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3rd International Conference on Innovations in computer science and software engineering, ICONICS 2022

作者： Peter Noronha, Joseph Terence Mahmood Khan, Shariq Sebastian, Stina Nabeel Mustafa, Syed Muhammad NED University of Engineering and Technology Dept. of Computer Science and I.T Karachi Pakistan Dept. of Business Management Riyadh Saudi Arabia

ISBN: (纸本)9781665461412

Over the years, numerous businesses transitioned to creating online access, which helped them towards gaining access to a global presence. With the rise of ecommerce businesses accessed through websites or social media platforms, many seemed to have failed to generate sufficient revenue for themselves. Machine learning, artificial intelligence, and data science have aided retailers in managing their supplies, creating a brand for themselves with the review system, and providing the customer with recommendations. Moreover, with the help of these recommendations, customers have made the right choices when purchasing a product. Thus, this is what all established retailers and their customers demonstrate. Due to a massive increase in e-commerce businesses, it is significant to have details of all the ongoing strategies in the current business to ensure a reasonable growth rate. This study will interpret the different trends, platforms, and strategies involved in an ecommerce business. © 2022 IEEE.

关键词： Electronic commerce

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Evaluating Large Language Models for Automatic Register Transfer Logic Generation via High-Level Synthesis

arXiv

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arXiv 2024年

作者： Swaroopa, Sneha Mukherjee, Rijoy Debnath, Anushka Chakraborty, Rajat Subhra The Dept. of Computer Science and Engineering Indian Institute of Technology West Bengal Kharagpur721302 India The Dept. of Computer Science and Engineering National Institute of Technology West Bengal Durgapur713209 India

The ever-growing popularity of large language models (LLMs) has resulted in their increasing adoption for hardware design and verification. Prior research has attempted to assess the capability of LLMs to automate digital hardware design by producing superior-quality Register Transfer Logic (RTL) descriptions, particularly in Verilog. However, these tests have revealed that Verilog code production using LLMs at current state-of-the-art lack sufficient functional correctness to be practically viable, compared to automatic generation of programs in general-purpose programming languages such as C, C++, Python, etc. With this as the key insight, in this paper we assess the performance of a two-stage software pipeline for automated Verilog RTL generation: LLM based automatic generation of annotated C++ code suitable for high-level synthesis (HLS), followed by HLS to generate Verilog RTL. We have benchmarked the performance of our proposed scheme using the open-source VerilogEval dataset, for four different industry-scale LLMs, and the Vitis HLS tool. Our experimental results demonstrate that our two-step technique substantially outperforms previous proposed techniques of direct Verilog RTL generation by LLMs in terms of average functional correctness rates, reaching score of 0.86 in pass@1 metric. Copyright © 2024, The Authors. All rights reserved.

关键词： C++ (programming language)

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Analysis and Ranking Resume using Machine Learning Algorithms and NLP

Analysis and Ranking Resume using Machine Learning Algorithm...

引用

Advances in Computing, Communication and Networking (ICAC2N), International Conference on

作者： Pratik Chandrasen Deshmukh M. R. Bendre Dept. of Data Science Engineering Pravara Rural Engineering College Loni India Dept. of Computer Engineering Pravara Rural Engineering College Loni India

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

Specialized recruiting firms have emerged as a result of the expansion of the Indian employment market. These agencies use machine learning models to expedite the hiring process and provide their clients with the best candidates. Thus, the suggested methodology offers the potential to improve the hiring process by incorporating the advantages of machine learning in addition to natural language processing(NLP). Employing NLP techniques, one may accurately grade resumes according to the company's criteria and extract useful information from them. After thorough testing and improvement, the web portal might prove to be an invaluable tool for hiring managers and job seekers ***, one might think about adding features to the CV classifier and ranker, such as customized feedback and recommendations for enhancements that might assist candidates in understanding where they might be lacking and increase their chances of getting recruited.

关键词： Machine learning algorithms Resumes Employment Natural languages Machine learning software Portals Recruitment Testing Qualifications

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AI-Driven Prognostics for State of Health Prediction in Li-ion Batteries: A Comprehensive Analysis with Validation

arXiv

引用

arXiv 2025年

作者： Ding, Tianqi Xiang, Dawei Sun, Tianyao Qi, YiJiashun Zhao, Zunduo Dept. of Electrical and Computer Engineering Baylor University WacoTX United States Dept. of Computer Science and Engineering University of Connecticut StorrsCT United States New York NY United States Dept. of Electrical and Computer Engineering University of Michigan Ann ArborMI United States Dept. of Computer Science New York University New YorkNY United States

This paper presents a comprehensive review of AI-driven prognostics for State of Health (SoH) prediction in lithium-ion batteries. We compare the effectiveness of various AI algorithms, including FFNN, LSTM, and BiLSTM, across multiple datasets (CALCE, NASA, UDDS) and scenarios (e.g., varying temperatures and driving conditions). Additionally, we analyze the factors influencing SoH fluctuations, such as temperature and charge-discharge rates, and validate our findings through simulations. The results demonstrate that BiLSTM achieves the highest accuracy, with an average RMSE reduction of 15% compared to LSTM, highlighting its robustness in real-world applications. Copyright © 2025, The Authors. All rights reserved.

