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

作者： Prabakaran, T. babu, A. Ramesh Hemalatha, S. Ramasamy, Jayabharathi Kovarasan, Rajesh Kambattan Elango, b. Joginpally B.R. Engineering College Department of Computer Science and Engineering Telangana Hyderabad India Vidya Vikas Institute of Engineering and Technology School of Computer Applications Karnataka Mysore India Tagore Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Rmk College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Adhiparasakthi Engineering College Department of Electronics and Communication Engineering Tamil Nadu Melmaruvathur India

ISBN: (纸本)9798350353068

Recent innovations at the convergence of genomics and the Internet of Things (IoT) have opened the way for an innovative strategy for administering individualized health care. This research study combines the potential of wearable health monitoring technologies with genetic insights to analyze the personal healthcare. A comprehensive knowledge of health dynamics may be achieved by seamlessly integrating real-time health data collected from IoT-enabled wearables with individual genetic profiles. Using algorithms for machine learning, connections between genetic differences and instantaneous health responses may be uncovered, paving the way for the development of individualized therapies and lifestyle recommendations. The suggested framework allows people to improve their health and gives medical professionals access to data-driven insights that can be used for the early diagnosis of diseases and the development of individualized treatment plans. The work that has been done sheds light on the revolutionary potential of merging genetics with the Internet of Things to establish a new paradigm of preventative and individualized medical treatment. © 2024 IEEE.

关键词： Genome

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Unified dimensionality reduction techniques in chronic liver disease detection

arXiv

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arXiv 2024年

作者： Karna, Anand Khan, Naina Rauniyar, Rahul Shambharkar, Prashant Giridhar B. Tech Student Computer Engineering Department Delhi Technological University India Computer Engineering Department Delhi Technological University India

Globally, chronic liver disease continues to be a major health concern that requires precise predictive models for prompt detection and treatment. Using the Indian Liver Patient Dataset (ILPD) from the University of California at Irvine’s UCI Machine Learning Repository, a number of machine learning algorithms are investigated in this study. The main focus of our research is this dataset, which includes the medical records of 583 patients, 416 of whom have been diagnosed with liver disease and 167 of whom have not. There are several aspects to this work, including feature extraction and dimensionality reduction methods like Linear Discriminant Analysis (LDA), Factor Analysis (FA), t-distributed Stochastic Neighbour Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP). The purpose of the study is to investigate how well these approaches work for converting high-dimensional datasets and improving prediction accuracy. To assess the prediction ability of the improved models, a number of classification methods were used, such as Multi-layer Perceptron, Random Forest, K-nearest neighbours, and Logistic Regression. Remarkably, the improved models performed admirably, with Random Forest having the highest accuracy of 98.31% in 10-fold cross-validation and 95.79% in train-test split evaluation. Findings offer important new perspectives on the choice and use of customized feature extraction and dimensionality reduction methods, which improve predictive models for patients with chronic liver disease. © 2024, CC bY.

关键词： Dimensionality reduction

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A Secure Data Encryption Mechanism in Cloud Using Elliptic Curve Cryptography 2

A Secure Data Encryption Mechanism in Cloud Using Elliptic C...

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2nd IEEE International Conference on Intelligent and Innovative technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Sudha, I. Donald, Cecil Navya, S. Nithya, G. balamurugan, Manivannan Saravanan, S. Saveetha Institute of Medical and Technical Sciences - Simats Department of Computer Science and Engineering Saveetha School of Engineering Chennai India Department of Computer Science Bengaluru India Raghu Engineering College Department of Computer Science Engineering Visakhapatnam India Department of Cse V. S. B College of Engineering Technical Campus Coimbatore India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Mathematics Tamil Nadu Avadi India

