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Detection and Mitigation of Attacks in MANETs: A Comprehensive Survey of Security Techniques 4

Detection and Mitigation of Attacks in MANETs: A Comprehensi...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Mohandas, R. Sivapriya, N. Vaigandla, Karthik Kumar Kirubasankar, K. Chennai Institute of Technology Department of Electronics & Communication Engineering Tamil Nadu Chennai India Cauvery College for Women Department of Computer Applications Tamilnadu Trichy India Balaji Institute of Technology and Science Department of Electronics & Communication Engineering Telangana Warangal India

ISBN: (纸本)9798331529635

MANETs are decentralized and self-organizing networks with no fixed infrastructure. They are very flexible but, at the same time are vulnerable to the huge diversity of security threats. The dynamic topology and the lack of central control along with the reliance on wireless communication expose MANETs to several attacks, which are blackhole, wormhole, Sybil, and DoS attacks. This is a survey paper that covers the most common security threats towards MANETs and probes for different detection and prevention techniques proposed to mitigate those risks. Various methodology are examined, ranging from cryptographic methods and trust-based models to machine learning and intrusion detection systems, comparing the effectiveness, scalability, and adaptability of those approaches in different network environments. The paper further outlines some real strengths and weaknesses of the currently used solutions, indicates potential key challenges, and states the future directions that could form the way for strengthening the security capabilities of MANETs with respect to evolving emerging technologies such as 5G and beyond. The aim is to demonstrate the ongoing state of the attack detection and prevention in MANETs, thereby offering insights into promising research opportunities. © 2024 IEEE.

关键词： Network intrusion

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Fluid Segmentation in Retinal OCT images with Pigment Epithelial Detachment 1

Fluid Segmentation in Retinal OCT images with Pigment Epithe...

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1st International Conference on Advances in Electrical, Electronics and Computational Intelligence, ICAEECI 2023

作者： Sheeba, T.M. Raj, S. Albert Antony Anand, M. SRM Institute of Science and Technology Faculty of Science and Humanities Department of Computer Applications Chennai Kattankulathur India SRM Institute of Science and Technology Faculty of Engineering and Technology Department of Networking and Communications Chennai Kattankulathur India

ISBN: (纸本)9798350342796

For the assessment of Pigment Epithelial Detachment(PED), optical coherence tomography (OCT) must automatically and precisely segment the fluid related subretinal fluid. An algorithm for guided fluid segmentation in two stages is suggested. To get the primary curve of the fluid part for the Graph cut approach, the candidate fluid region is automatic determined in the first step. The Gaussian pre-fitting approach is suggested to segment sub-retinal-fluid in the second stage. The suggested algorithm is evaluated using the testing data set, which consists of 55 longitudinal OCT scans from 50 eyes of 50 individuals. DC (Dice Coefficient), Precision, and Recall of the proposed system are calculated. The suggested algorithm can achieve excellent segmentation accuracy without the need to segment the retinal layer. This approach has the potential to enhance clinical PED therapy and may be able to reliably segregate subretinal fluid from OCT images. © 2023 IEEE.

关键词： Graph cut method Local Gaussian Pre-fitting OCT PED Subretinal Fluid Segmentation

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A Deep Review to Find the Most Accurate Method among KNN,RF and MLP for Predicting Alzheimer Disease 4

A Deep Review to Find the Most Accurate Method among KNN,RF ...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Subathra, K. Malakar, Antara Suhasini, S. Prakash, Sneha Abed, Sarah Jassim Abdtawfeeq, Tareq Hafdhi Sri Sairam Engineering College Department of Mathematics Chennai44 India The Assam Royal Global University Department of Computer Science & Engineering Guwahati India Cvr College of Engineering Hyderabad India Adi Shankara Institute of Engineering & Technology Department of Computer Applications Kalady India National University of Science and Technology Collage of Nursing Dhi Qar64001 Iraq Al-Farahidi University Baghdad Iraq

