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Design of an Efficient Model for Enhanced IoT Security and Maintenance in Smart Cities 1

Design of an Efficient Model for Enhanced IoT Security and M...

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1st International Conference on Technological Innovations and Advance Computing, TIACOMP 2024

作者： Bhandari, Saurav Chaudhary, Sumit Vaidya, Himadri Kumar, Ujjwal Kirola, Madhu Anandaram, Harishchander Uttaranchal Institute of Technology Uttaranchal University Dept. of Computer Science and Engineering Dehradun India Graphic Era Hill University Graphic Era Deemed to Be University Dept. of Computer Science and Engineering Dehradun India Jb Institute of Technology Dept. of Mechanical Engineering Dehradun India Amrita Vishwa Vidyapeetham Amrita School of Artificial Intelligence Coimbatore India

ISBN: (纸本)9798350392111

It is of the utmost importance to ensure robust security and effective device maintenance in the rapidly changing landscape of smart city infrastructures. Be that as it may, existing procedures frequently miss the mark in tending to the dynamic and different difficulties presented by emerging dangers and the perplexing exchange of IoT environments. This paper presents a clever way to deal with these issues through three inventive strategies: Adaptive Reinforcement Learning for IoT Security (ARLIS), Graph-Analytics-Based Anomaly Detection (GABAD), and Predictive Maintenance using Time Series Forecasting (PMTSF). Customary safety efforts frequently battle to adjust to the advancing danger scene, prompting weaknesses and likely breaks. ARLIS tends to this constraint by loading the force of support figuring out how to progressively change security conventions in view of ongoing IoT information and organization conditions. ARLIS continuously learns from feedback to improve security measures, which reduces response times to security threats and improves overall security efficacy. Also, irregularity location in IoT networks is tested by the sheer volume and complexity of information produced by interconnected gadgets. GABAD beats this obstacle by utilizing diagram examination to recognize oddities in device connections and ways of behaving inside smart city organizations. By building an exhaustive chart model and applying progressed oddity identification calculations, GABAD accomplishes unequaled precision in peculiarity location while limiting misleading up-sides. In addition, optimizing system reliability and minimizing service disruptions require proactive maintenance of IoT devices. PMTSF alters upkeep methodologies by utilizing time series to foresee gadget disappointments before they happen. By examining gadget execution measurements and ecological variables, PMTSF empowers convenient support mediations, prompting a significant decrease in spontaneous margin time and

关键词： Predictive maintenance

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A Review of Design and Implementation of computer Based Census Management System 2

A Review of Design and Implementation of Computer Based Cens...

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2nd International Conference on Multidisciplinary engineering and Applied Science, ICMEAS 2023

作者： Bashir, Umar Mohammed Nyangwarimam, Ali Obadiah Thomas, Sadiq Odimba, Chinomso Roselyn Lawal, Shariff Aboaba, Adulfatah A. Elijah, Olakunle Nile University of Nigeria Dept. of Computer Engineering Abuja Nigeria Nile University of Nigeria Dept. of Industrial Chemistry Abuja Nigeria Airforce Institute of Technology Dept. of Mechanical Engineering Kaduna Nigeria University of Maiduguri Dept. of Computer Engineering Borno Nigeria

ISBN: (纸本)9798350358834

This Paper Presents a computer-based census management system which is a software application designed to collect, store, manage, and analyze census data electronically. It replaces traditional paper-based methods of data collection and processing, making the census process more efficient, accurate, and secure. The system typically consists of several components that work together to facilitate the census data management process. © 2023 IEEE.

关键词： Information management

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Efficient Diagnosis of Coronary Artery Disease: A Hybrid Convolutional-XGBoost Approach 2

Efficient Diagnosis of Coronary Artery Disease: A Hybrid Con...

引用

2nd International Conference on Ambient Intelligence in Health Care, ICAIHC 2023

作者： Mishra, Arkashree P. Panigrahi, Suvasini Veer Surendra Sai University of Technology Dept. of Computer Science & Engineering Burla India

ISBN: (纸本)9798350344882

Cardio Vascular Diseases (CVDs) are the prime reason of death globally. Particularly, the cases of coronary artery disease (CAD) have been rising in the post Covid era, especially amongst middle-aged people. Thus, the before time detection of the disease might evade the growing number of fatalities and morbidities. Two most common and accurate methods used to detect CAD are angiography and electrocardiography (ECG). While the former is invasive and a risky procedure, the latter one although non-invasive lacks correctness. Over a decade, non-invasive Machine Learning (ML) as well as Deep Learning (DL) methods have shaped the healthcare scenario by facilitating early diagnosis of the diseases through the application of various computer based algorithmic models over clinical data and images. A hybrid Convolutional- Extreme Gradient Boosting (Convolutional XGBoost) model has been proposed to accurately detect CAD by outperforming some of the existing methods. © 2023 IEEE.

关键词： Diseases

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Convolutional Neural Networks (CNN) and Facial Features based Smart Driver Alertness Detection 7

Convolutional Neural Networks (CNN) and Facial Features base...

