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Computing, sciences and Communications (ICCSC), International Conference on

作者： Jayaprakash Sunkavalli B. Madhav Rao M. Trinath Basu Harish Dutt Sharma P. Ashok Kumar Ketan Anand Information Technology Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada Andhra Pradesh India Department of CSE SIR C R Reddy College of Engineering Eluru Andhra Pradesh India Department of CSE KL University Hyderabad TS India Uttaranchal School of Computing Sciences Uttaranchal University Dehradun Uttarakhand India Dept of CSE (Data Science) ACE Engineering College Hyderabad TS India Department of AI&ML Sreenidhi institute of Science and Technology Hyderabad TS India

ISBN: (数字)9798350353648

ISBN: (纸本)9798350353655

The quantum computing made complex computations may be executed at light speed, superpositions allow for parallelism, and massive quantities of data can be efficiently processed. Furthermore, researchers and engineers are quickly becoming interested in natural language processing (NLP) as a topic for developing more comprehensive computational models of NLP. Therefore, there is hope for future progress in NLP efforts that use quantum technology, especially in emotion classification. In this study, we use quantum computing models to delve deeply into two sentiment categorization approaches. To be more specific, the effectiveness of the models is evaluated by looking at VQC and QSVM. The analyzed models are evaluated by simulations in the Qiskit simulator and the real-time quantum computer Qiskit_ibm_osaka. Results demonstrate the importance of the experiment and validate it using positive predicted value, sensitivity and Fl-Score as performance metrices.

关键词： Training Text mining Analytical models Quantum computing Sensitivity Computational modeling Parallel processing Natural language processing Real-time systems Testing

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Advanced Fuzzy Rule Set-Based Approach for Fetal Heart Rate Detection and Classification

Advanced Fuzzy Rule Set-Based Approach for Fetal Heart Rate ...

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International Conference on ICT in Business Industry & Government (ICTBIG)

作者： R. P. Ambilwade Neeraj Giri Regonda Nagaraju Vikas Gupta Subhash Bhagavan Kommina Devanshi Mishra Department of Computer Science National Defence Academy Pune India Department of Computer Science & Engineering IES Institute of Technology and Management IES University Bhopal M.P. India Department of AI & ML School of Engineering Malla Redy University Hyderabad Telangana India Dept of CSE- AIML Gyan Ganga Institute of Technology & Sciences Jabalpur IT Department Sasi Institute of Technology & Engineering (Autonomous) Andhra Pradesh GGITS Jabalpur MP India

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

Fetal heart rate measurement is important in evaluation of fetal well-being and the visible sign of compromise or dysfunction. Congenital heart diseases rank among the main causes of infant mortality and can be identified by means of fetal heart rate monitoring. An effective and reliable method of recognizing and categorizing fetal heart rate is hence important for correct diagnosis and early intervention. The directed research utilizes an improved fuzzy rule set to boost fetal heart rate detection and classification precision. This technique incorporates fuzzy algorithms to adapt to the non-linearity of the fetal cardiotocographic signal as compared to precise methodologies. Some of the difficulties that arise when using FHR analysis include handling of maternal heart rates, power line interference and other biomass signals which tend to complicate the fetal signal. With fuzzy rule sets, this work shall be able to generate better classification between fetal and maternal components for accurate extraction and differentiation of FHR patterns. Moreover, this approach is able to account for uncertainties and temporal changes of the signal and could potentially provide a more robust solution for clinical applications of fetal monitoring and early detection of congenital heart diseases.

关键词： Wavelet transforms Fuzzy logic Accuracy Uncertainty Fetal heart rate Interference Electrocardiography Wavelet analysis Monitoring Diseases

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High-Capacity, A Secret Video Sharing Using DM-Stego and Genetic Algorithm

High-Capacity, A Secret Video Sharing Using DM-Stego and Gen...

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Self Sustainable Artificial Intelligence Systems (ICSSAS), International Conference on

作者： R. Logeshwari Rajasekar Velswamy Subhashini R V Karunakaran Department of Network and Communication Engineering SRM Institute of Technology Kattankulathur Chennai India Department of Computer Science and Engineering SRM Institute of Science and Technology Chennai India Department of Information Technology Sona College of Technology Salem India Dept. of CSE-AI and ML Sri Eshwar College of Engineering Coimbatore India

Electronic media knowledge is unprecedently increasing in recent years. In almost all security control areas, traffic control, weather monitoring, video conferences, social media etc., videos and multimedia data analysis practices are used. As a consequence, it is necessary to retain and transmit these data, by considering the security and privacy issues. IN this research study, a new Div-Mod Stego algorithm is combined with the Multi-Secret Sharing method along with temporary frame reordering and Genetic algorithm to implement high-end security in the process of video sharing. The qualitative and quantitative analysis has also been carried out to compare the performance of the proposed model with the other existing models. A computer analysis shows that the proposed solution would satisfy the requirements of the real-time application.

