment analysis. © 2022 Iraqi Journal for computer Science and Mathematics. All rights reserved.

关键词： Sentiment analysis

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Development of Area-Based Explosive Trace Detection Using Deep Transfer Learning 1

Development of Area-Based Explosive Trace Detection Using De...

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1st International Conference on Innovative engineering Sciences and Technological Research, ICIESTR 2024

作者： Ohemu, Monday F. Azeta, Ambrose A. Adeyanju, Ibrahim A. Obasi, Chukwuemeka C. Airforce Institute of Technology Department of Electrical/Electronics Engineering Kaduna Nigeria Namibia University of Science and Technology Department of Software Engineering Namibia Federal University Department of Computer Engineering Oye-Ekiti Nigeria Edo State University Department of Computer Engineering Uzairue Nigeria

ISBN: (纸本)9798350348637

Terrorism poses a significant global threat, necessitating comprehensive security measures to protect people and critical infrastructure. With the increasing frequency of explosive-based attacks, there is a pressing need for advanced detection systems to mitigate potential devastation. This paper presents the development of an area-based explosive trace detection system using deep transfer learning. Leveraging deep learning technology trained on a substantial dataset collected from sensor networks, the proposed model, named Deep Transfer Learning for Explosive Trace Detection (DTLETD), demonstrates remarkable capabilities in classifying explosive gases based on concentrations of Carbon (C), Hydrogen (H), Oxygen (O), and Nitrogen (N). The methodology involves converting the dataset into 2D data using a serial data to image generator, followed by the adaptation of a pre-trained model for transfer learning. The DTLETD model outperformed conventional Convolutional Neural Network (CNN) models, achieving a training time reduction of about 92 seconds compared to 1287 seconds for the CNN model. Additionally, the transfer learning model exhibits faster convergence with nearly zero losses during training and validation, yielding an impressive accuracy of 99.7% and an average AUC value of approximately 0.89. This research significantly advances the field of explosive trace detection by integrating machine learning-based approaches with deep transfer learning techniques, thereby enhancing security protocols and mitigating potential threats. © 2024 IEEE.

关键词： Convolutional neural networks

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Predictive Analytics for Business Decision-Making: Unleashing the Power of Data-driven Insights 15

Predictive Analytics for Business Decision-Making: Unleashin...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Nishat, Nourin Mehedi, Mithila Rashid, Saidur Raasetti, Muniroopesh Lamar University Department of Management Information Systems TX United States Rajshahi University of Engineering and Technology Department of Electrical and Computer Engineering Rajshahi6204 Bangladesh Lamar University Department of Electrical and Electronics Engineering TX United States Lamar University Department of Electrical Engineering TX United States

ISBN: (纸本)9798350370249

This study examines how Predictive Analytics may alter company decision-making by employing sophisticated data-driven insights to improve strategic decisions. The report examines predictive analytics' methods, algorithms, and applications, showing how it has transformed industry decision-making. Predictive Analytics' ability to anticipate future trends, recognize patterns, and generate accurate forecasts based on previous data is examined in corporate operations. The paper uses case studies and practical examples to show how predictive models enhance risk management, resource efficiency, and strategic planning. Business decision-making is explored in relation to predictive analytics components including machine learning algorithms, statistical modeling, and data mining. The study highlights predictive analytics' benefits in sales forecasting, consumer behavior research, and market trend projections. The study also examines the social and economic effects of corporate predictive analytics. It examines how data-driven decision-making boosts competitiveness, innovation, and economic development, serving national interests. For a balanced and transparent deployment, ethics, data privacy, and predictive analytics are considered. This study shows how predictive analytics can help firms make smart decisions in an age of unparalleled data availability. The study shares successful applications and addresses problems to help business professionals, analysts, and researchers navigate datadriven business strategies. © 2024 IEEE.

关键词： Business Decision Making Linear Regression Polynomial Regression Predictive analytics

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A Comprehensive Technique Based on Machine Learning for Device and Circuit Modeling of Gate-All-Around Nanosheet Transistors

IEEE Open Journal of Nanotechnology

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IEEE Open Journal of Nanotechnology 2023年 4卷 181-194页

作者： Butola, Rajat Li, Yiming Kola, Sekhar Reddy National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Institute of Communications Engineering the Institute of Biomedical Engineering the Department of Electronics and Electrical Engineering the Institute of Pioneer Semiconductor Innovation and the Institute of Artificial Intelligence Innovation Hsinchu300093 Taiwan

Machine learning (ML) is poised to play an important part in advancing the predicting capability in semiconductor device compact modeling domain. One major advantage of ML-based compact modeling is its ability to capture complex relationships and patterns in large datasets. Therefore, in this paper a novel design scheme based on dynamically adaptive neural network (DANN) is proposed to develop fast and accurate compact model (CM). This framework constitutes a powerful yet computationally efficient methodology and exhibits emergent dynamic behaviors. This paper demonstrates that the compact model based on ML can be designed to replicate the performance of conventional compact model for nanodevices. For this work, gate-all-around (GAA) nanosheet (NS) device characteristics are comprehensively analyzed for process variability sources using the proposed model. The device geometry parameters such as channel length, nanosheet width and nanosheet thickness are fed as input features to the DANN model. The adaptive neural network learns dynamically by updating weights of the model in accordance with the input features and achieves accurate neural weight convergence. The proposed model predicted the electrical characteristics of NS devices with less than 1% error rate. The model is also implemented and validated for the simulations of digital circuit designs such as inverter, and logic gates. ©;2023 The Authors.

