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Iraqi Journal for computer Science and Mathematics 2023年第1期4卷 65-70页

作者： Mijwil, Maad M. Aljanabi, Mohammad ChatGPT Computer Techniques Engineering Department Baghdad College of Economic Sciences University IRAQ Baghdad Iraq Department of Computer College of Education Aliraqia University IRAQ Baghdad Iraq Open AI L.L.C. 3180 18th Street San FranciscoCA94110 United States AlSalam university college Iraq

Today, cybersecurity is considered one of the most noteworthy topics that are circulated frequently among companies in order to protect their data from hacking operations. The emergence of cyberspace contributed to the growth of electronic systems. It is a virtual digital space through which interconnection is established between computers and smartphones connected within the Internet of Things environment. This space is critical in building a safe digital environment free of threats and cybercrime. It is only possible to make a digital environment with the presence of cyberspace, which contains modern technologies that make this environment safe and far from unauthorized individuals. Cybersecurity has a wide range of challenges and obstacles in performance, and it is difficult for companies to face them. In this report, the most significant practices, sound, and good strategies will be studied to stop cybercrime and make a digital environment that guarantees data transfers between electronic devices safely and without the presence of malicious software. This report concluded that the procedures provided by cybersecurity are required and must be taken care of and developed. © 2023 Authors. All rights reserved.

关键词： Artificial intelligence

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Signal Processing Techniques for Enhanced Wireless Network Performance 2

Signal Processing Techniques for Enhanced Wireless Network P...

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2nd International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2024

作者： Nirmala, G. Haripriya, M.P. Shaik, Mohammed Rizwan Vijayalakshmi, S. Martis, Jason Elroy Babu, M. dinesh Department of Computer Science and Engineering R.M.D. Engineering College Chennai India Karnataka Bengaluru India Department of Electrical & Electronics Engineering College of Engineering& Computer Science Jazan University Jazan Saudi Arabia Department of ECE R.M.K. Engineering College Chennai India Department of ISE NMAM Institute of Technology Nitte Deemed to be University Karnataka Mangaluru India Department of Mechanical Engineering Rajalakshmi Institute of Technology Chennai India

ISBN: (纸本)9798331541217

With increasing complexity and data rates of wireless communication networks, an effective signal processing technique is significantly required. Memoryless predistortion techniques undoubtedly help to minimise nonlinear distortions in RF power amplifiers. This is so because they guarantee high amplifier output. Methodology: This paper presents a new multichannel wireless signal processing technique to give wireless communication systems memoryless predistortion. The predistortion approach is efficiently applied over numerous channels using wireless signal processing resources. This method lets run in real time with minimum unnecessary hardware overhead. Findings: The experiments reveal that the method based on wireless signal processing is an effective approach for correcting for nonlinear distortions developing in RF power amplifiers. © 2024 IEEE.

关键词： Nonlinear distortion

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Enhancing Oral Cancer Screening with deep learning Algorithms 1

Enhancing Oral Cancer Screening with Deep Learning Algorithm...

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1st International Conference on Computing, Sciences and Communications, ICCSC 2024

作者： Bhaskar, Attarde Viren Soujanya, A. Ramal, P. Janaki Santhana lakshmi, S. Nithyakalyani, K. Kumar, Bharathi Ramesh Dr. D.Y. Patil Medical College Hospital & Research Centre Pimpri Department of Critical Care Medicine Pune India CVR College of Engineering Ibrahimpatnam Department of Computer Science and Engineering Telangana India Saveetha Engineering College Chennai Department of Computer Science Engineering Tamil Nadu India Mohamed Sathak A.J.college of Engineering Siruseri Department of Computer Science and Engineering Chennai India Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Chennai Department of Biomedical Engineering Tamil Nadu India Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi Department of Mathematics Chennai India

ISBN: (纸本)9798350353648

The majority of the conventional approaches used in current oral cancer screening methods, such as biopsy and visual inspection, result in late-stage detection, high false-positive/negative rates, and higher healthcare expenses. These techniques need specialized staff and are labor-intensive and subjective. To improve diagnostic speed and accuracy, the research offers a unique approach that makes use of deep learning methods, particularly Transformer models and Hybrid Neural Networks (HNNs). To enhance input quality, the system incorporates sophisticated image preparation methods such as data augmentation and normalization. While HNNs integrate Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) for robust feature extraction and temporal analysis, Transformers use attention techniques to extract contextual information. Its potential for early and accurate oral cancer detection is highlighted by the results, which show a significant improvement in detection accuracy (94% vs. 80-85% in existing systems), sensitivity (90% vs. 70-78%), and specificity (92% vs. 75-82%), along with a reduction in processing time to just 2 minutes. © 2024 IEEE.

