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A Study and Recognition of Alzheimer's Disease Using Machine Learning 8

A Study and Recognition of Alzheimer's Disease Using Machine...

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8th IEEE International Conference for Convergence in Technology, I2CT 2023

作者： Basha, S.K. Masthan Ashalatha, G. Vara Prasad, Srisailapu D Al-Hameed, Mazin R. Zearah, Sajad Ali Saikumar, K. Vignan's Institute of Management and Technology for Women ECE Department Hyderabad India VNR Vignana Jyothi Institute of Engineering and Technology Department of Computer Science-CS-DS&AI&DS Telangana Hyderabad India GITAM Deemed to Be University Computer Science and Engineering School of Technology Telangana Hyderabad India National University of Science and Technology College of Engineering Technology Department of Medical Device Industry Engineering Dhi Qar Iraq Al-Ayen University Scientific Research Center Thi-Qar Iraq Koneru Lakshmaiah Education Foundation Andhra Pradesh India

ISBN: (纸本)9798350334012

Nowadays, Alzheimer's disease is one of the most severe threats to people of all ages and socioeconomic backgrounds. Their everyday activities may measure this disorder, and how they speak about it shows how concerned they are about it. The way we live today is different from how it was in the past because of our working environment and the lack of opportunities to interact with people. WHO estimates that about 10 billion individuals perish each year due to Alzheimer's disease since not been appropriately diagnosed and treated. An advanced Machine Learning-based recognition system design performs in this research work. The proposed model has been designed based on the human being EEG dataset(testing) and the Kaggle Alzheimer dataset (training). The developed intelligent system will be updated with data collection and pre-processing through the Random Forest machine model to locate our health condition and recognize daily routine changes. Only by developing a machine-based approach will we be able to discover Alzheimer's patients who previously had no idea that they had the illness and are now unable to reverse the progression of their condition. The designed application has attained a sensitivity of 93.01%, an accuracy of 91.32%, a recall of 97.28%, and a detection rate of 98.91%. The implemented application is beneficial to investigators and surgeons for easy treatment. The proposed RFO Machine learning design competes with present technology and outperforms methodology. © 2023 IEEE.

关键词： Intelligent systems

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Ensuring Security in Internet of Everything (IoT) With Smart Sensor Networking and Mobile Ad Hoc Network 3

Ensuring Security in Internet of Everything (IoT) With Smart...

引用

3rd International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Ashalatha, G. Modi, Sanjay Dixit, Anil Kumar Kumar, R. Ravi Al-Chilibi, Hayder Alazzam, Malik Bader Department of Computer Science - CS - DS & AI & DS VNR Vignana Jyothi Institute of Engineering and Technology Telangana Hyderabad India Lovely Professional University Phagwara India Uttaranchal Institute of Technology Uttaranchal University India School of Computer Science and Artificial Intelligence SR University Telangana Warangal India Computer Science and IT Al-Farahidi University Baghdad10021 Iraq Information Technology College Ajloun National University Jordan

ISBN: (纸本)9798350399264

A new age of connection is being ushered in by the Internet of Things (IoT), one that relies on a wide variety of objects and several modulation schemes to communicate. The followings will define this new era of correspondence: The IoT presents a substantially higher number of concerns, restrictions, and problems than do conventional computers since all of its items are interconnected and operate in unprotected contexts. This is because there are fewer linked elements in conventional computer systems. As a result, it is essential that cybersecurity be given priority in a new strategy, although that is not presently true of regular computer platforms. Two key elements in the development of an Internet of Everything system are the Smart Sensor Networking and also the Mobile Ad hoc Network. Sensing, pollution prevention, data collection, multimodal telecommunication networks, and data management are only a few of the many operations that employ this technology. IoT is vulnerable to the same types of safety concerns that other networks face since it combines elements of both Mobile ad hoc networks and Embedded system. A Suspension of Operation is a subtype of an onslaught known as a Delegates Entities Operation. The intruder delivers an unacceptably large number of command sessions that seem genuine. An SD operation is one of the various types of DoS attacks that are possible. This makes it far more difficult to identify this kind of assault than a straightforward one that drains the capacitors capability. The necessity to improve energy conservation and extend the life of Iot networks is one of the other major issues that occur in a networking throughout an SD attack. This is another important problem that develops in a network after an assault. Utilizing a Rng Generation with Heterogeneous Idss, often known as RNGHID, is advised. There are several distinct industries and regions that will make up the Web of Things economy. In order to detect any nodes within the netwo

关键词： Mobile ad hoc networks

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Prevention Of DDOS Attacks in Cloud Using Combinational Learning Approach 4

Prevention Of DDOS Attacks in Cloud Using Combinational Lear...

