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Harmonising Heterogeneous data Sources for Comprehensive Forensic Analysis

Harmonising Heterogeneous Data Sources for Comprehensive For...

2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Chandra, Pradeep Kumar Himabindu, Modi Raj, Vijilius Helena Dutt, Amit Kumbha, Koreswar Rao Mahdy, Abdulkareem LLloyd Institute of Engineering & Technology Knowledge Park II Uttar Pradesh Greater Noida201306 India Institute of Aeronautical Engineering Hyderabad Dundigal India New Horizon College of Engineering Department of Applied Sciences Bangalore India Lovely Professional University Phagwara India St. Martin's Engineering College Dept. of It Telangana Secunderabad India National University of Science and Technology College of Pharmacy Dhi Qar64001 Iraq

ISBN: (纸本)9798350373783

New techniques must be devised to integrate data types for extensive and accurate analysis as digital forensics evolves. Five key algorithms are recommended for a full framework in this study. Cross-platform normalization for standardization, semantic integration for common representation, federated learning for collaborative analysis, ontology-driven anomaly detection, and blockchain-enhanced chain of custody. The semantic integration algorithm creates a common vocabulary, maps data sources, and standardizes data first. Future algorithms employ this common representation to handle standardization, joint analysis, anomaly detection, and ownership tracking concerns. The ablation research details what each software undertakes to improve and adapt to real-time changes. The proposed performance evaluation system is more accurate, safer, customizable, and effective than present techniques. Federated learning allows numerous analysts to work together while protecting data, while ontology-driven anomaly detection detects anomalies with data consistency. Blockchain-Enhanced Chain of Custody makes Common Representation more dependable by employing a safe and independent blockchain. Forensic research in evolving data systems presents challenges, which this paradigm addresses. The offered methodologies give specialists additional tools to combine and assess multiple types of data, providing a full and flexible solution for current forensic investigations. © 2024 IEEE.

关键词： Federated learning

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A Deep Learning Application to Detect and Diagnose Metabolic Encephalopathy

A Deep Learning Application to Detect and Diagnose Metabolic...

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Smart Technologies, Communication and Robotics (STCR)

作者： Anjali R Ganesh Babu C Nagarajan N Dept. of Artificial Intelligence and Data Science Coimbatore Institute of Engineering and Technology (Aff.to Anna University ) Coimbatore India Dept. of Electronics and Instrumentation Engineering Bannari Amman Institute of Technology Sathyamangalam India Dept. Electronics and Communication Engineering Coimbatore Institute of Engineering and Technology (Aff.to Anna University ) Coimbatore India

ISBN: (数字)9798350357530

ISBN: (纸本)9798350357547

Brain health plays a major role in human life to recognize their complete prospective over the life course stating the brain functionality which includes sensory, behavioral, cognitive, social-emotional and motor domains regardless of the existence or nonexistence of disorders. If there exists a brain dysfunction due to chemical imbalances in the blood, it is named as Metabolic Encephalopathy. This chemical imbalances occur due to the systematic illness such as diabetes, kidney or liver disease which may disturb mental states and if not treated in right time may lead to permanent brain damage. The role of artificial intelligence in medical field is showing its significant growth in recent years. This document showcases deep learning techniques in the diagnosis of metabolic encephalopathy.

关键词： Training Deep learning Accuracy Systematics Sensitivity Transfer learning Artificial neural networks Artificial intelligence Chemicals Testing

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A Novel Approach for Driver Drowsiness Detection using Deep Neural Networks

A Novel Approach for Driver Drowsiness Detection using Deep ...

