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共 2513 条记录，以下是81-90 订阅

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Analysis of Factors influencing Early Detection of Breast Cancer 3

Analysis of Factors influencing Early Detection of Breast Ca...

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3rd international conference on Emerging trends in Electrical, Control, and Telecommunication Engineering, ETECTE 2024

作者： Malik, Hassal Sonia Sial, Umaira Ahmad Bukhari, Faisal Department of Data Science Faculty of Computing and Information Technology University of the Punjab Lahore Pakistan

ISBN: (纸本)9798331504861

Carcinoma, particularly breast cancer, poses a formidable challenge, especially in Pakistan where it stands as the most prevalent cancer among women. This research investigates the complex landscape of breast cancer in Pakistan, utilizing a dataset from local hospitals. Through comprehensive statistical analyses and advanced machine learning techniques, including XGBoost and RandomForest, the study uncovers significant associations between socio-economic and psychological factors and the timing of breast cancer diagnosis among Pakistani women. Factors such as embarrassment, fear of treatment, and lack of awareness emerge as crucial determinants. The findings provide valuable insights for tailored interventions and contribute to the global fight against breast cancer. ©2024 ieee.

关键词： Lung cancer

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Against the current: Introducing Reversibility to Superscalar Processors via Reversible Branch Predictors 15

Against the Current: Introducing Reversibility to Superscala...

引用

15th international Green and Sustainable computing conference

作者： Gregg, Byron Teuscher, Christof Portland State Univ Dept Elect & Comp Engn Portland OR 97207 USA

ISBN: (纸本)9798331507879;9798331507862

Although highly energy efficient, adiabatic and reversible systems suffer from performance drawbacks inherent to the physical operations that make them so efficient. Superscalar processors provide high performance through out-of-order speculative work of which an effective branch predictor is a key component in those performance gains. In the context of reversibility, a branch predictor is a design focal point because any fully reversible system must also be able to predict branch outcomes when in reverse mode. Taking advantage of Temporal Streaming techniques, this paper introduces several reversible branch predictor implementations which enable reversible and out-of-order instruction execution. These first-of-their-kind designs allow for a superscalar architecture that would maintain both a high level of performance and a high level of energy efficiency with the ability to un-compute obsolete data stored in memory. Testing our designs using the SimpleScalar out-of-order simulator, we estimate possible additional savings of 24 fJ per MB of data recovered at room temperature and at reverse prediction rates 2.27% higher than the forward. This work opens new avenues for designing and developing what we are calling Fully Adiabatic, Reversible, and Superscalar (FARS) Processor Architectures and is the first of many adaptations of conventional superscalar components to a reversible system.

关键词： adiabatic computing branch predictor efficient computing fars fully adiabatic and reversible fully adiabatic reversible and superscalar hyper-efficient computing reversible and superscalar reversible branch predictor reversible computing superscalar

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Interpretable Cervical Cell Classification: A Comparative Analysis 4

Interpretable Cervical Cell Classification: A Comparative An...

引用

4th international conference on advanced Research in computing, ICARC 2024

作者： Gnanavel, Nishaanthini Inparaj, Prathushan Sritharan, Niruthikka Meedeniya, Dulani Yogarajah, Pratheepan University of Moratuwa Department of Computer Science and Engineering Moratuwa Sri Lanka School of Computing Engineering and Intelligent Systems Ulster University Londonderry United Kingdom

ISBN: (纸本)9798350384864

Cervical cancer is a significant global health issue, and traditional screening methods like Pap smears are labor-intensive and may miss some cases. Automation is needed, but it faces challenges in terms of interpretability and data availability. To address this, the paper proposes using Explainable Artificial Intelligence (XAI) techniques like GradCAM, GradCAM++, and LRP to improve the transparency and interpretability of a cervical cell classification model, making it a novel contribution to enhancing the trustworthiness of automated cervical cancer detection. Using the Herlev Dataset, we employ data pre-processing, data augmentation techniques and develop a binary classification model, achieving a 91.94% accuracy with VGG16. The qualitative analysis of XAI methods confirmed that the model relied on nucleus and cytoplasm features, key indicators of malignancy. The least mean image entropy of 2.4849 and steep prediction confidence drop with perturbations quantitatively proved Layer-wise Relevance Propagation (LRP) to be the most effective XAI technique for cervical cell classification. © 2024 ieee.

关键词： Artificial intelligence

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Machine Learning-Driven Sri Lankan Stock Market Prediction: Harnessing Economic Indicators and Sentiment Analysis 4

Machine Learning-Driven Sri Lankan Stock Market Prediction: ...