关键词： Convolutional neural networks

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Analysis of COVId-19 datasets Using Statistical Modelling and Machine learning Techniques to Predict the disease

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SN computer Science 2024年第1期5卷 181页

作者： Nramban Kannan, Senthil Kumar Kolla, Bhanu Prakash Sengan, Sudhakar Muthusamy, Rajendiran Manikandan, Raja Patel, Kanubhai K. dadheech, Pankaj Department of Computer Science and Engineering School of Computing Institute of Science and Technology (Deemed to Be University) Vel Tech Rangarajan Dr. Sagunthala R&D Tamil Nadu Chennai 600062 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur 522302 India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli 627152 India Department of Computer Science and Engineering Panimalar Engineering College Chennai 600123 India Department of Electronics and Communication Engineering K. Ramakrishnan College of Technology Tamil Nadu Trichy 621112 India Charotar University of Science and Technology Gujarat 388421 India Department of Computer Science and Engineering Management and Gramothan (SKIT) Swami Keshvanand Institute of Technology Rajasthan Jaipur 302017 India

Sustainable development is crucial for a prosperous future, but epidemic diseases like Coronavirus disease 2019 (COVId-19) pose real and complex challenges. The global pandemic, declared by the WHO on March 11, 2020, has led to significant loss of life and challenges in public health, economy, food systems, and social life. This research article aims to develop a Machine learning (Ml) model to predict a patient’s COVId-19 diagnosis, as early diagnosis is fundamental for controlling the spread of COVId-19. An investigation of the literature and a research study is conducted to find suitable mathematical methods and measure how they impact prediction models. This paper analyses COVId-19 pandemic deaths, confirmed cases, and recovered individuals using Time Series Analysis (TSA) to study the disease's impacts and understand the TAS. A forecasting model can predict future COVId cases by analyzing trends in time-series and connecting global changes with government restrictions. Since higher predictive accuracy is the limitation of ensemble learning algorithms, better Ml approaches are proposed here. Autocorrelation plots clearly showed the results executed for the considered objectives. The hybrid ARIMA algorithm proposed in this work proved adequate results. © 2024, The Author(s), under exclusive licence to Springer Nature Singapore Pte ltd.

关键词： Analysis and visualization Autocorrelation COVId-19 Machine learning Time-series analysis Time-series forecasting

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A Machine learning Approach for Enhanced Security Using Blockchain in Finance Auditing Services 15

A Machine Learning Approach for Enhanced Security Using Bloc...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Sheerin Banu, M. Parveen, Nikhat Gayathri, R. Gera, Anju Kumar, Sunil Anitha, G. R.M.K. Engineering College Department of Information Technology Tamil Nadu Kavaraipettai India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Guntur India Rajalakshmi Engineering College Department of ECE Tamilnadu Chennai India G.L Bajaj Institute of Technology and Management Department of Computer Science and Engineering Uttar Pradesh Greater Noida India SGT University Faculty of Commerce and Management Haryana Gurugrm India R.M.D. Engineering College Department of ECE Tamilnadu India

ISBN: (纸本)9798350370249

In financial auditing services, which is constantly evolving, it is of the utmost importance to establish stringent security protocols. Since the financial sector is increasingly being targeted by sophisticated cyber-attacks, solutions are required to protect sensitive information and prevent the integrity of financial audits from being compromised. Conventional security measures, in many instances, do not devote sufficient attention to addressing the known vulnerabilities that exist in the auditing systems that are currently in use. This research proposes a novel approach to enhance security in financial auditing services by integrating Support Vector Machines (SVM) with Blockchain technology. By combining Blockchain technology with Machine learning (Ml), and more specifically SVM, this research suggests a novel approach to enhancing the level of security that can be achieved. Taking advantage of the inherent capability of SVMs to correctly categorise and predict potential security concerns, the research makes use of these models for the purposes of machine learning. Utilising Blockchain technology allows for the generation of a record that can be audited since it is reliable and verifiable. By combining SVM with Blockchain, it is possible to quickly identify and resolve any security breaches that may have occurred. The findings indicate that the level of protection provided by financial auditing services has significantly increased in recent years. Considering that the SVM with Blockchain architecture can recognise and classify a wide variety of threats, it has the potential to be utilised in the process of making defences more proactive and robust. © 2024 IEEE.