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Women in Innovation, Technology & Entrepreneurship (ICWITE), IEEE International Conference for

作者： Sharon K V Natarajan B P. Madhumitha A Dhanamathi Prabu S S Saranya Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India School of Computer Science and Engineering VIT University Chennai Tamilnadu India Dept. of Artificial Intell. and Data Science Panimalar Engineering College Chennai India Department of Computer Science and Engineering Roever Engineering College Perambalur Tamilnadu India Dept. of CSE K. Ramakrishnan College of Engineering Tiruchirappalli Tamil Nadu India

One major problem that contributes to many traffic accidents that cause injuries and fatalities all over the world is drowsy driving. This research introduces the novel approach for detecting driver drowsiness using deep networks and computer vision techniques aims to solve this issue and improve road safety. The Proposed system employs an InceptionV3-based Convolutional Neural Network (CNN) model to precisely determine whether the driver’s eyes are open or closed. A sizable dataset of eye images taken in actual driving situations is used to train the CNN model. Whenever the indicators of drowsiness are detected, the proposed system will identify them in real time and promptly notify the driver. The proposed model not only has strong real-time capabilities but also exhibits superior accuracy based on stringent evaluation metrics, which makes a significant contribution to efforts aimed at promoting road safety. The performance and adaptability of the system are improved and further integrates alarm activation for drowsiness detection in a variety of driving conditions.

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Exploring the Potential of IoT in Healthcare: Challenges and Opportunities

Exploring the Potential of IoT in Healthcare: Challenges and...

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Technological Advancements in Computational sciences (ICTACS), International Conference on

作者： Navneet Verma Sumit Rana Shivani Gaba Shally Nagpal Dept. of Computer Science & Engg. Paniapt Institute of Engineering and Technology Panipat Haryana India Dept. of Artificial Intelligence and Data Science Paniapt Institute of Engineering and Technology Panipat Haryana India

Due to the propagation of IoT technologies Healthcare cannot remain outside of this paradigm. In latest years, there has been a lot of interest in the Internet of Things (IoT) technology to relieve the strain on healthcare systems brought on by the ageing population and the rise in persistent illnesses. One of the main obstacles preventing advancement in this field is standardization or global connectivity between the Internet of Things (IoT) and the medical surroundings. All from electrical engineers to data engineers have a significant problem in having to integrate everything in a global setting. The difficulties that healthcare IoT confronts, such as security, privacy, and less-energy procedures, are discussed, and suggestions are provided for future research paths. This paper is written to gather data on various Internet of Things architectures now used in Healthcare and analyse how well different academics have applied it.

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Phishing URLs Using Machine Learning Hybrid Stacking Classifier Approach with XGBoost, Random Forest and Extra Trees

Phishing URLs Using Machine Learning Hybrid Stacking Classif...

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Information Technology, Electronics and Intelligent Communication Systems (ICITEICS), IEEE International Conference on

作者： Chengamma Chitteti Tanguturi Sai Divya Sree K. Reddy Madhavi Pikkili Pooja Sannapaneni Jayanth Middi Harshavardhan Reddy Dept. Of Data Science Mohan Babu University Erstwhile Sree Vidyanikethan Engineering College(Autonomous) Tirupathi India Dept. of Information Technology Sree Vidyanikethan Engineering College Tirupathi India Dept. Of AI & ML Mohan Babu University Tirupati India

ISBN: (数字)9798350382693

ISBN: (纸本)9798350382709

Within the dynamic field of cybersecurity threats, phishing remains a prevalent and sophisticated tactic employed by malicious actors to deceive consumers. This paper presents a novel approach that leverages the strengths of Random Forest, ExtraTrees, and XGBoost in a machine learning framework to enhance the detection of phishing URLs through a hybrid stacking classifier. The suggested method combines the best features of all three ensemble learning techniques to provide a phishing detection model that is reliable and precise. An ensemble of classifiers is formed by combining XGBoost, which is well-known for its boosting abilities, with the highly randomized ExtraTrees method and the adaptable Random Forest algorithm. By using the various decision limits of each base learner, the hybrid stacking model combines their particular capabilities in the best possible way to provide higher prediction performance. Preprocessing is done on the training dataset to extract pertinent characteristics such as content, URL structure, and contextual data. The efficacy of the suggested method in detecting phishing URLs is assessed by extensive testing on an extensive dataset. The results indicate how well the hybrid stacking classifier performs in comparison to separate classifiers and highlight the possibility for improved phishing detection in practical situations. The study has importance as it presents a novel and efficient machine learning-based approach to counteract the rising danger of phishing assaults, hence propelling the area of cybersecurity forward.

