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检索条件"机构=dept. of Computer Computer Systems Engineering"

共 66560 条记录，以下是461-470 订阅

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Leveraging Name Entity Recognition and Text Classification for Talent Mapping 2

Leveraging Name Entity Recognition and Text Classification f...

引用

2nd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Charan, Annepu Sai Moirangthem, Dennis Singh Iiit Manipur Dept. of Computer Science and Engineering Imphal India

ISBN: (纸本)9798350360523

The paper presents a dual-model approach that can be used for categorizing the work experience data from resumes or other textual copra. The application tracking system (ATS), which is the main tool used to screen resumes, presents operational issues in the quickly changing software sector. Although this technique matches resumes with job descriptions effectively, it requires separate scans for every job posting, which could result in errors and inefficiencies. This initiative is motivated by the need to expedite the hiring process for tech companies and potential employees. The suggested approach presents a cutting-edge tool to improve and expedite job matching and resume analysis. The work attempts to improve the software industry's hiring process efficiency by overcoming present system restrictions. This study presents a novel solution - a text classification model intended for resume analysis and shortlisting to address the operational difficulties with the modern resume screening process. Even though the Application Tracking System (ATS) is currently used, it requires separate scans for every job posting, which could lead to inefficiencies. We propose dual-model architecture which includes our custom Name Entity Recognition (NER) Model which is trained on annotated work experience data and a Multi-Layer Perceptron (MLP) model for multi label classification, aiming to streamline the resume screening process. This dual-model approach enhances accuracy and efficiency in resume classification, benefiting both aspirants and tech companies. This research is a pivotal step in optimizing recruitment in the dynamic software industry. The complete work can be found on GitHub1 © 2024 IEEE.

关键词： Mapping

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Lung Cancer Disease Prediction using Hybrid Subtractive Clustering Method 2

Lung Cancer Disease Prediction using Hybrid Subtractive Clus...

引用

2nd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Paul, Banibrata Mir, Mahmood Hussain Murugan, Stalin Kurian, Asha Alliance University Dept. of Computer Science & Engineering Bangalore India

ISBN: (纸本)9798350360523

As of right now, lung cancer is the primary cause of cancer-related deaths worldwide for both men and women. One possible explanation for lung cancer's main cause is smoking. 86% to 96% of instances of lung cancer are thought to originate in the epithelial cells that line the larger and smaller airways (bronchi and bronchioles), despite the fact that lung cancer can grow in any section of the lung. The primary objective of this work is to identify lung cancer using the ANFIS approach based on subtractive clustering method. Using Kaggle, we have made use of the Lung Cancer dataset. The network is trained using data from 1000 lung cancer patients, ranging in age from 14 to 73. The proposed system uses 11 input attributes to assess the presence and absence of lung cancer during testing, and consistently achieves 100% accuracy for each clustering radii. © 2024 IEEE.

关键词： Diseases

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Sentiment Analysis of Customer Reviews on Food Ordering Portals of Bangladesh using Natural Language Processing 6

Sentiment Analysis of Customer Reviews on Food Ordering Port...

引用

6th International Conference on Cybernetics, Cognition and Machine Learning Applications, ICCCMLA 2024

作者： Deb, Priom Bhuiyan, Asibur Rahman Ahmed, Farhan Hossain, Md. Rakib Mahrin, Habiba Ahmed, Md Faisal Karim, Dewan Ziaul Brac University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798331505790

In recent years, the popularity of online food ordering services has surged, offering consumers a convenient way to order food from restaurants and have it delivered to their doorstep. During this period, HungryNaki and Foodpanda Bangladesh have been identified as key contributors to the growth and advancement of the online food delivery market. This study aims to anticipate the sentiments of Bangladeshi customers towards online meal ordering services, with a specific focus on Foodpanda Bangladesh and HungryNaki. We created a new dataset for this research, subjected it to preprocessing and employed six machine learning models and three deep neural network models. In the machine learning approach, the Random Forest Classifier demonstrated excellence in accuracy, precision, and recall, achieving an accuracy rate of 80.31%. On the other hand, the BERT Classifier performed effectively in the deep learning approach, reaching a peak accuracy of 89%. Despite challenges such as data ambiguity and imbalances in the dataset, our findings underscore the potential of the BERT model in sentiment analysis, offering valuable insights for future research in this field. © 2024 IEEE.

关键词： Data accuracy

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Enhancing Efficiency in the IOTA Tangle: A POMDP-Based Tip Selection Approach

Enhancing Efficiency in the IOTA Tangle: A POMDP-Based Tip S...