引用

4th IEEE Global Conference for Advancement in Technology, GCAT 2023

作者： Kalangi, Ruth Ramya Saikumar, Kayam Janardhanarao, S. Veeranjaneyulu, Pagadala Sirisha, J. Suman, M. Department of C.S.E. Koneru Lakshmaiah Education Foundation Vaddeswaram522302 India Koneru Lakshmaiah Education Foundation Green Fields Andhra Pradesh Vaddeswaram522302 India Institute of Aeronautical Engineering College Dundigal Hyderabad India Department of Computer Science-CS-DS&AI&DS VNR Vignana Jyothi Institute of Engineering and Technology Telangana Hyderabad India Dept of IT Prasad V.Potluri Siddhartha Institute of Technology Vijayawada Kanuru India Department of C.S.E. Koneru Lakshmaiah Education Foundation Vaddeswaram52230 India

ISBN: (纸本)9798350305258

Individuals reinforce and staggering intensity of a clouds is an important task. It has delicate security data in perspective on the nonappearance of client-to-cloud controllability. To promise assurance, the data manager reorganizes encoded data before plaintexts. CP-ABE they developed Cipher text-Policy Attribute-based Encryption to arrange fine-grained and manager-motivated admittance power to present the blended records to various customers. In any case, this needs to be adequately protected besides dissimilar strikes. Different precedent strategies do not allow the cloud dealer the capacity to certify to be a downloader can unscramble. These reports are supposed to be available to everyone to unlock the passed-on accumulating. A compromising attacker is to download essential documents to send out EDoS (Economic Denial of Sustainability) attacks, which will, everything considered, gobble up the cloud benefit. Here the cloud association allows the cost. Additionally, the cloud supplier provides both the representative and the payee of preferred standpoint use charge without the straightforwardness to information proprietors. These burdens ought to be settled in authentic open passed-on storing. An action plan is proposed here for secure blended cloud holds from EDoS ambushes, giving benefit operation commitment. It utilizes CP-ABE designs in a disclosure technique and agrees to the discretionary access approach of CP-ABE. Here we provide a fit scattered enlisting way to oversee and keep the DDoS assaults utilizing dynamic character-based encryption. © 2023 IEEE.

关键词： CP-ABE distributed computing EDoS

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Enhanced Skin Lesion Diagnosis using a Deep Learning Techniques

Enhanced Skin Lesion Diagnosis using a Deep Learning Techniq...

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International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： M Sornalakshmi G Elizabeth Rani M Sakthimohan M. Thenmozhi D Amuthaguka V Sanjeevi Kumar Department of PG (CS) Arulmigu Kalasalingam College Of Arts And Science Krishnankoil India Department of AI&DS KIT-KalaignarKarunanidhi Institute of Technology Coimbatore Tamilnadu India Department of ECE KalaignarKarunanidhi Institute of Technology Coimbatore Tamilnadu India Department of AI&DS Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Department of Computer Applications Kalasalingam Academy of Research and Education Tamil Nadu India Department of CSBS KGiSL Institute of Technology Coimbatore Tamilnadu India

ISBN: (数字)9798350367904

ISBN: (纸本)9798350367911

Skin diseases are a serious global public health concern that need to be promptly treated using accurate and efficient diagnostic methods. This study addresses the issue of automated skin disease diagnosis by utilising Convolutional Neural Networks (CNNs), a recent advancement in machine learning. The proposed CNNbased method aims to enhance diagnostic abilities by identifying various skin conditions through the analysis of dermatological pictures. A customised CNN architecture is created for effective feature extraction, and a range of annotated skin image datasets are collected and preprocessed for consistency as part of the technique. The model undergoes rigorous training, validation, and testing processes to ensure high performance. Incorporating the trained model into healthcare systems is one potential technique to assist dermatologists in making early diagnoses of skin issues. This research advances the developing field.