ISBN: (纸本)9798350360165

Debilitating neurodegenerative conditions like Alzheimer's disease (AD) are linked to great pain and eventually death. For the purpose of successful treatment planning and individualized therapy, it is imperative that Alzheimer's disease (AD) and its precursor phases, such as moderate cognitive impairment (MCI), be identified as soon as possible. Single nucleotide polymorphisms, or SNPs, and neuroimaging data, such as positron emission tomography (PET) and magnetic resonance imaging (MRI), have been found to work better together to improve the ability to distinguish between AD/MCI and normal control (NC) participants. But missing data from multimodal sources will always exist, and model learning is complicated by the variety of phenotypic and genotypic biomarkers. Furthermore, most current research uses multistage techniques to forecast illness development and focuses only on classifying brain diseases, ignoring the regression problem. Using the most samples possible inside a k-nearest neighbour, random forest and multi layer perceptron (KNN-RF-MLP) architecture, we provide a unique framework to concurrently solve classification and clinical score regression problems by combining multimodal neuroimaging and genetic data. To impute missing features in partial data, we first apply linear interpolation in our technique. We next employ KNN to extract neuroimaging features from SNPs, MRI, and PET data in order to reduce the variation in phenotypic and genotype data. © 2024 IEEE.

关键词： Positron emission tomography

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Next-Gen Telehealth Ecosystems Using IoT for Virtual Consultations and Support 10

Next-Gen Telehealth Ecosystems Using IoT for Virtual Consult...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Kathija Nasreen, A. Priya, S. Gupta, Nukala Sujata Shoba, L.K. Mohankumar, N. Arunkumar, S. Rmk College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Deemed To Be University Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Chaitanya Bharathi Institute of Technology Department of Computer Engineering and Technology Telangana Hyderabad India Srm Institute of Science and Technology Department of Computing Technologies Chenagalpattu Chennai Tamil Nadu Kattankulathur India Symbiosis Institute of Technology Maharashtra Nagpur India Adhiparasakthi Engineering College Department of Computer Applications Tamil Nadu Melmaruvathur India

ISBN: (纸本)9798350353068

The research discussed focuses on improving virtual medical consultations and support using Internet of Things (IoT) technologies in telehealth services. It is predicted that IoT devices can assist with creating a new telehealth environment where patients and doctors can communicate easily for remote monitoring, diagnosis, and treatment. The main objective of this unique approach is to expand access to healthcare, especially in poor and rural areas. Connected medical equipment, smart sensors, and wearable technology all play an important role in this system. These IoT components enable gathering real-time and complete patient data, helping doctors make better, more individualized choices and care plans. Data safety, privacy, and system compatibility are some areas where this integration hits a snag. Sensitive patient data and the integrity of the IoT-enabled systems need robust safety measures. These IoT improvements in telemedicine have had a revolutionary effect. Proactive interventions, continuous health monitoring, and individualized care are ways patients might benefit from improved medical care. Virtual consultations are increasingly used in the medical field, and a more connected and data-driven framework for the patient experience is emerging. © 2024 IEEE.

关键词： Internet of things

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The Use of 6G Communication Technology in Healthcare applications for the Accurate and Better Transmission 4

The Use of 6G Communication Technology in Healthcare Applica...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Omkar, Jamvant Menaka, S. Praveena, J. Varshney, Neeraj Arunkumar, B. Reddy, G. Vinoda Ies College of Technology Department of Electronics & Communication Engineering M. P Bhopal India Nehru Institute of Information Technology and Management Coimbatore India Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Hummanities TN India Gla University Department of Computer Engineering and Applications Mathura India Karpagam Academy of Higher Education Computer Science Engineering Department India Hyderabad Medchal India

ISBN: (纸本)9798350360165

The IIoT is the most important element in the 6th generation networks, or 6G, so it greatly depends on effective data processing and storage. For the cause of ensuring smooth node-to-node communication, 6G provides ultra-low delays, high bandwidth, and higher-level QoS. Intelligent diagnosis, numerous in-hospital, and distant healthcare services are prerequisites to enable healthcare institutions to provide patientcentered care. What is essential is that the system hosting an array of uses, ranging from ambient appropriate living settings with wearable technology-enabled remote patient monitoring to robotic procedures powered by an intelligent networking platform. Therefore, this work addresses the needs of the healthcare sector by providing a reference-layered contact architecture for locations and devices in real-time communication. The unique 6G technology can, therefore, be utilized with great foresight to the future in the healthcare sector, enabling effective treatment and intelligent service integration. Because several networking nodes, including terminal devices, are integrated into effective health service delivery, a backbone structure is thus required for the services that take much time to complete. Therefore, a backbone structure for managing services is required to enable smooth communication by the framework, developed to enable this kind of 6G-enabled advanced healthcare application. © 2024 IEEE.

关键词： Remote patient monitoring

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Leveraging Internet of Things (IoT) Sensors and Deep Learning Techniques for Precision Agriculture 1

Leveraging Internet of Things (IoT) Sensors and Deep Learnin...