ISBN: (纸本)9798350306415

Cloud computing is undergoing continuous evolution and is widely regarded as the next generation architecture for computing. Cloud computing technology allows users to store their data and applications on a remote server infrastructure known as the cloud. Cloud service providers, such Amazon, Rackspace, VMware, iCloud, Dropbox, Google's Application, and Microsoft Azure, provide customers the opportunity to create and deploy their own applications inside a cloud-based environment. These providers also grant users the ability to access and use these applications from any location worldwide. The subject of security poses significant challenges in contemporary times. The primary objective of cloud security is to establish a sense of confidence between cloud service providers and data owners inside the cloud environment. The cloud service provider is responsible for ensuring user data's security and integrity. Therefore, the use of several encryption techniques may effectively ensure cloud security. Data encryption is a commonly used procedure utilised to ensure the security of data. This study analyses the Elliptic Curve Cryptography method, focusing on its implementation in the context of encryption and digital signature processes. The objective is to enhance the security of cloud applications. Elliptic curve cryptography is a very effective and robust encryption system due to its ability to provide reduced key sizes, decreased CPU time requirements, and lower memory utilisation. © 2024 IEEE.

关键词： Cloud computing cryptography data security ECC encryption

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An IoT and Random Forest-DT-based Early Enhanced Forest Fire Detection in Large and Small Targets 3

An IoT and Random Forest-DT-Based Early Enhanced Forest Fire...

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3rd International Conference for Innovation in technology, INOCON 2024

作者： Prabakaran, Manjula Wadhwani, Omesh Pandiaraj, V. Zainab, Kaneez Nishant, Neerav Justin, Z. Andhra Pradesh India Shri Ramdeobaba College of Engineering and Management Department of Information Technology Nagpur India Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Mechanical Engineering Chennai India B.N. College of Engineering and Technology Department of Computer Science & Engineering Lucknow India Babu Banarasi das University School of Engineering Department of CSE Lucknow India Thiruvalluvar India

ISBN: (纸本)9798350381931

The world is confronted with more complex and severe environmental concerns than in the past. Extreme weather events have become more common and destructive in the last several decades, endangering forests and urban forests around the globe. Massive forest fires are among the most catastrophic natural catastrophes that affect Earth's climate and the diversity of life on it. Thus, in order to minimize harmful impacts on ecosystems and people, it is vital to have well-planned and coordinated plans for early warning, prevention, and reaction. Image preprocessing, feature extraction, and training the model are the initial steps in the suggested methodology. Histogram matching and picture smoothing are part of the image preparation procedure. The gray histogram is a way to see how many pixels fall into each grayscale level, and image smoothing is a way to enhance a large region of a picture using image processing. The GLCM and LbP histograms are employed in the feature extraction process. Training RF-Multivariate ARS models requires feature extraction as a prerequisite. The system improved accuracy to 97.29 percent after applying the approach. © 2024 IEEE.

关键词： Feature extraction

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The NavINST Dataset for Multi-Sensor Autonomous Navigation

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IEEE Access 2025年 13卷 84659-84674页

作者： De Araujo, Paulo Ricardo Marques Mounier, Eslam bader, Qamar Dawson, Emma Kaoud Abdelaziz, Shaza I. Zekry, Ahmed Elhabiby, Mohamed Noureldin, Aboelmagd Queen’s University Department of Electrical and Computer Engineering KingstonONK7L 3N6 Canada Ain Shams University Cairo11566 Egypt Micro Engineering Tech Inc. CalgaryABT2M 0L7 Canada Royal Military College of Canada Department of Electrical and Computer Engineering KingstonONK7K 7B4 Canada

The Navigation and Instrumentation (NavINST) laboratory has developed a comprehensive multisensory dataset from various road-test trajectories in urban environments, featuring diverse lighting conditions, including indoor garage scenarios with dense 3D maps. This dataset includes multiple commercial-grade IMUs and a high-end tactical-grade IMU. Additionally, it contains a wide array of perception-based sensors, such as a solid-state LiDAR—making it one of the first datasets to do so—a mechanical LiDAR, four electronically scanning RADARs, a monocular camera, and two stereo cameras. The dataset also includes forward speed measurements derived from the vehicle’s odometer, along with accurately post-processed high-end GNSS/IMU data, providing precise ground truth positioning and navigation information. The NavINST dataset is designed to support advanced research in high-precision positioning, navigation, mapping, computer vision, and multisensory fusion. It offers rich, multi-sensor data ideal for developing and validating robust algorithms for autonomous vehicles. Finally, it is fully integrated with the robot operating system (ROS), ensuring ease of use and accessibility for the research community. The complete dataset and development tools are available at (***). © 2013 IEEE.