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7th International Conference on Trends in Electronics and informatics, ICOEI 2023

作者： Kumar, K.V Pavan Chidanand, M. Shabu, S.L.Jany Grace, L.K.Joshila Poornima, D. Sathyabama Institute of Science and Technology Dept. of Computer Science and Engineering Chennai600119 India

ISBN: (纸本)9798350397284

The development of machine learning assists drivers in the form of unmanned automobiles etc. Nearly 22% of accidents are caused by the driver's drowsiness and lack of alertness. This study intends to monitor the alertness of driver by analyzing their facial expressions and eye variations. The developed smart processing system substantially reduces highway accidents. The proposed model considers various facial characteristics like PERCLOS, Eye Aspect Ratio (EAR), blink values, and yawning. The driver is continually observed by the camera in the proposed method. HC classifiers are used to identify the driver's face. CNN uses the extracted eye images to determine whether the eyes are closed. Based on the classification results, EAR is measured. If the mouth is detected to be open, the lip distance (yawn) is measured. Finally, a warning will be generated if the driver is in a drowsy state. © 2023 IEEE.

关键词： Aspect ratio

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CrimeVerse: Exploring Crime Scene through Virtual Reality 6

CrimeVerse: Exploring Crime Scene through Virtual Reality

引用

6th International Conference on Inventive Computation Technologies, ICICT 2023

作者： Deshmukh, Jay Shetty, Srishti Waingankar, Mrunmayee Mahajan, Gauri Joseph, Richard Dept. of Computer Engineering Vivekanand Education Society's Institute of Technology Mumbai India

ISBN: (纸本)9798350398496

Modern technologies are applied in order to enhance the caliber of crime investigations. The documentation of crime scenes plays a crucial role in the investigation and prosecution of criminal cases. The challenges faced by traditional methods of crime scene documentation such as photographs, sketches, and written notes have their limitations in terms of accuracy, completeness, limited accessibility, and the risk of losing crucial evidence. The precise scene recording offered by 3D data capture techniques is free from the spatial distortion effects present in 2D photographic recordings. This research study explores the use of LIDAR scanning and virtual reality in enhancing crime scene documentation. The paper focuses on evaluating the effectiveness of LIDAR scanning and VR walkthroughs and also focuses on evidence tagging for enhancing crime scene documentation. It also helps in investigating the usability of the VR system for law enforcement professionals. The proposed model faces several challenges during its development, including the high cost and technical complexity of LIDAR scanning, the need for efficient data processing and visualization, and the limited accessibility of the VR system due to hardware and software requirements. Through user testing and analysis, the project demonstrated significant improvements in the accuracy and completeness of crime scene documentation, providing a more detailed and immersive experience for investigators. The VR system was found to be user-friendly and effective in aiding decision-making and communication among law enforcement professionals. Overall, the project provides valuable insights which let you see through the eyes of a witness by documenting their perspective, which aids in the investigation and comprehension of the incident. They can also be used as proof in court. © 2023 IEEE.

关键词： Object detection

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Plagiarism Detection and Similarity Checking Program using Machine Learning and String Matching Algorithm 9

Plagiarism Detection and Similarity Checking Program using M...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Bhavana, Munugapati Rao, Koppula Srinivas Koduru, Gagan Kumar Vatsal, K.Vijay Krupa Parvathi, S. Sravani, Mogiligidda Mlr Institute of Technology Affiliated to Jntu Dept. of Computer Science & Information Technology Hyderabad India Mlr Institute of Technology Affiliated to Jntu Department of Computer Science & Engineering Hyderabad India Mlr Institute of Technology Hyderabad India Malla Reddy University Department of Computer Science & Engineering Hyderabad India Mlr Institute of Technology Dept.of Computer Science & Information Technology Hyderabad India Mvsr Engineering College Department of Information Technology India

ISBN: (纸本)9798350377972

This research proposes a system for detecting plagiarism in academic submissions. The system utilizes advanced text comparison algorithms to identify instances of plagiarism, ensuring academic integrity and promoting a culture of originality among students. By providing educators with a tool to efficiently detect plagiarism, this system aims to streamline the evaluation process and enhance the overall learning experience for students. This system has the potential to significantly reduce plagiarism incidents and foster a more ethical and productive academic environment. © 2024 IEEE.

关键词： Contrastive Learning

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IoT Intrusion Detection: Evaluating ML-Based IDS on Image and Network Traffic Datasets 9

IoT Intrusion Detection: Evaluating ML-Based IDS on Image an...