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Applying Multi-Layer Perceptrons (mlP) for Demand Forecasting in Supply Chain Management

Applying Multi-Layer Perceptrons (MLP) for Demand Forecastin...

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IoT, Communication and Automation Technology (ICICAT), International Conference on

作者： Sreejyothsna Ankam G. Durgadevi Rakesh Kumar Bathula Mounika Zaid Alsalami Niranjanamurthy M Department of CSBS GRIET Bachupally Hyderabad Telangana India Department of ECE New Prince Shri Bhavani College of Engineering and Technology Chennai Tamil Nadu India Department of Computer Engineering & Applications GLA University Mathura Department of Computer Science and Engineering CMR Institute of Technology Hyderabad Telangana India Department Of Computers Techniques Engineering College Of Technical Engineering The Islamic University Najaf Iraq Department of AI and ML BMS Institute of Technology and Management Bangalore Karnataka India

ISBN: (数字)9798350368109

ISBN: (纸本)9798350368116

Demand forecasting plays a significant role in supply chain management and business operational effectiveness. This paper focuses on the use of Multi-Layer Perceptrons (mlP) in demand forecasting which is one of artificial neural networks due to its proved ability to model non-linear patterns. This typology suggests a detailed approach which embraces data gathering, data preparation, model building and model assessment. The mlP model is compared with other models including ARIMA, Random Forest and LSTM models. Therefore, the use of mlP model indicated improved performance in terms of RMSE and MAPE in comparison to traditional approaches. Although LSTM has slightly higher performance, it is relatively more simpler and faster to implement mlP which results to better accuracy and fewer computations. It is identified that historical sales data, seasonal factors and economic factors are some of the most important features that affect the accuracy of the forecast. The use of mlP during the implementation of the project proves the efficiency of the proposed approach and the obtained results. This paper reveals the possibility of mlPs in improving the part of demand forecasting in the supply chain management process and suggested ways that could increase opportunities for better supply chain management through the management of inventory. Further research can be dedicated to the elaboration of the combined approaches utilizing the advantages of the notable methodologies for sales forecasting..

关键词： Economics Analytical models Supply chain management Accuracy Computational modeling Supply chains Demand forecasting Predictive models Data models Long short term memory

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Sentiment Analysis Optimization Using Hybrid Machine Learning Techniques

Sentiment Analysis Optimization Using Hybrid Machine Learnin...

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engineering and Technology (PICET), Parul International Conference on

作者： Ravindra Changala Lakshmi H N G. Venkata Krishna H Bhagya Lakshmi G Uday Kiran Venkata Nagaraju Thatha Department of Computer Science and Engineering Guru Nanak Institutions Technical Campus Hyderabad India Department of Computer Science and Engineering (AIML CS and DS) CVR College of Engineering Hyderabad India Department of CSE(AIML) Prasad V Potluri Siddhartha Institute of Technology Kanuru Vijayawada India Department of ECE Malla Reddy College of Engineering for Women Hyderabad India Department of CSE(AI & ML) B V Raju Institute of Technology Narsapur India Department of Information Technology MLR Institute of Technology Hyderabad India

ISBN: (数字)9798350369748

ISBN: (纸本)9798350369755

Researchers are driven to complete their work in sentiment analysis by the ever-increasing demands placed on government agencies and commercial companies. The way people express themselves on social media is a reflection of their opinions on various products, services, and events. As a subfield of NLP, sentiment analysis aims to extract positive or negative polarities from text found in social media platforms. Three cutting-edge machine learning classifiers-Naive Bayes, SVM, and OneR-are showcased in this study for the purpose of optimizing sentiment analysis. Two benchmark datasets are used in the studies; one dataset is derived from Amazon, while the other is derived from IMDB movie reviews. We compare and analyze the results of these classification methods. The Naive Bayes learned rather quickly, but OneR shows more promise with a precision of 92.6%, an F-measure of 96%, and a properly categorized occurrence rate of 93.4%.

关键词： Support vector machines Sentiment analysis Social networking (online) Reviews Government Machine learning Motion pictures Reflection Bayes methods Optimization

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Detecting Objects in Surveillance Videos with Deep Neural Networks for Crime Scene Analysis

Detecting Objects in Surveillance Videos with Deep Neural Ne...

