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Machine Learning-Enhanced Voice Interaction: Revolutionizing Windows Desktop Applications

Machine Learning-Enhanced Voice Interaction: Revolutionizing...

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Advances in computer Science, Electrical, Electronics, and Communication Technologies (CE2CT), International conference on

作者： Raj Vora Rajesh Kumar Chakrawarti Namrata Raghuwanshi Computer Science and Engineering Sushila Devi Bansal College of Technology Indore India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

The use of voice interaction is changing the way human being communicate with devices as it offers a simple and effective way of interaction. To implement voice interaction within desktop applications and make it more effective as it is integrated with the operating system, there is a various need models, to particularly improve Convolutional its neural quality. Networks This (CNNs), paper Recurrent considers Neural how Networks (RNNs), and transformers, can enhance the effectiveness of voice interaction in the context of Windows desktop platforms. The research focuses on several major issues: enhancing the speech recognition performance in the presence of background noise, handling the differences in user's accent and speech manner, and real-time operation with reduced delay. It also examines issues of privacy relating to handling of sensitive voice information and ways of reducing these vulnerabilities. Using and enhanced user machine intent learning and results operating encompass connecting detection strategies, the indicate system the in this user's and that voice possibility developing voice research experience provides the interaction the of recognition in is an application with next increasing systems. Different designed better of other these generation the practical user to machine current models of multilingual applications experience. Improve learning and are desktop capabilities, including the enhances future assessed apps. Implementing productivity current The AI from the tools, limited paper effectiveness solutions, the ability accessibility capabilities also and thus perspective to features of provides quality stating of recognize and contextual recommendations of that performance, users' intelligent understanding for voice flexibility emotions, assistants. future control is and the research, commands the which on key them to Windows.

关键词： Productivity Recurrent neural networks Operating systems Speech recognition Speech enhancement Transformers User experience Real-time systems Multilingual Convolutional neural networks

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AI-Enabled Safe and Scalable Pet Care: An Intelligent System for Real Time Health Monitoring and Care

AI-Enabled Safe and Scalable Pet Care: An Intelligent System...

引用

International conference on ent Computing and control systems (ICICCS)

作者： Aravind G Jeeva S Selvaganesh D S.P. Sasirekha Department of Computer Science Engineering Karpagam Academy of Higher Education Coimbatore India

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Modern pet health management requires advanced solutions because the world demonstrates rising demand for companion animals. The current pet care systems operate without real-time data monitoring because they do not adapt to shifting diagnostic criteria. An AI-powered pet management system based on Internet of Things (IoT) links a secure cloud-edge computing system to offer these capabilities. A proposed system depends on strong machine learning techniques to deliver persistent health tracking while simultaneously analyzing behavioral patterns and carrying out automatic care operations. Through its IoT-connected smart sensors the system obtains real-time data which enables it to identify anomalies as soon as possible while providing tailored medical solutions. A modular distributed architecture design offers better system adaptability thus making possible seamless operations between multiple operational domains. Security is of utmost importance in the system so a combination of encryption layers works together with authentication protocols and monitored data channels serve to secure both privacy and data integrity. The experimental findings reveal that predictions about behavioral outcomes together with optimized resource usage and system protection function at superior rates than traditional methods do. A thorough analysis of both ethical practices and data security measures exists for automated pet supervision to facilitate responsible program execution. This recommended system gives remote accessibility together with multi-functional pet care operations to provide safe services which benefit all pet owners along with veterinarians and additional stakeholders. The future research agenda includes developing hybrid federated learning systems to deliver individualised diagnoses as well as using diversified health data from various sources to conduct more thorough examinations while striving to enable this monitoring method in various animal care settings. Such resear

关键词： Ethics Animals Data security Scalability computer architecture control systems Real-time systems Internet of Things Stakeholders Monitoring

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A Survey on Vehicle Collision Avoidance systems: Innovations, Challenges, and future Prospects

A Survey on Vehicle Collision Avoidance Systems: Innovations...