引用

2024 International Conference on computer and Applications, ICCA 2024

作者： Alshaikhli, Mays Al-Maadeed, Somaya Saleh, Moutaz Qatar university Dept. Computer Science and Engineering Doha Qatar

ISBN: (纸本)9798350367560

The IOTA Tangle represents a promising alternative to blockchain technology, particularly for the Internet of Things (IoT) environment. However, ensuring efficiency and fairness in transaction processing remains a significant challenge. This paper focuses on the efficiency of the Tangle by evaluating a novel POMDP-based Tip Selection Algorithm (TSA). Through comparative analysis against Weighted TSA, G-IOTA, and E-IOTA under varying network loads (lambda values), the POMDP algorithm demonstrates superior performance in maintaining the fastest confirmation process and more efficiency in different network conditions. These results establish POMDP as a scalable and efficient solution, contributing to the advancement of decentralized ledger technology. Future directions include real-world testing and the integration of adaptive network factors for enhanced scalability. © 2024 IEEE.

关键词： Blockchain

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Evolutionary Computation-Based Scheduling of Machine Learning Workloads for GPU Clusters 5

Evolutionary Computation-Based Scheduling of Machine Learnin...

引用

5th International Conference on Advances in Electrical engineering and computer Applications, AEECA 2024

作者： Kwon, Seokmin Bahn, Hyokyung Ewha University Dept. of Computer Engineering Seoul Korea Republic of

ISBN: (纸本)9798350355253

Recently, machine learning (ML) workloads across diverse industries such as smart logistics, finance, and entertainment are increasingly being executed on cloud platforms. Efficient scheduling of these ML workloads is challenging as various types of workloads coexist and the cluster systems feature heterogeneous GPU resources. Although task scheduling has been extensively studied, traditional scheduling policies do not perform well in such environments as they cause resource fragmentation problems, which significantly lowers GPU utilization. To address this issue, this paper proposes a new scheduling approach utilizing evolutionary computation techniques, and implements it within a process-based event simulation framework. Experimental results, replicating extensive ML task traces collected from Alibaba's MLaaS cluster, demonstrate that the proposed scheduling approach significantly improves GPU utilization compared to conventional scheduling policies. It is anticipated that the scheduling policies proposed in this paper will be used effectively for the resource allocation of ML workloads in future GPU cluster systems. © 2024 IEEE.

关键词： Cloud platforms

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Enhancing Bikers' Safety and Coordination: A LoRa-Enabled Wearable System for Smart Cycling 4

Enhancing Bikers' Safety and Coordination: A LoRa-Enabled We...

引用

4th International Conference on Soft Computing for Security Applications, ICSCSA 2024

作者： Krishna Prasad, D.S. Sandeep Kumar, S. Deeksha, G.D. Rao, Manasa Megha, S. Shashwathi, N. Dept. of Computer & Communication Engineering Nitte Karnataka574110 India

ISBN: (纸本)9798331515720

In recent years, the popularity of biking, especially mountain biking, has surged, drawing enthusiasts seeking adventure and adrenaline amidst rugged landscapes. However, with this surge in popularity comes an increased awareness of the safety challenges inherent in the sport, particularly in remote or challenging terrains where traditional communication methods often fail to provide adequate support. Recognizing these challenges, the paper presents a LoRa-enabled wearable system tailored to enhance safety and coordination for mountain bikers, focusing on challenging terrain scenarios. By incorporating GPS functionality, the system delivers crucial information such as status updates and emergency notifications, thereby markedly elevating safety standards for riders. Furthermore, the project enables real-Time communication in network-deficient areas, effectively surmounting communication hurdles posed by mountainous landscapes. Leveraging LoRa technology extends the communication range, guaranteeing uninterrupted connectivity and empowering bikers to traverse rugged terrains with heightened assurance and security. © 2024 IEEE.

关键词： Global positioning system

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The Development of a Digital Forensic Framework for Ease of Forensic Analysis 12

The Development of a Digital Forensic Framework for Ease of ...

引用

12th International Symposium on Digital Forensics and Security, ISDFS 2024

作者： Suvarna, Dhruv Km, Mahesh Gupta, Mahika Gabburi, Sonal Honnavalli, Prasad Vm, Sapna Pes University Dept. of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350330366

With the growing use of digital devices and technologies, the volume of digital forensic evidence in need of analysis also increases. This results in a need for more effective methods to conduct analysis of this evidence in an investigation. This paper discusses the development of a framework for digital forensic processes. The framework will utilize various tools and techniques to enhance the process of collecting, analyzing, and preserving digital evidence. A survey of existing digital forensic tools, including FTK Imager, Autopsy, and The Sleuth Kit was conducted to help identify various pain points faced by investigators throughout the forensic lifecycle. Based on this study, a framework for the analysis of storage data and memory dumps in digital forensics is proposed, incorporating industry best practices and tools such as The Sleuth Kit and Volatility, enhancing ease of use, and thereby promoting more accurate resolutions. © 2024 IEEE.

