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6th IEEE International Conference on Artificial Intelligence and Knowledge engineering, AIKE 2023

作者： Mustafa, Ghulam Cho, Seong-Je Hwang, Youngsup Dept. of Computer Engineering Sun Moon University Asan-si Korea Republic of Dept. of Software Science Dankook University Yongin Korea Republic of

ISBN: (纸本)9798350331288

Driving behavior classification plays an important role in many real-world applications, including traffic accident prevention, driver safety, usage-based insurance, and optimizing ridesharing services. In this research, we introduce a novel approach called Hybrid ConvLSTM with Attention for Driver Behavior Classification (HCLA-DBC), designed to achieve precise and reliable driver behavior classification. Our main focus is to distinguish three fundamental driving behavior classes: normal, drowsy, and aggressive, which play a vital role in supporting advanced driver-assistance systems (ADAS). To evaluate the performance of our proposed models, we conducted experiments using the publicly available UAH-DriveSet dataset. The evaluation comprised six distinct models, encompassing two machine learning models (SVM and Gradient Boosting) and four deep learning models (CNN, MLP, LSTM, and HCLADBC). Our HCLA-DBC model outperformed all other models and previous results on the same data. On the test data, it achieved an accuracy of 94.12%, precision of 94.24%, recall score of 94.12%, F1-score of 94.12%, and a macro-average ROC AUC of 98.83. These exceptional outcomes underscore the efficacy of our hybridized approach and its potential to significantly enhance driver behavior classification. The HCLA-DBC model not only promises new possibilities for driver behavior analysis but also paves the way for elevated ADAS capabilities and safer driving experiences. © 2023 IEEE.

关键词： Support vector machines

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Machine Learning-Based Risk Prediction for Coronary Heart Disease Using Clinical Data 1

Machine Learning-Based Risk Prediction for Coronary Heart Di...

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1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Dewangan, Leelkanth Shitole, Mahesh V. Patra, J.P. Khan, Jalaluddin Dawda, Gopesh Kumar Parida, Prasanta Kumar Swangi Dept of Computer Science & Engineering Wardha India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Guntur522502 India Pune India Department of Civil Raipur Institute of Technology Raipur India Chhattisgarh Swami Vivekanand Technical University Bhilai India Kiit University India

ISBN: (纸本)9798350330915

Through the use of machine learning models, particularly Random Forest and Support Vector Machines, this work increases the prediction of CHD risk as well as achieves a remarkably high predictive accuracy. The use of interpretability methodologies clarifies important aspects of CHD risk estimation, stressing the importance of elements like age, cholesterol levels, as well as smoking behavior. Additionally, the creation of a user-friendly application streamlines the risk assessment procedure and improves communication between healthcare professionals and patients. Future studies need to focus on prospective validation, data source diversity, and ethical issues to better advance the subject. CHD risk management has a bright future thanks to the possible integration of real-time patient data and the advancement of AI-driven decision support systems. © 2023 IEEE.

关键词： Coronary heart disease interpretability machine learning prospective validation risk prediction user-friendly tool

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An Optimized Steganographic Technique for 24-Bit Digital Images using Slab Partitioning

An Optimized Steganographic Technique for 24-Bit Digital Ima...

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International Conference on ent Computing and Control Systems (ICICCS)

作者： Pinaki Pratim Acharjya Jayeeta Majumdar Subhabrata Barman Jayashri Deb Sinha Ramsundar Ghorai Arpan Ghosh Dept. of Computer Science and Engineering Haldia Institute of Technology Haldia West Bengal India Dept. of Computer Science and Engineering (Data Science) Haldia Institute of Technology Haldia West Bengal India

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Based on image slabs and sheets, a novel kind of image steganography method for digital images has been proposed in this research work. Using the binary representation of the pixel, this approach splits the cover image into 24 fixed-size sheets. The secret message's binary counterpart is then incorporated into the cover image. It looks through each sheet's rows and columns to locate the one that most closely matches the secret message's binary counterpart. The row or column's position and how it differs from the message it stores in the LSB slab. Compared to previous ways, the proposed technique somewhat modifies the cover image.

关键词： Image quality Steganography Visualization Digital images Control systems Robustness Slabs

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Addressing Weight Competition in Long Short-Term Memory (LSTM) Networks for Improving Temperature Prediction 12

Addressing Weight Competition in Long Short-Term Memory (LST...