引用

4th international conference on advanced Research in computing, ICARC 2024

作者： Dinushan, S. Wijegunasekara, M. Carmel University of Kelaniya Faculty of Computing and Technology Department of Software Engineering Kelaniya Sri Lanka

ISBN: (纸本)9798350384864

The stock market plays a pivotal role in a nation's economy. People tend to invest in the stock market due to its high rate of return. However, predicting the stock market remains a formidable challenge. This research uses machine learning algorithms to predict the ASPI and S&P 20 monthly stock market indexes by considering global economic conditions, macroeconomic variables, and sentiment data. Correlation tests, forward stepwise regression, and p-value tests were carried out to identify the influential variables. Machine learning algorithms including deep learning were utilized for model building, using the influential variables dataset. Shuffling and non-shuffling methods were deployed during dataset splitting. Additionally, included the previous month's stock value to the influential variables dataset and re-conducted the model building to assess the performance. The best-performing model (with the lowest MAE rate) was selected separately for ASPI and S&P 20 prediction. © 2024 ieee.

关键词： Forecasting

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Adaptive Edge computing for Autonomous Vehicles in Varying Environment 2

Adaptive Edge Computing for Autonomous Vehicles in Varying E...

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2nd international conference on Emerging trends in Information Technology and Engineering, ic-ETITE 2024

作者： Shreyas, K. Manjula, V. School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350328202

This research offers an in-depth analysis of the integration of Autonomous Vehicle Platooning, Vehicle-to-Everything (V2X) communication, Edge Analytics, and Real-time Traffic Management. Central to the study is 'Platooning,' where autonomous vehicles optimize road space, fuel efficiency, and safety. V2X communication, a silent yet critical interaction between vehicles and their surroundings, lays the foundation for advanced safety and navigation in autonomous vehicles. When combined with V2X, platooning generates vast data, making Edge Analytics essential for filtering realtime intelligence for swift decisions. The study emphasizes the importance of Real-time Traffic Management in managing urban congestion, with Edge Analytics providing dynamic route planning. By focusing on algorithms and practical applications, the research aims to merge these technologies for efficient, safe, and responsive Autonomous vehicles (AV) operations, offering insights for urban planning and traffic management, and setting the stage for a sustainable transportation future. © 2024 ieee.

关键词： Autonomous vehicles

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Panel: Sustainability in computing 21

Panel: Sustainability in Computing

引用

21st ieee international conference on Pervasive computing and Communications (PerCom)

作者： Singh, Gurdip Abowd, Gregory Chien, Andrew Jones, Alex Islam, Bashima Dahiya, Ravinder Hester, Josiah NSF CISE Directorate Comp & Network Syst Alexandria VA 22314 USA George Mason Univ Sch Comp Fairfax VA 22030 USA Northeastern Univ Coll Engn Boston MA 02115 USA Northeastern Univ Khoury Coll Comp Sci Elect & Comp Engn Boston MA 02115 USA Northeastern Univ Bouve Coll Hlth Sci Boston MA 02115 USA Univ Chicago Comp Sci Chicago IL 60637 USA NSF CNS Div CISE Directorate Alexandria VA USA Worcester Polytech Inst Dept Elect & Comp Engn & Comp Sci Worcester MA 01609 USA Northeastern Univ Dept Elect & Comp Engn Boston MA 02115 USA Georgia Inst Technol Atlanta GA 30332 USA

ISBN: (纸本)9781665453783

The growing use of computing and proliferation of computing devices requires a holistic focus on sustainability as the environmental impacts of computing technologies go beyond their energy consumption. Environmental impacts span all stages of the lifecycle - manufacturing, operation, and disposal. A sustainability mindset must permeate all organizations involved in design, manufacturing, operation, and disposal/recycling of computational devices. This panel will discuss current and new research on sustainability in computing that spans the full lifecycle, all layers of the computing stack and across the computing spectrum from edge to cloud. The panel will also discuss potential cross-disciplinary approaches to sustainable computing and new notions and metrics to quantify sustainability.

关键词： Environmental impact

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current trends and Challenges of Cloud computing and Emerging Technological Utilization in Technical Education 5

Current Trends and Challenges of Cloud Computing and Emergin...

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5th international conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Kaswan, Suresh Goyal, Parul Khirasaria, Vivek Yugal, Lisha Amita Chandigarh University Department of Computer Science & Engineering Punjab Mohali India M. M. Engineering College Maharishi Markandeshwar Deemed to Be University Computer Science & Engineering Department Haryana Mullana India Sharda University Faculty of Engineering and Technology Uzbekistan Sharda University Department of Computer Science & Engineering Greater Noida India

ISBN: (纸本)9798350379945

This research study explores the impact of cloud computing on engineering education in India, focusing onhow it enhances educational outcomes. This study examines the potentialof strategic cloud implementation in higher education to accelerate development and competitiveness. The study analyzes the current state of technical education in India and identifies areas where cloud integration can drive positive changes. Key research questions are formulated to guide the exploration of creating intelligent cloud applications for effective technical education. © 2024 ieee.

关键词： Cloud platforms

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Exploring Various Data Security Methods for Enhancing Security in Cloud computing 2

Exploring Various Data Security Methods for Enhancing Securi...