关键词： Circuit simulation

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Design and Development of Steering Controlled Powered Wheelchair 6

Design and Development of Steering Controlled Powered Wheelc...

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6th Parul International Conference on engineering and Technology, PICET 2024

作者： Megalingam, Rajesh Kannan Manoharan, Sakthiprasad Kuttankulangara Mohandas, Sreekanth Makkal Jayanthan, Kavya M. Puchakayala, Vijaya Krishna Tejaswi Labs Department of Electronics and Communication Engineering Amrita Vishwa Vidyapeetham Amritapuri India School Department of Electrical and Computer Engineering University of Florida Gainsville United States

ISBN: (纸本)9798350369748

The paper proposes an innovative approach to wheelchair design by integrating a steering wheel for control. This design aims to benefit patients, elderly individuals, and those with lower limb disabilities, enhancing mobility and fostering independence by eliminating the need for a caretaker. At the heart of the system lies a Raspberry Pi, efficiently managing DC motors based on input from the steering wheel. This wheelchair promises a user-friendly experience, empowering individuals to fulfill their basic needs without external assistance. However, a potential drawback lies in the possibility of misinterpretations arising from involuntary input signals. © 2024 IEEE.

关键词： Wheelchairs

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An IoT based Low Cost E-Parking System in Smart Cities 2

An IoT based Low Cost E-Parking System in Smart Cities

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2nd International Conference on electronics and Renewable Systems, ICEARS 2023

作者： Pradeepkumar, G. Monika, T. Gowrishankar, C. Bhat, C. Rohith Gowrishankar, V. Senthilkumar, M. Kpr Institute of Engineering and Technology Department of Electronics and Communication Engineering Coimbatore India Jkkn College of Engineering and Technology Department of Electrical and Electronics Engineering Kumarapalayam India Kongunadu College of Engineering and Technology Department of Electrical and Electronics Engineering Tholurpatti India Simats Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Velalar College of Engineering and Technology Department of Electronics and Communication Engineering Erode India Psg Institute of Technology and Applied Research Department of Electrical and Electronics Engineering Coimbatore India

ISBN: (纸本)9798350346640

The concerned workers ensure that vehicles are parked in the appropriate spaces. Employees must repeatedly poll their coworkers via personal surveys or the company's internal phone system to ensure that everything is correct. If there is an available parking space, the driver will manually move the vehicle there, regardless of any obstacles. If there is no available space, the car must return and try again later. The proposed intelligent parking system, if implemented, would solve all parking problems. This will take less time and fuel than other options. Intelligent parking solutions will fundamentally alter automobile-centric cities. It may make parking more convenient by bringing order to the chaos. People are always concerned that finding a parking spot will take too long, particularly in densely populated cities. This study focuses on developing a new system in which residents in high-traffic areas can earn extra money by renting out their unused parking spaces to those in need. This strategy could help malls to manage parking more effectively during peak shopping hours. A customer can save time by reserving a parking space ahead of time. © 2023 IEEE.

关键词： Costs

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Advanced Helical Antenna Design for X-Band Applications Using AI

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Progress in Electromagnetics Research C 2025年 153卷 201-211页

作者： Zeain, Mohammed Yousif Abu, Maisarah Toding, Apriana Zakaria, Zahriladha Alsariera, Hussein Ullah, Ihsan Abdulbari, Ali Abdulateef Yon, Hamizan Taha, Bilal Salman Inam Abbasi, Muhammad Hang Tuah Jaya Melaka Durian Tunggal76100 Malaysia Makassar Indonesia Department of Computer Science and Engineering Yuan Ze University Taoyuan Taiwan Information Technology Center University of Technology Baghdad Iraq Antenna Research Center College of Engineering School of Electrical Engineering Universiti Teknologi MARA Malaysia Department of Electrical and Electronics University Tenaga National Malaysia Malaysia

This paper presents the design, fabrication, and characterization of a novel 3D-printed helical antenna operating within the 9.4–10.8 GHz frequency band. The antenna, employing a lightweight paper substrate and a strip-based helical structure, exhibits robust circular polarization characteristics and wideband operation. Rigorous simulations predict a peak CP gain of 11.7 dBi at 9.8 GHz and a high simulated radiation efficiency of 95%. Experimental measurements validate these predictions, achieving a peak CP gain of 11.6 dBi at 9.8 GHz. This research demonstrates the potential of 3D-printed helical antennas for diverse applications in modern wireless communication systems, including 5G, satellite communication, and radar. Furthermore, this study leverages the power of Artificial Intelligence (AI) by employing the Grey Wolf Optimizer (GWO), a sophisticated metaheuristic algorithm, to optimize the antenna’s design. The GWO algorithm is utilized to efficiently search the design space and identify optimal values for key parameters, such as the number of turns, helix pitch, and helix diameter, with the objective of maximizing antenna gain to achieve a target of 15 dBi. This research highlights the potential of AI-driven optimization techniques in advancing the design of high-performance antennas for emerging wireless communication systems. © 2025, Electromagnetics Academy. All rights reserved.