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12th International Conference on Awareness science and Technology, iCAST 2023

作者： Lin, Kun-Wei Li, Yu-Fan Lin, Che-Yu Jiang, Zheng-Han Chen, Wei-Jian dept. Computer Science and Information Engineering Chaoyang University of Technology Taichung Taiwan dept. Information Management Chaoyang University of Technology Taichung Taiwan

ISBN: (纸本)9798350324693

This conference paper presents a study on the application of Long Short-Term Memory (LSTM) networks to predict temperature changes in the upcoming week. During our research, we encountered the issue of weight competition within the LSTM architecture, which can lead to suboptimal predictions. To overcome this challenge, three novel methods including Independent Time Series (ITS) Model, Arithmetic Segmented Time Series (ASTS) Model and Arithmetic Segmented Rolling Time Series (ASRTS) Model, respectively, were used to address weight competition and enhance the accuracy of temperature change forecasts. The effectiveness of these methods were evaluated through extensive experiments and compared against standard LSTM models. The results demonstrate the significance of mitigating weight competition to achieve more reliable and accurate temperature predictions. © 2023 IEEE.

关键词： Long short-term memory

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Fraud Detection and Analysis for Insurance Claim using Machine Learning

Fraud Detection and Analysis for Insurance Claim using Machi...

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2022 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems, SPICES 2022

作者： Urunkar, Abhijeet Khot, Amruta Bhat, Rashmi Mudegol, Nandinee Walchand College of Engineering Dept. of Computer Science and Engineering Sangli India Walchand College of Engineering Dept. of Information Technology Sangli India St. John College of Engineering and Management Dept. of Computer Engineering Palghar India

ISBN: (纸本)9781665449403

Insurance Company working as commercial enterprise from last few years have been experiencing fraud cases for all type of claims. Amount claimed by fraudulent is significantly huge that may causes serious problems, hence along with government, different organization also working to detect and reduce such activities. Such frauds occurred in all areas of insurance claim with high severity such as insurance claimed towards auto sector is fraud that widely claimed and prominent type, which can be done by fake accident claim. So, we aim to develop a project that work on insurance claim data set to detect fraud and fake claims amount. The project implement machine learning algorithms to build model to label and classify claim. Also, to study comparative study of all machine learning algorithms used for classification using confusion matrix in term soft accuracy, precision, recall etc. For fraudulent transaction validation, machine learning model is built using PySpark Python Library. © 2022 IEEE.

关键词： Insurance

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Using em technique for Juvenile crime zoning 5

Using em technique for Juvenile crime zoning

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5th International Conference on Information Systems and computer Networks, ISCON 2021

作者： Changalasetty, Suresh Babu Ghribi, Wade Badawy, Ahmed Said Bangali, Harun Ahmed, Abdelmoty M. Thota, Lalitha Saroja Baireddy, Ravinder Reddy Pemula, Rambabu King Khalid University College of Computer Science Dept of Computer Engineering Abha Saudi Arabia Annamacharya Institute of Technology and Science Dept of Computer Science and Engineering Hyderabad India Raghu Engineering College Dept of Computer Science and Engineering Visakhapatnam India

ISBN: (纸本)9781665403412

The primary elements for child criminal behavior are education levels and family background of children and others like friends, money problems, internet, drugs etc. In this research paper, we use expectation-maximization cluster method of data mining to segment juvenile crimes in India with a major risk element of education levels of children. This research will help to visualize the major zones of children involving in crimes and helps to take early preventive steps to mitigate juvenile delinquency © 2021 IEEE.

关键词： Data mining

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Spreading Points Using Gradient and Tabu

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Operations Research Forum 2023年第2期4卷 1-11页

作者： Huang, Xiangyang Huang, LiGuo College of Information Engineering Capital Normal University Beijing 100048 China Dept. of Computer Science Southern Methodist University Dallas 75205 TX United States

Large spreading (packing) problems are hard to be solved exactly. Consequently, heuristic approaches are usually used to find approximate solutions. In this paper, a hybrid heuristic method, consisting of a Neighborhood Tabu (NT) and a Perturbed Gradient (PG), is proposed for solving spreading points problems. NT applies an adaptive random technique to selecting promising candidate solutions from the neighborhood of the current local minimum and PG uses a neighborhood-based perturbed gradient algorithm to seek a better minimum, starting from the candidates. Two procedures work alternatively and cooperatively. When applied to some previously studied packings, the proposed method can improve the accuracy of the previous record solutions. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： Gradient Minimum enclosing ball Packing Spreading Tabu search

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Penalty-Enabled Serverless Architecture for Cloud-based Startup Solutions 6

Penalty-Enabled Serverless Architecture for Cloud-based Star...