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Data science, Agents & Artificial Intelligence (ICDSAai), International Conference on

作者： I. Sudha G. Kirubasri M. Deivakani Vijendra Pratap Singh T.Rajesh Kumar M Niranjanamurthy Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai Tamilnadu India Department of Computer Science and Engineering Sona College of Technology Salem India Department of ECE PSNA College Of Engineering And Technology Dindigul India Department of Computer Science & Applications Mahatma Gandhi Kashi Vidyapith Varanasi U.P Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai India Department of AI and ML BMS Institute of Technology and Management(Affiliated to Visvesvaraya Technological University Karnataka) Bangalore Karnataka India

Surveillance systems are critical components of modern security and forensic investigation, and their efficacy is strongly reliant on precise object detection in video recordings. This study looks into the use of deep neural networks to improve object detection in surveillance films for crime scene analysis. This study investigates the capabilities of cutting-edge deep learning architectures in recognizing and classifying objects in various surveillance contexts. The study’s findings are extensive, including detection accuracy metrics for numerous item classes such as “Person,” “Vehicle,” and “Suspicious Item.” Precision values range from 0.78 to 0.92, recall values range from 0.82 to 0.90, and F1 scores range from 0.80 to 0.90, demonstrating the models’ ability to recognize objects accurately, but with variances among item categories. This study also looks into computational performance, offering information about inference times and GPU utilization. Inference times for ResNet-50 and YOLOv3 are 15 ms and 20 ms, respectively, with GPU use percentages of 75% and 90%. These findings provide useful information for picking models that fulfill real-time processing needs while optimizing computational resources. The research also examines the connection between video resolution, detection speed (up to 30 frames per second), and average detection accuracy. Lower resolutions allow for faster processing, but at the expense of accuracy, whilst higher resolutions provide finer details at the expense of larger computational needs. These trade-offs are critical considerations when building surveillance systems to meet certain operational requirements.

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A Robust Development of Calorie Prediction Methodology based on Artificial Intelligence Assisted Machine Learning Model

A Robust Development of Calorie Prediction Methodology based...

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Intelligent Technologies (CONIT), International Conference on

作者： G Naga Venkata RamaKrishna S Sreelakshmi Meenakshi Diwakar S. Ramkumar T Vinithra Banu T. Thilagam Faculty of Engineering University of Technology and Applied Sciences Al Musannah Sultanate of Oman Department of Computer Science and Engineering Saveetha Engineering College Chennai Tamil Nadu India Department of CSE (AI and ML) S.A.Engineering College Chennai Tamil Nadu India Department of Electronics and communication engineering Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Department of Mechanical Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Tamil Nadu India Department of Computer Science & Engineering S.A Engineering college Chennai Tamil Nadu India

ISBN: (数字)9798350349900

ISBN: (纸本)9798350349917

Modern people’s hectic schedules are a direct result of the ways they live and the responsibilities they have at work. On the other hand, maintaining a healthy lifestyle demands consistent physical exertion. Obesity is a result of people not paying attention to what they eat. As a result of modern living, obesity is on the rise. As a result, in order to maintain a healthy weight, people pick their food and exercise habits accordingly. People should know how many calories they consume and how many calories they burn. Calorie counts are easy to get online or on product labels, so it’s not hard to keep track of one’s calorie intake. There aren’t many tools available to help you keep track of the calories you burn. To keep up with the hectic pace of modern life and the constant search for shortcuts, we built a system that uses machine learning to track post-workout calorie burn and development. By taking a few attributes as input, this system can approximate the calories burned, which will show daily growth and motivate people to exercise more. This paper presents an innovative ml model for accurate calorie prediction; it’s called the Improved Learning Model for Calorie Prediction (ILMCP). To test how well it works, the authors cross-validate it with the traditional Extreme Gradient Boosting (XGBoost) algorithm. As a result, it is the quickest approach and may be utilized for object identification in real-time. Next, we’ll apply the ILMCP algorithm to the picture for segmentation. Foreground extraction with little to no user input requires it. Following picture segmentation, we use the probing object’s known volume to determine the food item’s volume. Once the volume has been determined, the food item’s mass may be determined using formulae. Then, the connection between weight and calories can be used to compute the food item’s calories.

关键词： Obesity Schedules Accuracy Target recognition Computational modeling Predictive models Feature extraction

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Blockchain and ai-Driven Palmprint Biometrics for Secure Voting Systems in E-Governance Applications

Blockchain and AI-Driven Palmprint Biometrics for Secure Vot...