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1st International Conference on Innovative engineering sciences and Technological Research, ICIESTR 2024

作者： Wilfred Blessing, N.R. Pyingkodi, M. Wasim Haidar, S.K. Palarimath, Suresh Paravathavarthini, S. Balakumar, C. Sheeja Kumari, V. Sutherlin Subitha, G. Seldev Christopher, S. University of Technology and Applied Sciences-Ibri College of Computing and Information Sciences Ibri Oman Kongu Engineering College Department of Computer Applications Erode India University of Technology and Applied Sciences-Salalah College of Computing and Information Sciences Salalah Oman Kongu Engineering College Department of Computer Technology Erode India Karpagam Academy of Higher Education Department of Computer Science Coimbatore India Saveetha Institute of Medical and Technical Sciences Department of Computational Intelligence Chennai India Stella Mary's College of Engineering Department of Computer Science and Engineering Aruthenganvilai India St. Xavier's Catholic College of Engineering Department of Computer Science and Engineering Nagercoil India

ISBN: (纸本)9798350348637

Precision agriculture, driven by advancements in technology, aims to optimize farming practices by utilizing data and technology to enhance efficiency, productivity, and sustainability This research introduces an innovative pilot investigation centered on the convergence of two revolutionary technologies are the Internet of Things (IoT) and Deep Learning. With a specific focus on advancing precision agriculture, the primary objective of this study is to evaluate the combined effects of data collection facilitated by IoT and analytical capabilities of deep learning in enhancing and optimizing various agricultural processes. The integration of IoT in agriculture has revolutionized data acquisition, employing an array of sensors to monitor critical parameters such as soil moisture, temperature, and crop health. Concurrently, Deep Learning, a subset of artificial intelligence, exhibits the potential to glean actionable insights from voluminous datasets, offering advanced analytics and predictive capabilities. This study investigates the practical implementation and efficacy of this integration in a controlled agricultural setting. Sensors strategically positioned in the pilot study capture real-time data, while deep learning algorithms process and analyze this information. The primary objectives include evaluating the effectiveness of this combined technology in optimizing irrigation schedules, predicting crop yields, and identifying anomalies in crop health. Preliminary findings underscore the transformative potential of IoT and Deep Learning, empowering farmers with real-time data for informed decision-making. Key considerations encompassed in the study include IoT Sensors, Deep learning algorithms, and user adoption. The research not only sheds light on the technical intricacies of the integration but also delves into the challenges and opportunities inherent in merging these technologies within the agricultural landscape. As agriculture transitions towards the next

关键词： Smart agriculture

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Digital Forensics and the Law: A computer engineering Perspective 15

Digital Forensics and the Law: A Computer Engineering Perspe...

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

作者： Agarwal, Gaurav Bhatt, Rahul Pathak, Paresh Kumar, Ashwani Kumar, Anup Pandey, Sakshi Invertis University Department of Computer Science & Engineering Uttar Pradesh Bareilly243123 India Dev Bhoomi Uttarakhand University School of Engineering and Computer Uttarakhand 248007 India IIMT University School of COMPUTER Science and Applications Meerut India K. R. Mangalam University School of Engineering & Technology Department of Computer Science & Engineering Haryana Gurugram122103 India ARKA JAIN University Department of Engineering & IT Jharkhand Jamshedpur India Chitkara University Institute of Engineering and Technology Chitkara University Centre for Interdisciplinary Research in Business and Technology Punjab India

ISBN: (纸本)9798350370249

Digital Forensics and the law is an interdisciplinary subject that studies the legal implications of digital evidence in the context of legal guidelines, rules governing investigations, and data-driven tactics for preventing, detecting, and remediating crimes. It explores how the use of virtual forensics techniques impacts the legal manner in crook and civil instances and the expert obligation of those chargeable for using these strategies. The scope of virtual Forensics and the regulation additionally covers the felony implications of the generation-aided discovery system utilized in pursuing investigations into online activity and the capacity to find evidence for which there's no bodily trace or era platform to get admission. Ultimately, the implications of municipal, state, and federal laws governing digital Forensics are also examined. © 2024 IEEE.