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6th International Conference on Inventive Computation Technologies, ICICT 2023

作者： Benedict, Shajulin Subair, Rubiya Gupta, Tanya Vedanta, S.P. Dept. of Computer Science and Engineering Indian Institute of Information Technology Kottayam Valavoor P.O. Kerala Kottayam686635 India

ISBN: (纸本)9798350398496

The step to the success of startups is through overcoming competitors by adopting software innovations that improve businesses. Serverless computing model, recently, has intrigued a sizable number of startup professionals belonging to various sectors, including financial or IoT-enabled application developers. One of the main flaws is its heavy dependency on cloud providers, which can still result in hefty pricing to startups and stalling functions in applications. This article proposes a penalty-enabled serverless architecture for startups. The architecture can boost the economy of startups and can analyze the serverless-oriented cost-saving options in applications. The penalty-oriented approach could enable cloud architects, developers, and startups, to rethink the utilization of serverless functions;to gleam of with future innovations. © 2023 IEEE.

关键词： Economics

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Quantitative Analysis of Error Propagation in Hindi-English Cascaded Speech-to-Speech Translation Models

Quantitative Analysis of Error Propagation in Hindi-English ...

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Advancement in Computation & computer Technologies (InCACCT), International Conference on

作者： Mahendra Gupta Maitreyee Dutta Chandresh Kumar Maurya Dept. of Computer Science and Engineering NITTTR Chandigarh Chandigarh India Dept. of IMEE NITTTR Chandigarh Chandigarh India Dept. of Computer Science and Engineering IIT Indore Indore India

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

Speech-to-Speech Translation (S2ST) plays a crucial role in reducing language barriers and enabling seamless communication between people from different linguistic backgrounds. Traditional S2ST systems typically rely on a cascaded design, where components like Automatic Speech Recognition (ASR), Machine Translation (MT), and Text-to-Speech (TTS) work together in sequence to complete the translation process. Cascaded models are widely used S2ST models and are prone to error propagation (EP) across the pipeline, significantly impacting translation quality. EP is discussed in various literature. However, a comprehensive quantitative analysis is not available, particularly for low-resource languages like Hindi and English. This work presents a detailed quantitative study of EP in Hindi-English cascaded S2ST models, bridging this critical research gap. This study utilizes both text-based and textless evaluation metrics such as BLEU, Translation Edit Rate (TER), and BLASER score for translation accuracy to quantify the impact of EP at various stages of the pipeline on the FLEURES dataset. The result analysis shows that due to EP, the translation quality of the S2ST model decreases in BLEU score of 11.55 for English→Hindi and BLEU score of 12.16 for Hindi→English. Similarly, reference-based BLASER decreases by 0.61 and 0.45 for English→Hindi and Hindi→English, respectively.

关键词： Measurement Analytical models Translation Accuracy Statistical analysis Computational modeling Pipelines Linguistics Text to speech Machine translation

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Breast Cancer Prediction Using Machine Learning

Breast Cancer Prediction Using Machine Learning

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Emerging Systems and Intelligent Computing (ESIC), International Conference on

作者： Shiv Ashish Dhondiyal Pooja Luthra Bhupender Sharma Rajni Rajni Ishita Uniyal Dept. of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Department of Computer Science & Engineering Chandigarh University Mohali Punjab Department of Computer Science and Application Coer University Roorkee Uttarakhand Department Of Computer Science & Engineering Guru Jambheshwar University of Science and Technology Hisar Haryana India Department of Electrical Engineering Graphic Era Hill University

ISBN: (数字)9798331522100

ISBN: (纸本)9798331522117

The number of newly diagnosed cases of breast cancer exceeds 2.3 million annually worldwide. There is a severe lack of valid prognostic and predictive indicators for the clinical treatment of breast cancer patients at the present time. In order to decrease the mortality rate and increase the survival time of breast cancer patients, early detection of this deadly disease is crucial. Improving breast cancer prognosis relies on the analysis and processing of mammography data, the gold standard for screening and early detection. In order to detect breast cancer in mammograms, the Fuzzy C-means algorithm is employed for image segmentation. Next, we train it to identify the characteristics of the segmented regions, and then we use an effective classifier to categorise the trained pictures into their respective mammography classes. This research used the Breast Cancer Wisconsin Diagnostic dataset and two ML algorithms: K-Nearest Neighbours (KNN) and Random Forest. After getting the results, we compared and evaluated the performance of these two classifiers. Potentially useful tools for both early diagnosis and treatment planning include breast cancer prediction models that account for a wide range of risk factors. Effective disease management involves the collection, storage, and administration of many data sets, as well as intelligent systems based on several aspects for the prediction of breast cancer.

关键词： Image segmentation Accuracy Machine learning algorithms Sensitivity Databases Nearest neighbor methods Prediction algorithms Breast cancer Classification algorithms Random forests

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