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Artificial Intelligence and Data Engineering (AIDE), International conference on

作者： Ramesh G Kiran Raj K M Abhishek Mahim Tharesh Devadiga Manohara M Shashank Boloor Sowjanya N Dept. AIML AIET Mangalore India Assistant Lecturer NITK Surathkal India

ISBN: (数字)9798331527518

ISBN: (纸本)9798331527525

Vehicle Collision Avoidance systems (VCAS) enhance road safety by enabling vehicles to autonomously detect and respond to potential hazards using technologies such as radar, LiDAR, cameras, V2X communication, and machine learning algorithms. Key features like Adaptive Cruise control, Autonomous Emergency Braking, and Lane Departure Warning help prevent accidents and improve driver assistance. Despite challenges like sensor limitations in adverse conditions, communication delays, and cybersecurity risks, advancements in sensor accuracy, decision-making algorithms, and edge computing continue to drive innovation. This paper highlights the importance of technological improvements, regulatory frameworks, and system interoperability in advancing VCAS adoption and achieving safer, autonomous transportation.

关键词： Surveys Technological innovation Machine learning algorithms Sensor fusion Road safety Collision avoidance computer security Vehicle-to-everything Vehicles Cruise control

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A Comprehensive Review of Traffic Generators in Software-Defined Networking (SDN)

A Comprehensive Review of Traffic Generators in Software-Def...

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AIML-Applications for Engineering & Technology (ICAET), International conference on

作者： Medha N Kulkarni Jagdish W Bakal Computer Engineering Department Pillai HOC College of Engineering and Technology Mumbai India

ISBN: (数字)9798350355611

ISBN: (纸本)9798350355628

A major change in network architecture is represented by Software-Defined Networking (SDN), which separates control plane from data plane i.e. forwarding operations. Network management is made more adaptable and effective by this approach. Traffic generators are essential tools for testing and assessing SDN networks because they can simulate a wide range of network traffic patterns. This study examines the most popular SDN traffic generators, like Iperf, MGEN, DITG, Ostinato assessing their characteristics and suitability for various SDN scenarios. future directions and new trends in traffic generation under SDN frameworks are also covered in the paper.

关键词： Measurement Protocols Reviews Telecommunication traffic Traffic control Network architecture Generators Real-time systems Software defined networking Testing

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An Ontology-Based Representation of Knowledge for Specifying Ransomware Behavior

An Ontology-Based Representation of Knowledge for Specifying...

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International ECTI Northern Section conference on Electrical, Electronics, computer and Telecommunications Engineering (ECTI-NCON)

作者： Manh-Duc Hoang Duc-Anh Nguyen Nhat-Huy Nguyen Viet-Chien Nguyen Duy-Huy Doan Viet-Nam Ta Linh Manh Pham ICT Department FPT University Hanoi Vietnam Information Assurance FPT University Hanoi Vietnam Information Technology UET University Hanoi Vietnam

ISBN: (数字)9798331543273

ISBN: (纸本)9798331543280

This paper introduces CyberRanto, an ontology-based framework for analyzing ransomware behavior. As ransomware attacks grow more sophisticated, structured knowledge representation becomes crucial. CyberRanto, developed from a thorough review of ransomware lifecycle stages, categorizes actions into phases like infiltration, Behavior detail properties, and ransom demand. This aids in early detection, prevention, and investigation of cyber threats. Leveraging real-world instances and diverse behavioral patterns, the framework enhances threat intelligence, supports digital forensics, and is useful for cybersecurity training. future research may adapt this framework to emerging ransomware variants, maintaining its relevance in dynamic threat landscapes.

关键词： Command and control systems Training Phishing Prevention and mitigation Real-time systems Encryption Electronic mail Behavioral sciences Ransomware Monitoring

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A Review on the Use of Smart Agriculture systems to Protect Crops

A Review on the Use of Smart Agriculture Systems to Protect ...