关键词： Lithium-ion batteries

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IoT Based Smart Automated Agriculture and Real Time Monitoring System 2

IoT Based Smart Automated Agriculture and Real Time Monitori...

引用

2nd International Conference on Smart Electronics and Communication, ICOSEC 2021

作者： Shamrat, F. M. Javed Mehedi Hossain, Alamin Roy, Tonmoy Adeeb Khan, Md Ahasanul Khater, Ankit Rahman, Md Tareq Daffodil International University Dept. of Software Engineering Dhaka Bangladesh Daffodil International University Dept. of Computer Science and Engineering Dhaka Bangladesh Dhaka Bangladesh Dalhousie University MEng In Internetworking Halifax Canada Jadavpur University Dept. of Computer Science and Engineering India

ISBN: (纸本)9781665433686

In Bangladesh, agriculture is the primary source of income. It has a significant impact on the country's economy. However, agriculture is being hampered these days due to citizens shifting from rural to urban areas. Monitoring environmental factors is not a natural remedy for increasing crop production. Several causes have a significant impact on efficiency. Consequently, to address these issues, agriculture must incorporate automation. A farmer can save time, cost, resources, and energy by using an automated irrigation device. Traditional agricultural irrigation methods necessitate human interference. Human interference can be reduced with automated irrigation technology. This research work has designed and developed a reliable smart farming system (IoT) to reduce farmers' time costs and resources. Our proposed system can detect temperature, detect the moisture level and water level of the agricultural land, and remotely monitor the land crops. The proposed model sort out into four modules: Water Le vel Detection Module (WLDM) always detect the water level to avoid drops destruction;Soil Moisture Detection Module (SMDM) calculate the soil moisture level from the land, if the level goes down its start the water pump;Temperature Detection Module (TDM) is always counting the temperature and humidity of the air if its high then it will start the fan;Cloud and Notification Module (CNM) is handled the user notification through message and remotely monitoring the data of first three modules to take the necessary steps. The result shows the system successfully performed and it can be noted that our proposed can be implemented with any type of environment and agricultural land. © 2021 IEEE.

关键词： Soil moisture

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Differential Diagnosis of Lung Disease through Quantitative Lung Ultrasound Spectroscopy, an in vivo clinical study over 114 patients

Differential Diagnosis of Lung Disease through Quantitative ...

引用

2023 IEEE International Ultrasonics Symposium, IUS 2023

作者： Mento, Federico Perpenti, Mattia Barcellona, Giuliana Perrone, Tiziano Demi, Libertario Dept. of Information Engineering and Computer Science Ultrasound Laboratory Trento University of Trento Trento Italy Emergency and Urgency Department Humanitas Gavazzeni Bergamo Bergamo Italy

ISBN: (纸本)9798350346459

Lung ultrasound (LUS) represents an important imaging tool to evaluate the state of the lung surface. However, it is mainly based on the visual interpretation of imaging artifacts, especially the vertical ones. This subjective interpretation leads to poorly reproducible approaches, and, thus, qualitative diagnoses. Therefore, in this study, we present a quantitative multifrequency analysis based on the parameterization of vertical artifacts. Specifically, radiofrequency (RF) data were acquired from 114 patients affected by different diseases using a linear and a convex probe, connected to the ULtrasound Advanced Open Platform (ULA-OP). The vertical artifacts observed in patients affected by pneumonia, cardiogenic pulmonary edema (CPE), and pulmonary fibrosis (PF) were extracted and analyzed. Specifically, the significance of three features (native frequency, bandwidth, and total intensity) in discriminating these diseases was evaluated. The results show how the total intensity seems significant in differentiating PF from the other two diseases (p-value © 2023 IEEE.

关键词： Ultrasonics

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Mitigating Attacks on Artificial Intelligence-based Spectrum Sensing for Cellular Network Signals

Mitigating Attacks on Artificial Intelligence-based Spectrum...

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2022 IEEE GLOBECOM Workshops, GC Wkshps 2022

作者： Catak, Ferhat Ozgur Kuzlu, Murat Sarp, Salih Catak, Evren Cali, Umit University of Stavanger Dept. of Electrical Eng. & Computer Science Rogaland Norway Old Dominion University Dept. of Engineering Technology NorfolkVA United States Virginia Commonwealth University Dept. of Electrical and Computer Engineering RichmondVA United States Independent Researcher Stavanger Norway Norwegian University of Science and Technology Dept. of Electric Power Engineering Trondheim Norway

ISBN: (纸本)9781665459754

Cellular networks (LTE, 5G, and beyond) are dramatically growing with high demand from consumers and more promising than the other wireless networks with advanced telecommunication technologies. The main goal of these networks is to connect billions of devices, systems, and users with high-speed data transmission, high cell capacity, and low latency, as well as to support a wide range of new applications, such as virtual reality, metaverse, telehealth, online education, autonomous and flying vehicles, advanced manufacturing, and many more. To achieve these goals, spectrum sensing has been paid more attention, along with new approaches using artificial intelligence (AI) methods for spectrum management in cellular networks. This paper provides a vulnerability analysis of spectrum sensing approaches using AI-based semantic segmentation models for identifying cellular network signals under adversarial attacks with and without defensive distillation methods. The results showed that mitigation methods can significantly reduce the vulnerabilities of AI-based spectrum sensing models against adversarial attacks. © 2022 IEEE.