关键词： Uniform resource locators Adaptation models Accuracy Phishing Prevention and mitigation Stacking Malware

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Distributionally Robust Optimization as a Scalable Framework to Characterize Extreme Value Distributions

arXiv

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arXiv 2024年

作者： Kuiper, Patrick Hasan, Ali Yang, Wenhao Ng, Yuting Bidkhori, Hoda Blanchet, Jose Tarokh, Vahid Dept. of Electrical and Computer Engineering Duke University DurhamNC United States Dept. of Management Science and Engineering Stanford University StanfordCA United States Computational and Data Sciences Dept. George Mason University FairfaxVA United States

The goal of this paper is to develop distributionally robust optimization (DRO) estimators, specifically for multidimensional Extreme Value Theory (EVT) statistics. EVT supports using semi-parametric models called max-stable distributions built from spatial Poisson point processes. While powerful, these models are only asymptotically valid for large samples. However, since extreme data is by definition scarce, the potential for model misspecification error is inherent to these applications, thus DRO estimators are natural. In order to mitigate over-conservative estimates while enhancing out-of-sample performance, we study DRO estimators informed by semi-parametric max-stable constraints in the space of point processes. We study both tractable convex formulations for some problems of interest (e.g. CVaR) and more general neural network based estimators. Both approaches are validated using synthetically generated data, recovering prescribed characteristics, and verifying the efficacy of the proposed techniques. Additionally, the proposed method is applied to a real data set of financial returns for comparison to a previous analysis. We established the proposed model as a novel formulation in the multivariate EVT domain, and innovative with respect to performance when compared to relevant alternate proposals. © 2024, CC BY.

关键词： Poisson distribution

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PSDNet: A Breakthrough Parking Space Detection Network Powered by YOLOv8

PSDNet: A Breakthrough Parking Space Detection Network Power...

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Advances in Computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： Prabu Selvam P. Saravanan M. Marimuthu M. R. Nithya V. Sathiyapriya V. Nareen Harsha Department of Computer Science and Engineering SRM Institute of Science & Technology Tiruchirappalli India School of Computer Science and Engineering VIT University Chennai India Dept. of CSBS K. Ramakrishnan College of Engineering Trichy India Department of CSE Knowledge Institute of Technology Salem India Dept. of CSE (AIE) Amrita VishwaVidyapeetham Chennai India

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

Parking congestion presents a significant challenge in urban areas worldwide, spanning both developed and developing nations. The escalating volume of vehicles compounds the scarcity of parking spots, particularly in downtown areas and adjacent streets. Local authorities are tasked with enforcing regulations to mitigate this issue, yet the current state of affairs often falls short of meeting the needs of numerous residents. Consequently, this article advocates using the YOLOv8 algorithm to identify vacant parking spaces. The proposed YOLOv8 comprises three pivotal blocks: the Backbone, Neck, and Head. The Backbone segment employs an enhanced version of the CSPDarknet53 network, integrating cross-stage partial connections to strategically strengthen information exchange across its diverse layers. The Neck section merges features with varying scales and incorporates contextual information from the feature maps. The Head section predicts class, objectness scores, and bounding boxes of identified objects. The performance of the YOLOv8 algorithm in identifying vacant parking spaces is evaluated using the parking space availability dataset. It achieves an F1-score of $98.7 \%$, which is $8.7 \%$ higher than the existing object detector.

关键词： Local government Urban areas Detectors Prediction algorithms Regulation Neck Object recognition

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Multi-Language Medical Symptoms Analyzer and Hospital Locator Chatbot

Multi-Language Medical Symptoms Analyzer and Hospital Locato...