关键词： Robot Operating System

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Enhancing Security in IoT Networks Through RDAD for Attack Detection in RPL-Enabled Environments

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SN computer Science 2024年第7期5卷 864页

作者： Sridhar, K. Kumar, b. Anjanee Devi, S. Anjali Raju, Vadali Pitchi Soni, Anita Singh, Pallavi Deore, Shailesh Shivaji Department of Mechanical Engineering Lendi Institute of Engineering and Technology Andhra Pradesh Vizianagaram India Department of Electronic and Communication Engineering Dadi Institute of Engineering and Technology Gavarapalem Andhra Pradesh Anakapalle India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur India Indur Institute of Engg. & amp Tech Siddipet India IES University Bhopal India Department of ECE Hindustan Institute of Technology and Science TN Chennai India Department of Computer Engineering SSVPS B S DEORE College of Engineering Maharashtra Dhule India

The “Internet of Things” (IoT) revolutionizes technology integration, enabling interconnected devices to communicate both among themselves and with the online realm. These smart gadgets collect valuable data, facilitating remote sharing via networked connections. Yet, their constrained memory, power, and computational capacities pose challenges. Within IoT, devices vary in functionality, from fully capable to limited subsets. These communication methods are integral to IoT's existence. Security is paramount when deploying IoT applications due to the inevitability of routing attacks aiming to intercept data in transit. To thwart such unauthorized access, Rank Decreased Attack Detection (RDAD) emerges as a crucial solution. Developed with IoT in mind, the Routing Protocol for Low-Power and Lossy Networks (RPL) serves as an anticipatory routing protocol. In an IoT environment, routing flaws can obscure data, thus highlighting the significance of RPL's topology-building process, which prioritizes nodes based on a ranking measure. RDAD proposes a mechanism that considers a parent node's Packet Delivery Ratio (PDR) to select trustworthy routing paths for data packets. Comparative analysis reveals RDAD's superiority over existing detection methods, significantly enhancing packet delivery rates and minimizing losses. With a detection rate of nearly 96.5% for RDA attacks, RDAD emerges as a potent safeguard for IoT routing security. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： INTI IoT PDR RDAD RPL

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Data-driven visual analytics of Human Mobility data and green cover using Image Processing for Smart Cities 3

Data-driven visual analytics of Human Mobility data and gree...

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3rd IEEE International Conference on Intelligent technologies, CONIT 2023

作者： Ramanathan, Sivasubramanian Syed, Kulsoom Chavan, Tejaswini B. Tech Computer Science & Business Systems SVKMs NMIMS Navi Mumbai Mumbai India School of Technology Management & Engineering SVKMs NMIMS Navi Mumbai Mumbai India

ISBN: (纸本)9798350338607

This research paper presents an approach to data-driven visual analytics of human mobility data using Kernel Density Estimation visualized through heatmaps, highlighting the need for exploration of forecasting methods and intuitive visualizations using the ARIMA model. A specific geographic area is chosen for the demonstration of the scope of the proposed system. The system is a web application developed using Streamlit, an open-source python framework. To effectively implement the smart city concept, it is crucial to integrate diverse IoT systems, data sources, data streams, and analytical tools into a unified and seamless platform that facilitates the collection, analysis and presentation of information related to urban systems and subsystems. We propose taking into consideration several attributes when analyzing human mobility patterns, such as vehicle/traffic density, modes of transport, transport data, demographics, latitude, and longitude. Additionally, the system utilizes image processing as an efficient method for calculating urban green cover using morphological operations that are computationally cheaper and easy to use as compared to traditional surveys that are time and resource intensive. This information is used to develop smart plans to sustain or increase green cover in select areas, leading to the creation of sustainable and green smart cities. © 2023 IEEE.

关键词： Smart city

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An electronic warfare approach for deploying a software-based Wi-Fi jammer 5

An electronic warfare approach for deploying a software-base...