关键词： Wireless networks

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Home Care Assistance Solution based on a software Multi-Agents System 24

Home Care Assistance Solution based on a Software Multi-Agen...

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24th International Conference on Control Systems and computer science, CSCS 2023

作者： Rasras, Mohammad Marin, Iuliana Radu, Serban University Politehnica of Bucharest Doctoral School of Automatic Control and Computers Bucharest Romania University Politehnica of Bucharest Dept. of Engineering in Foreign Languages Bucharest Romania University Politehnica of Bucharest Dept. of Computer Science Bucharest Romania

ISBN: (纸本)9798350313390

A smart home allows automated technology to control systems and appliances to assist daily life activities. In the current work, a prototype of a designed smart home health-aware system is presented. Three predefined ruled agents have been used to gather data, communicate between themselves, interpret the data based on several machine learning algorithms to predict abnormality for cardiac conditions. The paper also compares results obtained by running five machine learning algorithms. The agents function in accordance with predefined dataset describing residents ' age, gender, height, weight, systolic and diastolic blood pressure, cholesterol, and glucose levels, smoking and alcohol consumption habits, and lastly, physical activity. Our plan is to simplify ubiquitous health monitoring for smart home's residents. The proposed solution aims to monitor parametric measurements to alert health specialists in case of emergencies. © 2023 IEEE.

关键词： Multi agent systems

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A Web-based Application for Snake Species Identification using Vision Transformer and CNN-based Ensemble Meta Classifier

A Web-based Application for Snake Species Identification usi...

引用

2022 IEEE Pune Section International Conference, PuneCon 2022

作者： Dandeniya, D.D.K.R.W. Wickramasinghe, B.C.T. Dasanayaka, C. University of Peradeniya Dept. of Electrical and Electronic Engineering Peradeniya Sri Lanka University of Peradeniya Dept. of Statistics and Computer Science Peradeniya Sri Lanka

ISBN: (纸本)9781665498975

Being a tropical country, Sri Lanka has one of the highest snakebite rates in the world. In 2019, 50 snake bite fatalities have been reported in Sri Lanka. Therefore, the accurate identification of the snake category is crucial for healthcare workers to diagnose and treat the victims as well as to save the snake from being killed. In this paper, we present a web-based application based on Convolutional Neural Network and Vision Transformer architectures to classify between the Russell's viper and the Indian Rock Python. Five different image classification models were trained using the pre-trained architectures ResNet-50, ResNet-100, EfficientNet B0, EfficientNet B7 and Data-Efficient Image Transformers. We were able to gain a testing accuracy of 94.5% by using an ensemble approach for the mentioned classifiers. Furthermore, this study presents the first web-based application in Sri Lanka enabling the automatic identification between Russell's Viper and the Indian Rock Python. © 2022 IEEE.

关键词： Python

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The Development of HP Based ML Model for Reliable Computational Intelligence System 4

The Development of HP Based ML Model for Reliable Computatio...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Vahidhabanu, Y. Ramkumar, K. Bhatt, Chandradeep Srinivasan, M.L. Sasirekha, S.P. Saravanakumar, S. Kongunadu College of Engineering and Technology Department of Computer Science and Engineering Trichy India Rajalakshmi Institute of Technology Department of Computer Science and Engineering Tamilnadu Chennai India Graphic Era Hill University Graphic Era Deemed to Be University Department of Cse Uttarakhand Dehradun248007 India Karpagam Academy of Higher Education Department of Cse Eachanari Coimbatore India Dept. of Cse Faculty of Engineering & Technology Bengaluru India

ISBN: (纸本)9798350360165

many educational institutions employ data mining to keep track of their students' records, particularly their academic achievements, which is more significant. To improve their achievements, students' academic performances, as well as the institutions' overall results, must be evaluated. Furthermore, the prediction of students' academic accomplishment educational data mining has been a growing research area machine learning techniques are used to gather information from educational warehouses. As a result, this research proposes the hybrids pedagogical statistical model, a novel approach for analyzing student performance, to improve educational quality for students. The suggested methodology assesses student performance using diverse parameters that result in suitable outcomes. In addition, for obtaining the findings and accurately categorising student performance, the framework illustrates the synergy between the j 48 separator and the enn-based segmentation method. The model is put to the test using the weka environment's benchmark schooling dataset., which is available online. The findings suggest that the proposed model outperforms previous studies in assessing edm student performance. © 2024 IEEE.

关键词： Students

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