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Electronics and Renewable systems (ICEARS), International conference on

作者： Gulshan Kumar Palash Gourshettiwar Saurabh Suman Shravan Kumar Dhiraj Kumar Rahul Raushan Department of Computer Science and Design Faculty of Engineering and Technology Wardha Maharashtra India Department of Computer Science and Medical Engineering Faculty of Engineering and Technology Wardha Maharashtra India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

Smart agriculture systems leverage the possibilities offered by cutting-edge technologies such as IoT, AI, and remote sensing to revolutionize conventional farming by enhancing resource utilization, production, and crop damage mitigation. Real-time monitoring of soil and crop health, predictive analytics, pest control, and precision irrigation measures are all enabled by these systems. They are able to address major Indian agriculture issues, consequently boosting yield and profitability and promoting environmental sustainability. The largescale deployment of intelligent agriculture systems will change the agriculture landscape in India and will assure long-term food security for an ever-growing population. Challenges include adequate research and future studies in order to better install and achieve smart agricultural systems to protect crops. Intelligent agriculture involves all advanced research, including science and innovations, in national development through space technologies to enhance soil quality, conserve water, and facilitate agriculture information. Space ventures will undergo improved modernization through the introduction of crop sprayers, precision gene editors, epigenetics, big data analytics, IoT, wind and photovoltaic smart energy, AI-enabled robotic applications, and wide-scale desalination technologies. Implementing digital farming systems in developing economies will help their sectors as 85 percent of the global population is set to live in developing countries by 2030. Automation will prove to be necessary since food scarcity is on the rise along with resource wastage. control strategies such as the IoT, aerial imagery, machine learning, and artificial intelligence will boost production and prevent soil degradation. These advanced technologies are also able to alleviate such issues as plant disease detection, pesticide management, and water application. The introduction of the Internet of Things in the agricultural research world has started

关键词： Smart agriculture Technological innovation Space technology Crops Production Soil Communications technology Internet of Things Remote sensing Farming

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A Study on Next-Generation Malware Reverse Engineering Paradigms

A Study on Next-Generation Malware Reverse Engineering Parad...

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Computing for Sustainability and Intelligent future (COMP-SIF), International conference on

作者： Chandana D M Mohan B A Quitika Reddy P N Murthy M S Department of Information Science BMS Institute of Technology & Management Bangalore India Department of Information Science BMS Institute of Technology & Management Bengaluru India

ISBN: (数字)9798331538538

ISBN: (纸本)9798331538545

As cyber threats evolve, complex malware targets various systems, weak points in a wide range of situations and can't be stopped by conventional measures. To address these emerging and potent threats, this paper examines the next-generation malware reverse engineering paradigms. We discuss how AI/ML is applicable to the topic for anomaly detection, hypervisor-based introspection to secure continuous monitoring, memory analysis for file-less malware, and system calls mapping that helps in tracking the execution flow of malware. Measures, which allow countering modern malware's features such as obfuscation, polymorphism, and encrypted messages, are described, and they include methods like a hybrid approach to threat analysis, adaptable sandboxes, and heuristic modeling. Some of these techniques have been showcased herein with real-life examples captured in the enterprise and industrial control systems domains. Prevention techniques including the use of prediction models and automatic response mechanisms to control threats are presented to improve threat detection and mitigation. Lastly, in the area of future perspectives for malware detection and analysis, the research looks at quantum-resistant security features, the adoption of blockchain technologies for safe threat sharing as well as progress in cognitive computing for supporting decision-making processes. Such emerging paradigms are emblematic of the need for constant change to preserve a high-security level as the threats keep growing much more severe in the present day context.

关键词： Threat modeling Analytical models Adaptation models Prevention and mitigation Reverse engineering Machine learning Malware Complexity theory computer security Next generation networking

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Classifying Human Emotions through EEG data with Machine Learning

Classifying Human Emotions through EEG data with Machine Lea...