关键词： Training Accuracy Skin Convolutional neural networks Medical diagnosis Lesions Public healthcare Medical diagnostic imaging Diseases Testing

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Ensuring Security in Internet of Everything (IoT) With Smart Sensor Networking and Mobile Ad Hoc Network

Ensuring Security in Internet of Everything (IoT) With Smart...

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Advance Computing and Innovative Technologies in Engineering (ICACITE), International Conference on

作者： Ashalatha. G Sanjay Modi Anil Kumar Dixit R. Ravi Kumar Hayder Al-Chilibi Malik Bader Alazzam Department of Computer Science – CS - DS & AI & DS VNR Vignana Jyothi Institute of Engineering and Technology Hyderabad Telangana India Lovely Professional University Phagwara Uttaranchal Institute of Technology Uttaranchal University School of Computer Science and Artificial Intelligence SR University Warangal Telangana India Computer Science and IT Al-Farahidi University Baghdad Iraq Information Technology College Ajloun National University Jordan

ISBN: (数字)9798350399264

ISBN: (纸本)9798350399271

关键词： computers Protocols Surveillance Organizations Software Safety Internet of Things

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Facial Recognition System with LBPH Algorithm: Implementation in Python for Machine Learning

Facial Recognition System with LBPH Algorithm: Implementatio...

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Intelligent Cyber Physical Systems and Internet of Things (ICoICI), International Conference on

作者： Ramalakshmi K Berin Jeba Jingle Shirley C P Suvisheik V Joyce Beryl Princess P Vidhya K Department of Computer Science and Engineering Alliance University Bangalore Dept. of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Dept. of AI & ML Karunya Institute of Technology and Sciences Coimbatore India Dept. Of CS and DS Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (数字)9798331540661

ISBN: (纸本)9798331540678

Face recognition technology, while making significant advancements, faces challenges in achieving human-level accuracy due to factors like variations in facial appearance, lighting conditions, and noise. This research proposes a novel approach using the Local Binary Pattern (LBP) algorithm to improve face recognition accuracy. By utilizing LBP histograms, our method enables face recognition from both frontal and side views, enhances performance in low-light conditions, and increases real-time identification rates. The primary objective of this research is to develop a robust face recognition system for security applications. The proposed system offers user benefits by preventing unauthorized access and ensuring security. The LBP histogram method is employed for face recognition, requiring users to enter their name and photo into a database. Upon login, the system identifies the user, displaying their name and triggering a warning alert for unknown individuals. The implementation of this face recognition system using the LBP algorithm aims to provide a widely applicable and valuable solution for various organizational settings.

关键词： Histograms Machine learning algorithms Accuracy Face recognition Noise Lighting Machine learning Feature extraction Real-time systems Security

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Prevention Of DDOS Attacks in Cloud Using Combinational Learning Approach

Prevention Of DDOS Attacks in Cloud Using Combinational Lear...

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Global Conference for Advancement in Technology (GCAT)

作者： Ruth Ramya Kalangi S Janardhanarao J Sirisha Kayam Saikumar Pagadala Veeranjaneyulu M Suman Department of C.S.E. Koneru Lakshmaiah Education Foundation Vaddeswaram India Institute of aeronautical engineering college Dundigal Hyderabad Dept of IT Prasad V. Potluri Siddhartha Institute of Technology Kanuru Vijayawada India Koneru Lakshmaiah Education Foundation Green Fields Vaddeswaram Andhra Pradesh Department of Computer science-CS-DS&AI&DS VNR Vignana Jyothi Institute of Engineering and Technology Hyderabad Telangana India

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A Study and Recognition of Alzheimer’s Disease Using Machine Learning

A Study and Recognition of Alzheimer’s Disease Using Machin...

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International Conference for Convergence of Technology (I2CT)

作者： SK Masthan Basha Ashalatha G Srisailapu D Vara Prasad Mazin R. Al-Hameed Sajad Ali Zearah K Saikumar ECE Department Vignan's Institute of Management and Technology for Women Hyderabad India Department of Computer Science-CS-DS&AI&DS VNR Vignana Jyothi Institute of Engineering and Technology Hyderabad Telangana India Computer Science and Engineering School of Technology GITAM Deemed to be University Hyderabad Telangana Department of Medical Device Industry Engineering College of Engineering Technology National University of Science and Technology Dhi Qar Iraq Scientific Research Center Al-Ayen University Thi-Qar Iraq Koneru Lakshmaiah Education Foundation Andhra Pradesh India

关键词： Training Sensitivity Surgery Organizations Data collection Brain modeling Data models

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SmartFit: ai-Powered Training Recommendations with Biometric Integration 5

SmartFit: AI-Powered Training Recommendations with Biometric...