关键词： Helical antennas

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Stock Recommendation System for Better Investment Plan

Stock Recommendation System for Better Investment Plan

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2024 International Conference on Signal Processing, Computation, electronics, Power and Telecommunication, IConSCEPT 2024

作者： Lokesh, D. Aravind Vasan, G. Vani, V. Karthik, N. National Institute of Technology Puducherry Department of Computer Science and Engineering Karaikal India National Institute of Technology Puducherry Department of Electrical and Electronics Engineering Karaikal India

ISBN: (数字)9798331540685

ISBN: (纸本)9798331540685

In the volatile landscape of financial markets, making well-informed decisions for intraday trading and long-term investment necessitates an integrated analysis of real-time stock data and the contextual information provided by news articles. This paper presents an innovative stock recommendation system that leverages advanced Machine Learning (ML) techniques and Natural Language Processing (NLP) to deliver timely, data-driven recommendations by assessing both risk and profit potential. Our system employs ML algorithms to process live stock price, trading volume, and technical indicators, enabling real-time assessment of a stock's performance. Developing a stock recommendation system is a challenging undertaking that frequently calls for a multidisciplinary team with experience in data science, machine learning, and finance. Simultaneously, it harnesses NLP models to extract sentiment and contextual information from news articles, providing insights into market sentiment and potential impact on stock behavior. By merging these two streams of data, our system offers a comprehensive view of stock's dynamics. © 2024 IEEE.

关键词： Recommender systems

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Machine learning applied to functionalized graphene sensors for noninvasive detection of renal diseases

Machine learning applied to functionalized graphene sensors ...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Sarkar, Lisa Bhattacharyya, Arindam Sett, Avik Karmakar, Gairik Bhattacharyya, Tarun Kanti Iit Kharagpur Department. of Electronics and Electrical Communication Engineering Kharagpur India Uc Davis Department of Computer and Electrical Engineering United States Tu Delft Department. of Microelectronics Delft Netherlands

ISBN: (纸本)9798350386226

Breath biomarker detection has been a significant non-invasive approach for disease diagnosis. This method has significant potential for early diagnosis and accurate analysis of diseases. Emission from breath contains several volatile organic compounds. Among them, ammonia is a very commonly found VOC and mainly responsible for chronic kidney diseases. There exist several strategies to detect ammonia, however they demonstrate severe limitations such as cross-sensitivity and poor selectivity. This work demonstrates the synergistic effect of sensor functionalization and application of machine learning for selective detection of ammonia in the environment. The sensor exhibits high degree of selectivity towards ammonia owing to enormous hydroxyl groups contributed through curcumin. At 500 ppm ammonia, the sensor demonstrates 274% response and very high selectivity among seven volatile organic compounds. The machine learning models were trained with the help of sensor transients. Random Forest and CNN models were applied to predict the presence of ammonia in a mixture. Random Forest achieved 96.25% accuracy compared to 89% accuracy of CNN. Hence, Random Forest algorithms applied to curcumin functionalized reduced graphene oxide sensors can detect ammonia vapors with very high efficiency among a mixture of gases. © 2024 IEEE.

关键词： Ammonia

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computer Vision based Panoptic Driving Perception under Various Weather Conditions

Computer Vision based Panoptic Driving Perception under Vari...

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2023 International Conference on Industry Sciences and computer Science Innovation, iSCSi 2023

作者： Sridevi, M. Harish, M. Department of Computer Science & Engineering National Institute of Technology Tiruchirappalli620015 India Department of Electrical and Electronics Engineering National Institute of Technology Tiruchirappalli620015 India

Learning models that have been known to multi-task are gaining too much attention in various fields. One such area of work where Multi-tasking models are extracting hopeful outcomes is Autonomous Driving. Panoptic Driving Perception has become an inevitable aspect of autonomous driving systems which demands a very good accuracy and speed. Over a decade, various multi-tasking models have been proposed to simultaneously carry out traffic object detection, drivable area segmentation, and lane detection. Among all of them, YOLOP, YOLOPv, and HybridNets are the 3 models that have accomplished the above tasks using the BDD100K Dataset with high excellent accuracy and efficiency, thus achieving the new State-Of-The-Art (SOTA) performance. This paper focuses mainly on comparing the performance of the above-mentioned models in various weather conditions based on accuracy, robustness, and computational speed. © 2024 The Authors. Published by ELSEVIER B.V.

关键词： Object detection

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