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5th International Conference on Contemporary Computing and Informatics, IC3I 2022

作者： Kaushik, Keshav Negi, Rahul Dev, Prabhav University of Petroleum and Energy Studies School of Computer Science Dehradun India University of Petroleum and Energy Studies B.Tech Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350398267

Some prominent instances have been centered on electronic warfare. For example, the American military has made significant investments in automation through UAV programs, only for competitors like the Iranians to create strategies to interfere with these systems. Iran managed to capture a top-secret U.S. surveil-lance drone by fooling it into descending in the incorrect place by jamming its control signals and providing it with bogus GPS data. In this paper, the authors have focused on the electronic warfare approach for deploying a software-based Wi-Fi jammer. The software-based Wi-Fi jammer can disconnect the targets using the DoS pursuit mode. The paper describes the unique methodology of how software can also be used for jamming wireless signals. © 2022 IEEE.

关键词： Wi-Fi

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Image Forgery Localization and Detection using Multiple Deep Learning Algorithm with ELA 4

Image Forgery Localization and Detection using Multiple Deep...

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International Conference on 4th Industrial Revolution based technology and Practices, ICFIRTP 2022

作者： Singh, Anshul Kumar Sharma, Chandani Kumar Singh, brajesh Quantum University Department of Computer Science and Engineering Roorkee India R. B. S. Engg. Tech. Campus Department of Computer Science and Engineering Agra India

ISBN: (纸本)9798350345919

In today's technical world, social media may help an individual grow significantly. On the other side, we must not overlook the reality that it is also a large platform for criticism. With recent advancements, approaches for creating and manipulating multimedia information may now deliver a highly sophisticated level of realism. There has been a blurring of the line between real media and fake media in recent years. Creative arts, film production, advertising, and video gaming are among the industries that could benefit from this technology. However, it poses significant security risks. Software tools freely accessible on the internet enable anybody, with no particular abilities, to generate extremely convincing phony images and films. A dataset composed of real photographs and false images is used in this article to identify images of modifications using Deep Learning algorithms with Error Level Analysis on each image. Our experiment yielded the accuracies of 93.5%, 89.1 and 92.4% in ResNet50, Vgg16 and CNN respectively for 50 epochs. © 2022 IEEE.

关键词： CNN ELA Image Forgery Real vs. Fake detection ResNet50 Vgg16

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IoT Enabled Anti-Child Trafficking System Using Data Analysis

IoT Enabled Anti-Child Trafficking System Using Data Analysi...

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Communication, Computing and Signal Processing (IICCCS), IEEE International Conference on

作者： Rittika Paul Zarin Tasnim Mim Alex Mathew Roy Piyush Chauhan Dewan Ahanaf Tahsen B-Tech of Computer Science and Engineering-IoT Jain (Deemed-to-be University) Bangalore India Computer Science and Engineering-IoT Jain (Deemed-to-be University) Bangalore India

ISBN: (数字)9798350390759

ISBN: (纸本)9798350390766

The increasing prevalence of child trafficking in the South particularly in bangladesh, India, and Nepal requires a creative solution that can go beyond the orthodox preventative techniques. This continuously growing issue mainly violates the fundamental rights of children, for example liberty, education, social support, and economic opportunities. Trafficked children must go through physical and sexual abuse, which turns into slavery, forced labor, and committing crimes like begging, theft, and delivering drugs. To create a definitive system that targets detecting and response for stopping child abduction and trafficking, this research provides an anti-child trafficking device that is embedded with different types of sensors and location trackers. These sensors are used to collect accurate data or information for real-time child monitoring. The collected data can be used for detecting potential trafficking routes and predicting future circumstances. Using these technologies, it is possible to create a protective boundary for children. This research paper presents a model of combined sensors of our proposed wearable device. It can be considered a resolver in the prevention of child trafficking and abduction, by providing any alert or messages to the parents now.

关键词： Data analysis Accuracy Target tracking Web services Signal processing Real-time systems Telecommunication computing Wearable devices Monitoring Wearable sensors

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