关键词： Impacts Municipal Remediating Techniques Utilized

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AI Based Security for Internet of Transportation Systems 4

AI Based Security for Internet of Transportation Systems

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4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Kousar, Heena Fatima, Shaista Ahmed, Syed Imran Sajithra, S. Kushwaha, Sumit Balaji, N. Alangudi East Point College of Engineering and Technology Department of Computer Science and Engineering Karnataka Bengaluru560049 India Mj College Department of Electronics and Communication Engineering Telangana Hyderabad500053 India R.M.K. Engineering College Department of Computer Science Engineering Tamilnadu Kavaraipettai601206 India University Institute of Computing Chandigarh University Department of Computer Applications Punjab 140413 India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram522302 India

ISBN: (纸本)9798350300888

The intelligent transportation systems (ITS) use a combination of sensors, cameras, and communication technologies to provide real-time data on traffic conditions. This data can be used to adjust traffic lights, but it can also be used to provide information to drivers, such as real-time traffic updates and recommended routes. Overall, control of traffic lights is an effective solution to address traffic congestion problems in urban areas. By optimizing traffic flow, cities can reduce travel times, improve safety, and reduce emissions from idling vehicles. However, the success of these systems depend on the effective planning, implementation, and ongoing maintenance to ensure that they continue to meet the changing needs of the city. In this work, the interest is to control of traffic lights in order to solve problems related to congestion;in this sense, it is proposed five mechanisms to make traffic lights intelligent and not automatic based on machine learning. The mechanisms implemented are based on four algorithms of machine learning: decision tree (DT), support vector machine (SVM), k nearest neighbor (KNN) and random forest (RF). The solutions obtained make it possible to predict the decision in order to act in traffic depending on several parameters such as the road, cars, passengers etc. © 2023 IEEE.

关键词： Traffic congestion

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Security of Cloud Computing Using Quantum Zero-Knowledge Proof System 1st

Security of Cloud Computing Using Quantum Zero-Knowledge Pro...

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1st International Conference of IoT and its applications, ICIA2020

作者： Kumar, Surya Bhushan Mandal, Ranjan Kumar Mukherjee, Kuntal Dwivedi, Rajiv Kumar Department of Computer Applications VBU University Jharkhand Hazaribag India Department of Computer Science and Engineering Ranchi University Jharkhand Ranchi India Department of Computer Science and Engineering BIT Mesra Jharkhand Ranchi India

ISBN: (纸本)9789811676369

Security and privacy play a significant role while using cloud computing. Users can use cloud services, if and only if they find it trust worthy. Users can outsource their sensitive data to cloud environment provided they fill that their data is secured at cloud environment. The popularity of cloud computing depends upon how it can provide the security and privacy of users’ valuable data. Thus, a secured cloud computing is the basic agenda prior to adoption of cloud computing. It is doubtless that the security of cloud computing can only be achieved provided by users of it is the authentic one. In this context, this endeavor has proposed the unique mechanisms to verify the authenticity of the users of the cloud. Apart from this trivial concept of verifying the authenticity, the Zero-Knowledge Proof concept has been introduced here. Furthermore, the concept of quantum mechanics has been incorporated here to provide Zero-Knowledge Proof Engine (ZKE) more effective. Thus, a quantum-based ZKE framework along with algorithm is the main concept of this manuscript. An experimental result has been shown to lay bare the effectiveness of the proposed algorithm. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cloud security

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Segmentation of Retinal Pigment Epithelium Layer in Retinal OCT Images 5

Segmentation of Retinal Pigment Epithelium Layer in Retinal ...

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5th IEEE International Conference on Advances in Electronics, computers and Communications, ICAECC 2023

作者： Sheeba, T.M. Antony Raj, S. Albert Anand, M. SRM Institute of Science and Technology Kattankulathur Faculty of Science and Humanities Department of Computer Applications Chennai India SRM Institute of Science and Technology Kattankulathur Faculty of Engineering and Technology Department of Networking and Communications Chennai India

ISBN: (纸本)9798350305449

Optical Coherence Tomography (OCT) is a relatively recent procedure for studying eyes that has proven to be quite beneficial. We propose automatic detection of the RPE (Retinal Pigment Epithelium) layer in Retinal OCT images in this work. The bottom layer of the retina in retinal OCT images is known as the RPE layer, which is extracted in this study and may be studied to diagnose retinal disorders. After effectively increasing the quality of the OCT image using the Wiener filter, we extract the Retinal Pigment Epithelium layer using the Threshold approach. The proposed approach is tested using 40 OCT images of the retina. The results demonstrate 95.50 percent accuracy in RPE layer segmentation. © 2023 IEEE.

关键词： Optical tomography

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