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Intelligent systems and Computational Networks (ICISCN), International conference on

作者： Gaganjot Kaur Meenu Gupta Rakesh Kumar Department of Computer Science and Engineering Chandigarh University Punjab India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

The Psychological and neurobiological phenomenon is called an emotion. The capability of human beings to present different expressions clashes with the emotional state of mind. Therefore, it is challenging to identify a human’s real emotional state by observing physical appearance. Nowadays, using emotional engineering to predict human emotions is one of the major attempts of Machine Learning (ML). This study aims to investigate the usage of techniques of machine learning for efficient emotion prediction that relies on electroencephalography (EEG) signals. EEG dataset is complex and sometimes noisy, making it difficult to interpret using traditional methods. So, in this research the Kaggle dataset collected through a Muse headband, five distinct ML methods are applied to classify emotions. Performance metrics such as precision, recall, F1 score, support, and accuracy are employed to differentiate between different classification techniques. Random Forest turned out to be the best method due to its high accuracy 99%. Three emotions are predicted based on the results of performance metrics. This study benefits mental health monitoring, neurofeedback therapy, stress management, human-computer interaction, etc. This study lays the groundwork for exploring this field and helps develop more sophisticated control systems in the future.

关键词： Support vector machines Radio frequency Accuracy Nearest neighbor methods Electroencephalography Object recognition Noise measurement Random forests Neurofeedback Monitoring

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Explainable Artificial Intelligence Paradigm in Cardiovascular Plaque Tissue Characterization and Classification for Risk Prediction

Explainable Artificial Intelligence Paradigm in Cardiovascul...

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International conference on ent Computing and control systems (ICICCS)

作者： Saritha Lalitha Ravindran Dipti Jadhav Pankaj K. Jain Department of Computer Engineering Ramrao Adik Institute of Technology D Y Patil Deemed to be University Navi Mumbai Maharashtra India Department of Computer Engineering Indian Institute of Technology Banaras Hindu University Varanasi UP India

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Atherosclerosis is the predominant cardiovascular disease caused by the blockage of blood vessels in various parts of the body which restricts the blood flow and can result in life-threatening conditions like stroke and heart attack. Several studies prove cardiovascular diseases are the major cause of fatality globally. Early detection of this precarious situation assumes importance in the proper administration of treatment methodologies. Artificial intelligence-based models can be effectively used for the segmentation, characterization, and classification of medical images to support clinicians in predicting the severity of blockages with improved accuracy and transparency. Conventional artificial neural network frameworks are perceived as ‘black-boxes’ as they are difficult to analyze and interpret by medical practitioners. Hence there is a growing interest in developing explainable ‘white-box’ techniques that are fully transparent and easily interpretable for medical and biological image processing to complement the existing approaches. This work covers a detailed literature review on how explainable artificial intelligence architectures enhance plaque segmentation, characterization, and classification. Also discusses the different modalities used for image acquisition, details of how model accuracy is calculated, limitations of the study, and future directions.

关键词： Deep learning Image segmentation Accuracy Explainable AI Computational modeling Predictive models Stroke (medical condition) Reliability Biomedical imaging Systematic literature review

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DRIVE: A Mobile Application for Directed, Remote, Interactive Viewing and Exploring

DRIVE: A Mobile Application for Directed, Remote, Interactiv...

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25th International conference on Internet Computing and IoT, ICOMP 2024, and 22nd International conference on Embedded systems, Cyber-physical systems, and Applications, ESCS 2024, held as part of the World Congress in computer Science, computer Engineering and Applied Computing, CSCE 2024

作者： Cates, Sonya Desanti, Rudolph Ramirez, Roderick Witham, Kyle Roger Williams University BristolRI02809 United States

ISBN: (纸本)9783031859229

Technologies to support remote interaction hold great potential to expand opportunities for engagement and interaction. In this work, we present a mobile application to allow remote exploration of an environment by those who cannot be physically present due to geographic or mobility limitations. Our system is designed to place control of the experience with the audience or viewer to encourage greater engagement with the remote environment. After presenting the application, we discuss how the application will be used and tested in realistic contexts and present future extensions to create a more immersive experience. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： computer supported cooperative work

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