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9th IEEE International Symposium on Smart Electronic Systems, iSES 2023

作者： Pathak, Manan Patel, Anuj Shah, Bela Cspit Charotar University of Science and Technology Dept. of Computer Science and Engineering Anand India

ISBN: (纸本)9798350383249

With the increasing pervasiveness of Internet of Things (IoT) devices, ensuring their security has become a critical concern. Advanced intrusion detection systems (IDS) leveraging machine learning (ML) and Deep learning(DL) techniques offer promising solutions for protecting IoT networks. However, the success of ML models depends heavily on preprocessing and feature extraction processes. This research presents a survey on ML-based IDS for IoT, focusing on ML models applied to both image(IEEE Dataport) and UNSW-NB15 datasets. The survey evaluates several feature extraction methods, including image filters and Machine learning models for the image dataset. For the UNSW-NB15 dataset, a range of preprocessing techniques specific to network traffic analysis, such as statistical features and protocol-specific features, are considered. In addition to feature extraction, various ML algorithms are assessed on both datasets. The evaluation is based on accuracy, precision, recall, and F1 score performance metrics, providing a comprehensive analysis of the ML model's effectiveness in detecting intrusions. This research contributes to the field of IoT security by providing insights into the performance of feature extraction algorithms and ML models on both image and network traffic datasets. The findings offer guidance for developing effective IDS solutions that can accurately detect and mitigate intrusions in IoT networks, enhancing the overall security of IoT devices and systems. © 2023 IEEE.

关键词： Internet of things

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3D AttU-NET for Brain Tumor Segmentation with a Novel Loss Function 6

3D AttU-NET for Brain Tumor Segmentation with a Novel Loss F...

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6th International Conference on Information Systems and computer Networks, ISCON 2023

作者： Roy, Rahul Annappa, B. Dodia, Shubham National Institute of Technology Karnataka Surathkal Dept. of Computer Science and Engineering Mangalore India

ISBN: (纸本)9798350346961

In the United States of America (USA), every year 150,000 patients are registered with a secondary brain tumor that is not generated in the brain. This necessitates the need for early brain tumor detection, which in turn will help patients to live longer. For clinical evaluation and treatment, precise segmentation of brain tumors in MRI images is required. This process can be aided by machine learning and efficient image processing, but manual imaging can be time-consuming. In this study, we aim to develop an 3D automated segmentation algorithm with a novel loss function. A 3D attention UNET CNN model was trained using the novel loss function, which was calculated by taking the weighted average of dice loss and focal loss to overcome the class imbalance. Results show the enhancement in the segmentation performance of attention UNET model with an average increase of 5% in the Dice coefficient for all three classes. However, the model's performance was not as strong for enhanced and core tumors. Further research may be needed to optimize performance in these areas.. © 2023 IEEE.

关键词： Magnetic resonance imaging

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Association Rules Mining for a Specific Time Period in a Day in Large Transactional Database 2

Association Rules Mining for a Specific Time Period in a Day...

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2nd International Conference on Information and Communication technology for Sustainable Development, ICICT4SD 2023

作者： Islam, Md. Rabiul Rahman, Mst. Sahela Nayeem, Md. Julker Pundra University of Science and Technology Dept. of Computer Science and Engineering Bogura5800 Bangladesh

ISBN: (纸本)9798350358667

Market basket analysis is a trendy topic in the field of data mining. Analyzing the daily transactions, several types of associations were noticed in the transacted item sets. The transactional database used in retail marketing has a large number of itemsets that are frequently purchased exclusively during specific hours of the day and are not usually considered to be frequently purchased. In some instances, association rule mining at a lower data level with uniform support doesn't reveal any notable patterns, yet it may be suppressing crucial data. For instance, the morning session sees the biggest demand for items like bread and cake, while the afternoon and evening sessions find higher demand for items like drinks and noodles. In the transaction at a supermarket, this study discovered the frequent item and the link among the frequent item sets. In this study, we used the tree-based FP-growth idea to locate the pertinent rules in the transaction based on the time of day, specifically the morning, afternoon, and evening. Our empirical analysis demonstrates that the suggested approach is computationally effective in a different time period of the day, enabling the Bangladeshi retailer Super Shop to make the best decisions and boost sales. © 2023 IEEE.

关键词： Association rules

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Classification of Skin Cancer Images using Lightweight Convolutional Neural Network 4

Classification of Skin Cancer Images using Lightweight Convo...

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4th International Conference for Emerging technology, INCET 2023

作者： Sandeep Kumar, Talapally Annappa, B. Dodia, Shubham National Institute of Technology Karnataka Dept. of Computer Science and Engineering Mangalore Surathkal India

ISBN: (纸本)9798350335750

Skin is the most powerful shield human organ that protects the internal organs of the human body from external attacks. This important organ is attacked by a diverse range of microbes such as viruses, fungi, and bacteria causing a lot of damage to the skin. Apart from these microbes, even dust plays important role in damaging skin. Every year several people in the world are suffering from skin diseases. These skin diseases are contagious and spread very fast. There are varieties of skin diseases. Thus it requires a lot of practice to distinguish the skin disease by the doctor and provide treatment. In order to automate this process several deep learning models are used in recent past years. This paper demonstrates an efficient and lightweight modified SqueezeNet deep learning model on the HAM10000 dataset for skin cancer classification. This model has outperformed state-of-the-art models with fewer parameters. As compared to existing deep learning models, this SqueezeNet variant has achieved 99.7%, 97.7%, and 97.04% as train, validation, and test accuracies respectively using only 0.13 million parameters. © 2023 IEEE.

关键词： Bacteria

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