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Disruptive Technologies (ICDT), International Conference on

作者： Polaki Sujatha A Soujanya E.J. Priyadharsini S. Thenappan T. Chandrasekar G. Karthikeyan Department of CSE(AI&ML) Prasad V Potluri Siddhartha Institute of Technology Krishna Andhra Pradesh India Department of Computer Science and Engineering CVR College of Engineering Ibrahimpatnam (M) Telangana India Department of ECE Karpagam Academy of Higher Education Coimbatore Tamil Nadu India Department of ECE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of EEE Velammal College of Engineering and Technology Madurai Tamil Nadu India Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai Tamil Nadu India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

This study introduces a secure voting system enhancing voter authentication, data integrity by integrating blockchain technology with an ai-driven biometric scheme, palmprint. The proposed method ensures that fraudulent voting is eliminated and allows trust within the election process in applications of e-governance because of the use of distributed ledger technology and reliable biometric verification. The system leverages the use of smart contracts to enable safe and secure transactions. It makes use of a CNN that has been trained on the PolyU Palmprint Database to check and authenticate palm prints. Simulation results show that the ai model presents phenomenal performance with 99.85% authentication accuracy and 35.42 transaction/sec blockchain free of data integrity issues. The system is also assured of an error-free user experience, excellent scalability, and lower latency. The proposed framework presents an excellent solution towards addressing the apprehensions regarding security and efficacy about the digital voting system that promotes safe and translucent e-governance.

关键词： Biometrics Accuracy Data integrity Scalability Smart contracts Authentication User experience Blockchains Security Artificial intelligence

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Fake News Detection: A Comprehensive Methodology Utilizing Topic Modeling and Machine Learning

Fake News Detection: A Comprehensive Methodology Utilizing T...

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Communication, computer sciences and engineering (IC3SE), International Conference on

作者： Shilpa Choudhary Swathi Gowroju R. Srilakshmi B. Bikram Kumar Deepika Ghai Nitin Rakesh Department of Compter Science & Engineering(AIML) Neil Gogte Institute of Technology Hyderabad India Department of Compter Science & Engineering (AI& ML) Sreyas Institute of Engineering and Technology Hyderabad India Department of Emerging Techonologies(CSE-DS) CVR College of Engineering Hyderabad India Department of Computer Science & Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram India Department of Electronics & Communication Engineering Lovely Professional University Punjab India Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune India

ISBN: (数字)9798350366846

ISBN: (纸本)9798350366853

In this work, we present a thorough methodology that makes use of advanced machine learning algorithms and natural language processing tools to identify fake news. We used Latent Dirichlet Allocation (LDA) for topic modeling in order to identify latent topics within the articles. To depict the topics in a lower-dimensional space, dimensionality reduction techniques like T-Distributed Stochastic Neighbor embedding (t-SNE) are used. To discern between authentic and fake news stories, classification algorithms undergo training using topic representations. We assess the effectiveness of proposed method and obtain 95.64% accuracy, 1.0293 AUC, 0.9347 recall, 0.9595 precision, and 0.9466 F1-score. These outcomes demonstrate how well our methodology works to address this important problem.

关键词： Dimensionality reduction Training Accuracy Machine learning algorithms Stochastic processes Data visualization Forestry

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AACLiteNet: A Lightweight Model for Detection of Fine-Grained Abdominal Aortic Calcification

arXiv

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arXiv 2024年

作者： Ilyas, Zaid Saleem, Afsah Suter, David Reid, Siobhan Schousboe, John T. Leslie, William D. Lewis, Joshua R. Gilani, Syed Zulqarnain Centre for AI & ML School of Science Edith Cowan University Australia Nutrition and Health Innovation Research Institute Edith Cowan University Australia Computer Science and Software Engineering The University of Western Australia Australia Park Nicollet Clinic HealthPartners Institute Minneapolis United States Department of Medicine and Radiology University of Manitoba Canada Department of Electrical and Computer Engineering University of Manitoba Winnipeg Canada

Cardiovascular Diseases (CVDs) are the leading cause of death worldwide, taking 17.9 million lives annually. Abdominal Aortic Calcification (AAC) is an established marker for CVD, which can be observed in lateral view Vertebral Fracture Assessment (VFA) scans, usually done for vertebral fracture detection. Early detection of AAC may help reduce the risk of developing clinical CVDs by encouraging preventive measures. Manual analysis of VFA scans for AAC measurement is time-consuming and requires trained human assessors. Recently, efforts have been made to automate the process;however, the proposed models are either low in accuracy, lack granular-level score prediction, or are too heavy in terms of inference time and memory footprint. Considering all these shortcomings of existing algorithms, we propose ‘AACLiteNet’, a lightweight deep learning model that predicts both cumulative and granular level AAC scores with high accuracy, and also has a low memory footprint, and computation cost (Floating Point Operations (FLOPs)). The AACLiteNet achieves a significantly improved one-vs-rest average accuracy of 85.94% as compared to the previous best 81.98%, with 19.88 times less computational cost and 2.26 times less memory footprint, making it implementable on portable computing devices. © 2024, CC BY-NC-SA.

关键词： Calcification (biochemistry)

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