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2nd international conference on advanced computing and Communication Technologies, ICACCTech 2024

作者： Rubi Rani, Sunita Saroha, Vinod Kr. Beniwal, Sonal Bhagat Phool Singh Mahila Vishwavidyalaya Department of Computer Science Eng. & IT Sonipat India

ISBN: (纸本)9798331519056

Today in the information technology (IT) field the continued advancement in technology cloud computing plays a crucial role in sharing, storing and retrieving data from anywhere anytime. In the present day, the large number of devices connected through the internet creates big amounts of data and raises security and privacy issues. This paper target is a comprehensive review of various existing data security techniques such as cryptography, steganography, homomorphic encryption, machine learning, deep learning, Deoxyribonucleic Acid (DNA) cryptography, quantum cryptography and blockchain that are used in cloud environments for safe transmission and storage of data. The paper identifies the key points of each technique and shows the compression among them for a better understanding the usability of each technique. It provides the knowledge of which technique is suitable when and how and describes the current status of data security technology. The earlier work regarding different security methods and their identification has been enveloped together in this paper for presenting detailed insight. © 2024 ieee.

关键词： Cloud security

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Cloud-Enhanced Machine Learning Models for Predictive Maintenance in Industrial IoT 2

Cloud-Enhanced Machine Learning Models for Predictive Mainte...

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2nd ieee international conference on trends in Quantum computing and Emerging Business Technologies, TQCEBT 2024

作者： Usharani, R. Sivagami, V.M. Saravanan, K. Pushparani, S. Rekha, K. Sashi Department of Computational Intelligence School of Computing Faculty of Engineering and Technology Srm Institute of Science and Technology Tamil Nadu Kattankulathur India Department of Information Technology Sri Venkateswara College of Engineering Sriperumbudur Tamil Nadu Chennai India Department of Artificial Intelligence and Data Science Care College of Engineering Tamil Nadu Trichy India Department of Computer Science and Engineering Meenakshi College of Engineering Tamil Nadu Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India

ISBN: (纸本)9798350384277

This study examines the implementation and impact of Cloud-Enhanced Machine Learning Models in the realm of Predictive Maintenance within Industrial Internet of Things (IIoT) settings. Emphasizing the integration of cloud computing with machine learning, the research focuses on how this synergy improves the accuracy and efficiency of maintenance predictions in industrial environments. Key metrics evaluated include prediction accuracy, operational efficiency, cost savings, and reduction in equipment downtime. The study demonstrates a significant increase in prediction accuracy, from 65% to 88%, attributed to the advanced data processing and analytical capabilities of cloud-enhanced machine learning. Operational efficiency saw marked improvements, with a 50% reduction in response times and a 25% increase in maintenance scheduling efficiency, leading to a 20% rise in asset utilization rates. Financially, the implementation led to a 37% reduction in annual maintenance costs and a 40% decrease in both the cost per downtime incident and long-term repair expenses. Most notably, the study recorded a 62.5% reduction in both the duration and frequency of downtime incidents. These results highlight the transformative potential of integrating cloud computing and machine learning in IIoT for predictive maintenance, showcasing significant advancements in predictive accuracy, operational efficiency, cost-effectiveness, and overall reduction in downtime. The study underscores the critical role of technological innovation in advancing industrial maintenance strategies, leading to more proactive, efficient, and economically viable operations. © 2024 ieee.

关键词： Predictive maintenance

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Impact of Cybercrime on Enterprises in Cloud computing Environment: A Review

Impact of Cybercrime on Enterprises in Cloud Computing Envir...

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2023 ieee international conference on Emerging trends in Engineering, Sciences and Technology, ICES and T 2023

作者： Hasan, Muhammad Zulkifl Hussain, Muhammad Zunnurain Alam, Intakhab Sarwar, Nadeem Qureshi, Ali Moiz Irshad, Asma University of Central Punjab Faculty of Computer Science and Information Technology Lahore Pakistan Bahria University Department of Computer Science Lahore Campus Pakistan University of Sialkot Department of Information Technology Sialkot Pakistan National College of Business Administration and Economics Department of Computer Science Lahore Pakistan University of the Punjab School of Biochemistry and Biotechnology Lahore Pakistan

ISBN: (纸本)9781665455602

Cloud computing has also provided significant benefits to companies and individuals. With cost-effective services accessible anywhere on the earth with just the Internet, the cloud has been a great source of computing power, with thousands of servers connected to provide users with a better experience and speed. Security in the cloud is a big concern in current times. While the data is stored in the cloud, cybercriminals may find it easy to attack. Giant cloud companies, such as Amazon and Microsoft, have been working to mitigate the issue of cyber criminals by providing high network security. This paper will overview cyber criminal activities and their targeted attacks. Further, the positive impact of cloud computing against cyber attacks and feasible solutions are also part of this research. Finally, a comparative analysis of the proposed work is performed based on selected features to fill the gaps. The comparative analysis shows our work is considered more feature-rich compared to current work to provide authenticity and flexibility for the reader regarding cloud computing enterprises. © 2023 ieee.

关键词： Cost effectiveness

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