关键词： Support vector machines

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Necessity of Self-explainability in AI: Insights from the Existing Social Projects

Necessity of Self-explainability in AI: Insights from the Ex...

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International Conference on Next Generation Systems and Networks, BITS EEE CON 2022

作者： Jadhav, Mayur K. laddha, Shilpa S. Kabra, Sanchit M. Ph.D. Student Department of Computer Science Engineering Government College of Engineering Maharashtra Chh. Sambhajinagar India Head of Department Information Technology Government College of Engineering Maharashtra Chh. Sambhajinagar India Computer Science Engineering BITs Pilani India

ISBN: (纸本)9789819904822

Self-explanatory artificial intelligence has lately gained prominence as researchers and practitioners strive to make their methodologies more understandable. It is not contentious to assert that researching how individuals explain to one another may be a useful beginning point for explanation in artificial intelligence, given that the majority of this research has focused on publicly explaining decisions or actions to a human observer. However, it is reasonable to say that the vast bulk of self-explanatory artificial intelligence research is based solely on the researchers’ subjective opinions of what constitutes a "good" explanation. Extensive and essential study in science and technology has been conducted on the definition, creation, selection, assessment, and presentation of explanations by humans. This study contends that people use certain cognitive biases and social expectations in the explanation process. This paper covers relevant studies from several fields and makes the case that self-explainable artificial intelligence should expand on some current research. It highlights several key results and explores how they may be combined with research on self-explainable artificial intelligence. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte ltd.

关键词： Explainability Explanation Interpretability Self-éxplainable AI Transparency

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Artificial Intelligence and Internet of Things Enabled Intelligent Framework for Active and Healthy living

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computers, Materials & Continua 2023年第5期75卷 3833-3848页

作者： Saeed Ali Alsareii Mohsin Raza Abdulrahman Manaa Alamri Mansour Yousef AlAsmari Muhammad Irfan Hasan Raza Muhammad Awais Department of Surgery College of MedicineNajran University Saudi ArabiaNajran61441Saudi Arabia Department of Computer Science Edge Hill UniversitySt Helens RdOrmskirkL394QPUnited Kingdom Electrical Engineering Department College of EngineeringNajran University Saudi ArabiaNajran61441Saudi Arabia Department of Electrical Engineering Hamdard UniversityIslamabadPakistan

Obesity poses several challenges to healthcare and the well-being of *** can be linked to several life-threatening *** is a viable option in some instances to reduce obesity-related risks and enable weight ***-of-the-art technologies have the potential for long-term benefits in post-surgery *** this work,an Internet of Things(IoT)framework is proposed to effectively communicate the daily living data and exercise routine of surgery patients and patients with excessive *** proposed IoT framework aims to enable seamless communications from wearable sensors and body networks to the cloud to create an accurate profile of the *** also attempts to automate the data analysis and represent the facts about a *** IoT framework proposes a co-channel interference avoidance mechanism and the ability to communicate higher activity data with minimal impact on the bandwidth requirements of the *** proposed IoT framework also benefits from machine learning based activity classification systems,with relatively high accuracy,which allow the communicated data to be translated into meaningful information.

关键词： Artificial intelligence healthcare obesity Internet of Things machine learning physical activity classification activity monitoring

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Hdl Environment for the Synthesis of 2-dimensional and 3- dimensional Network on Chip Mesh Router Architecture

HDL Environment for the Synthesis of 2-Dimensional and 3- Di...

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2024 International Conference on Communication, computer Sciences and engineering, IC3SE 2024

作者： Kumari, Shweta Rajput, Kapil Singh, Gurwinder Jain, Arpit Sachi, Savya Manwal, Manika Shri Fanishwar Nath Renu Engineering College Department Of Computer Science & Engineering Bihar Araria India Graphic Era Hill University Computer Science And Engineering Uttarakhand Dehradun India Chandigarh University Department Of Aitcse Punjab Mohali India Koneru Lakshmaiah Education Foundation Department Of Computer Science & Engineering AP Vaddeswaram India L N Mishra College Of Business Management Department Of Information Technology Bihar Muzaffarpur India Graphic Era Deemed To Be University Computer Science And Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350366846

Application specific Network on Chip (NoC) designs are quickly becoming the technology of choice for solving the problem of multiprocessor system architecture. Broadband, interposes communication, deadlock avoidance, port bandwidth, router architecture, and bespoke NoCs may all be improved by modelling and simulation of multilayer network structures and synthesis. Topology-based organization of 2d and 3d routers allows for the construction and ladyout of NoCs. The articles compare and contrast the design and analysis of two-dimensional and three-dimensional network-on-chip routers for setting up mesh topological nodes in a network. Xilinx 14.2 is used to execute the code. The most recent version of modalism, 10.0 student edition, is used to functionally mimic the program and modules. The experimental work and thorough testing on the Virtex-5 FPGA confirm the chip design. © 2024 IEEE.