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5th IEEE Global Conference for Advancement in Technology, GCAT 2024

作者： Parwej, Firoj Suganya, A. Naresh, Burri Goud, Veer Sudheer Kaliappan, S. Manikandan, G. Mandsaur University Department of Computer Science and Engineering Daulatpura Madhya Pradesh Mandsaur India Karpagam College of Engineering Department of Information Technology Tamilnadu Coimbatore India Cvr College of Engineering Department of Cse -CS Telangana Hyderabad India St. Martin's Engineering College Deptartment of Ai&ds Secunderabad India Lovely Professional University Division of Research and Development Delhi G.T.Road Punjab Phagwara India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350376685

This research introduces SmartFit, a state-of-the-art technology that seamlessly integrates biometric data and ai-powered training recommendations. SmartFit monitors the user's pulse rate, oxygen saturation, and muscle activity in real time. Subsequently, it implements machine learning algorithms to develop customized training regimens. This ongoing adjustment to the user's evolving fitness levels and objectives guarantees that the workout regimens are both highly effective and customized to their unique requirements. The proposed system is designed to optimize exercise efficacy, promote extended engagement in fitness activities, and improve user satisfaction. The novel method significantly enhanced the fitness ratings and overall satisfaction of participants in comparison to more conventional forms of exercise, as evidenced by a three-month pilot study. The results demonstrate the potential for SmartFit's data-driven, individualized training recommendations to transform the fitness industry. © 2024 IEEE.

关键词： Adversarial machine learning

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Leveraging Deep Learning for COVID-19 Detection: A Comparative Study Using ResNet Models on X-Ray Images

Leveraging Deep Learning for COVID-19 Detection: A Comparati...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Disha Tuteja Anurag Sinha Deepak Chandra Uprety Dyuti Banerjee Ayushi Dewan Chetna Sharma Department of ECE College Guru Tegh Bahadur Institute of Technology Delhi India Department of Computer Science IGNOU New Delhi India Department of Computer Science and Engineering (CS) School of Computer Science in Emerging Technologies Noida Institute of Engineering and Technology Greater Noida India Department of Artificial Intelligence & Data Science (AI&DS) Koneru Lakshmaiah Education Foundation Green Fields Andhra Pradesh India Rawal Institutions Faridabad India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

The COVID-19 pandemic has rapidly spread across the globe, infecting millions of people, with no signs of slowing down. This continuous surge in cases presents a major challenge for healthcare systems worldwide. To combat the exponential rise in infections, medical researchers have primarily relied on RT-PCR testing to distinguish between COVID-19 positive and negative cases. However, this approach faces significant hurdles, particularly in low-income countries where these tests are often too expensive and inaccessible in rural areas. Another critical challenge is the shortage of skilled healthcare professionals, especially doctors and radiologists, whose expertise is in high demand for diagnosing and treating COVID-19 patients. As a result, healthcare resources have been stretched to their limits. To address these issues, innovative solutions are essential, and artificial intelligence (ai) and machine learning (ML) offer promising possibilities. In medical diagnostics, ai, ML, and deep learning (DL) have demonstrated remarkable accuracy. This study explores the application of DL models, specifically ResNet-34 and ResNet-50, for COVID-19 detection using X-ray images. The results indicate an impressive accuracy of 96.4% with ResNet-34. To comprehensively evaluate the performance of these models, the study employs various quantitative metrics, including Matthews Correlation Coefficient (MCC), F1 Score, Precision, and Recall. By contributing to both the effective detection of COVID-19 and addressing the shortage of healthcare professionals, this research provides a hopeful step forward in the ongoing fight against the pandemic.

关键词： COVID-19 Deep learning Measurement Technological innovation Accuracy Pandemics Medical services Public healthcare X-ray imaging Testing

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