关键词： Digital forensics

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DevOps Practices Into Machine Learning 3

DevOps Practices Into Machine Learning

引用

3rd IEEE International Conference on Intelligent systems, Smart and Green Technologies, ICISSGT 2024

作者： Jain, Samridhi Kumar, Puneet Chandigarh University Dept. of Computer Science & Engineering Mohali India

ISBN: (纸本)9798350304329

All industrial machine learning (ML) projects have their ultimate objective to quickly develop and deploy ML solutions. However, a lot of machine learning projects are failing, and never reach production. In order to address these concerns, this paper aims to investigate how manual ML processes can be automated and operationalised so that more ML concept can be put into production. The advantages of DevOps automation and machine learning capabilities are now being combined by industries. MLOps is the name of this combination. Our analysis found that while there are some similar aspects in defining MLOps, still there is a lack of standardisation in the term, which can occasionally lead to misunderstandings and uncertainty. We carried out the research, which includes a review of the literature and an evaluation of the MLOps tools, to fill this gap. These investigations have led us to define MLOps and identify unresolved issues in the field. In addition to this, we offer a workflow-based summary of all essential components. Finally, this paper gives guidance for applying DevOps practises to machine learning for researchers and practitioners. © 2024 IEEE.

关键词： DevOps Machine Learning MLOps MLOps tools

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Power Consumption Assessment of Telecommunication Base Stations Harnessing Prototype Energy Models 15

Power Consumption Assessment of Telecommunication Base Stati...

引用

15th International Conference on Information, Intelligence, systems and Applications, IISA 2024

作者： Fevgas, Athanasios Korinthios, John A. Tsompanopoulou, Panagiota Frangogiannis, Nikolaos Tsoumanis, Pelopidas Paliaroutis, Georgios-Ioannis Takou, Alexandra Stamoulis, George University of Thessaly Dept. of Electrical and Computer Engineering Volos Greece

ISBN: (纸本)9798350368833

Energy consumed in telecommunication base stations is a significant part of the cellular network energy footprint. Efficient energy use, renewable energy sources, and infrastructure improvement can generate substantial energy savings. We aim to provide a roadmap to fulfill this objective, and energy modeling plays a crucial role in this direction. We introduce five base station energy models for the state-of-the-art EnergyPlus simulator, and we present the development of an OpenStudio Measure for the parameterization of the proposed models. The prototype models and the Measure provide network operators with a comprehensive tool suite for base station energy simulations. This makes our work apart from previous efforts. We conduct a case study demonstrating different usage scenarios of the proposed models utilizing the OpenStudio Application and the OpenStudio Analysis Framework (OSAF). The simulations indicate that construction materials and methods influence the energy efficiency of base stations, while ventilation and photo-voltaics can reduce consumption. Another merit of simulations is that they reveal the most important parameters for the energy performance of base stations. © 2024 IEEE.

关键词： Base stations

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Analytical Survey of Domain Effective Sentiment Lexicon for Aspect-Centric Opinion Analysis 8

Analytical Survey of Domain Effective Sentiment Lexicon for ...

引用

8th IEEE International Conference on Computing, Communication, Control and Automation, ICCUBEA 2024

作者： Purbey, Lal Babu Lakhwani, Kamlesh Jecrc University Dept. Computer Science of Engineering Jaipur India

ISBN: (纸本)9798350391770

Aspect-level sentiment analysis is a modern technology that enables people to share their views or emotions with products, services, or topics based on their aspects. Aspect-level sentiment analysis focuses on aspect extraction, which considers aspects from user reviews. Various methods and approaches have been employed for aspect extraction. Although these methods show better performance and attempt to overcome various challenges, many unsolved problems still need to be solved, such as sentimental ambiguity, opinion misprediction, a weak sentiment lexicon, and implicit sentiment detection. Numerous sentiment lexicons have been employed for word polarity extraction. Dictionaries also play an important role in sentiment classification. There are various dictionaries, but it has been found that issues such as one dictionary might not work properly for all domains. This study analyzes various existing sentiment lexicons employed for domain-specific sentiment analysis. Various approaches have been employed to create a domain-dependent sentiment lexicon and its effect on aspect-level sentiment analyses. © 2024 IEEE.

关键词： Aspect Extraction Aspect level sentiment analysis Domain dependent dictionaries Sentiment Analysis Sentiment Lexicon

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