关键词： Network-on-chip

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A Multi-Source Approach to Film Recommendations using Social Media, Search data, and Streaming History 5

A Multi-Source Approach to Film Recommendations using Social...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Raj, J. Relin Francis Karthikeyan, M. Saravana Rajkumar, G. Vinoth Shankar, S. Vijay Isaac, R. Augustian Anushya, S. Thanga Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Avadi Chennai India J. P. College of Engineering Department of Electronics and Communication Engineering Tenkasi India Sona College of Technology Department of Electrical and Electronics Engineering Salem India Saveetha Engineering College Department of Artificial Intelligence and Machine Learning Thandalam Chennai India SCAD College of Engineering and Technology Department of Computer Science and Engineering Cheranmahadevi India

ISBN: (纸本)9798331530013

In the rapidly evolving OTT platforms, providing highly personalized and relevant film recommendations has become a critical challenge. Traditional recommendation systems primarily rely on user ratings and watch history, neglecting the vast potential of other data sources such as social media interactions and search engine queries. This research presents a multi-source film recommendation framework, integrating data from social media posts, search behaviour, and OTT streaming history to deliver enhanced personalization. A novel algorithm, CoFiSent (Collaborative Filtering with Sentiment Analysis), is proposed, combining collaborative filtering techniques with sentiment analysis of user-generated content from social media. By analysing user sentiments and preferences, the algorithm refines recommendations to match users' dynamic interests more effectively. The performance of CoFiSent is evaluated against existing algorithms, including standard collaborative filtering and content-based filtering models. Simulations are conducted using real-world datasets, and the system's effectiveness is measured through precision, recall, F1-score, and mean reciprocal rank (MRR). Results demonstrate that CoFiSent outperforms traditional methods, particularly in capturing emerging trends and contextual preferences reflected in social media activities. The integration of multiple data sources enhances the overall accuracy and relevance of recommendations, offering a significant improvement in user satisfaction. The research findings suggest that incorporating social media sentiment and search behaviour into film recommendation systems represents a promising approach for next-generation entertainment platforms. © 2024 IEEE.

关键词： Collaborative filtering

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design and development of a Touch Free Smart Home Controlling System Based on Virtual Reality (VR) Technology

Design and Development of a Touch Free Smart Home Controllin...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： devi, S. Rukmani Raghi, K.R. Priya, S. Geetha Sathi, G. Kumar, Sarva Naveen dinesh, M. Saveetha College of Liberal Arts and Sciences Simats Deemed to Be University Department of Computer Science Tamil Nadu Chennai India St.Joseph's College of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India R.M.D. Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Mechanical Engineering Tamil Nadu Chennai India Mlr Institute of Technology Department of Electronics and Communication Engineering Hyderabad India Saveetha University Department of Vlsi Microelectronics Tamil Nadu Chennai India

ISBN: (纸本)9798350375237

The Touch-Free Smart Home Controlling System based on Virtual Reality (VR) Technology represents an innovative approach to home automation, leveraging the immersive capabilities of VR to provide users with seamless and intuitive control over their smart home environment without the need for physical touch. This system integrates VR headsets with motion sensors, depth cameras, and gesture recognition technology to enable users to navigate and interact with a virtual representation of their home environment. Smart home devices such as lights, thermostats, door locks, and entertainment systems are interconnected and controlled through the VR interface, offering users a compelling and immersive way to manage their living space. The central hub acts as the backbone of the system, facilitating communication between the VR interface, sensors, and smart home devices. Performance metrics such as response time, latency, throughput, and power consumption are monitored to ensure optimal system operation. Security measures including user authentication and data encryption are implemented to protect user privacy and prevent unauthorized access. The Touch-Free Smart Home Controlling System based on VR Technology not only enhances convenience and accessibility but also represents a significant step forward in the evolution of smart home automation towards more intuitive and immersive user experiences.. © 2024 IEEE.

关键词： Gesture recognition

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