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Expert Clouds and Applications (ICOECA), International Conference on

作者： Kasarapu Ramani Malepati Sai Deepthi N. Naveen Chintala Jeevan Raju Kandati Krishna Dept. of Data Science Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College) Tirupati India Department of IT Sree Vidyanikethan Engineering College Tirupati India

ISBN: (数字)9798350385793

ISBN: (纸本)9798350385809

Not every hospital may have a help desk to respond to patient queries regarding the disease prediction based on their symptoms and suggesting appropriate department or medicine, providing precautions for that disease in multiple languages. An automated system, such as a chatbot, can effectively address this issue. The medical chatbot is trained using Hybrid Deep Neural Network-Recurrent Neural Networks (DNN-RNN). The user's query accepted through user interface is pre-processed using Natural Language Processing (NLP) and based on the symptoms disease is predicted using wikipedia summary dynamically. The chatbot is user-friendly, and allows the users to interact in three different languages-English, Hindi and Telugu. It also enables users to enter the location in text, and it using Geopy library, Google Places API, and Folium library hospitals' location information is displayed in the user-specified area. The performance of the proposed method is evaluated using accuracy and loss function. This proposed method is a highly effective learning process, making the chatbot a reliable tool for medical queries and disease prediction based on symptoms.

关键词： Hospitals Neural networks Encyclopedias User interfaces Chatbots Libraries Internet

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Precise Diagnosis of Heart Disease with Stacking Classifier

Precise Diagnosis of Heart Disease with Stacking Classifier

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Emerging Technology (INCET), International Conference for

作者： V. Jyothsna B. Poojitha A.Sai Divya B. Ganesh D. Srinija Department of Data Science Mohan Babu University Tirupati India Dept. of information technology Sree Vidyanikethan Engineering College Tirupati India

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361162

Heart disease stands as a significant global health challenge, marked by a worrying rise in prevalence and substantial mortality rates. Despite the wealth of healthcare data, early detection of heart conditions remains elusive. This endeavor aims to address this issue by crafting a predictive model that integrates individual medical histories and lifestyle factors. The proposed methodology centers on the use of a Stacking classifier for heart disease prediction. To gauge the model's efficacy, the Framingham Heart Disease dataset is employed, sourced from the widely recognized Kaggle repository, endorsed by numerous researchers. Evaluation encompasses ensemble model assessments, incorporating critical metrics such as sensitivity, specificity, accuracy, and f-measure. Furthermore, the investigation delves into the application of ensemble methods for forecasting heart disease incidence over a decade, aiming to contribute to more effective strategies for early detection and management. Through the fusion of advanced modeling techniques and comprehensive datasets.

关键词： Heart Accuracy Sensitivity Stacking Predictive models Prediction algorithms Robustness

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Real-Time Stress Level Monitoring Using IOT

Real-Time Stress Level Monitoring Using IOT

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Integrated Intelligence and Communication Systems (ICIICS), International Conference on

作者： Asha Nandini Patil B M Preeti Laxmi Dept. of Artificial Intelligence and Data Science Sharnbasva University Kalaburagi Karnataka India Dept. of Computer Science and Engineering Sharnbasva University Kalaburagi Karnataka India Dept. of Electrical and Electronics Engineering Sharnbasva University Kalaburagi Karnataka India

Stress is a prevalent and debilitating issue in contemporary society, contributing to a range of physical and mental health problems. As our lives become increasingly fastpaced and interconnected, addressing stress effectively has become imperative. This introduces a pioneering approach to tackle challenge through real-time stress level monitoring utilizing the Internet of Things (IoT). We propose a holistic system that merges IoT sensors, advanced data analytics, and machine learning techniques to provide continuous stress assessment and management solutions. The data is collected through the designed model then preprocessed, and essential features are extracted to construct a predictive model. The heart of the system is a machine learning model, which constantly updates itself based on user-specific stress patterns, yielding highly accurate real-time stress level estimations. Users are provided with actionable feedback and personalized recommendations, ranging from deep breathing exercises to lifestyle adjustments. Extensive experimentation demonstrates the system's robustness, with stress level predictions boasting a mean absolute error of under 5%.

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