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检索条件"任意字段=6th International Conference on Advanced Informatics for Computing Research, ICAICR 2023"

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6th international conference on advanced informatics for computing research, icaicr 2023

6th International Conference on Advanced Informatics for Com...

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6th international conference on advanced informatics for computing research, icaicr 2023

ISBN: (纸本)9783031840616

the proceedings contain 58 papers. the special focus in this conference is on advanced informatics for computing research. the topics include: Face Spoof Detection: the Preprocessing Paradigm;Traffic Sign Recognition Using Convolutional Neural Networks with Transfer Learning for advanced Driving Assistance Systems (ADAS);analyzing the Criticalness of "the Silent Killer" Using Machine Learning;AI-Optimized Erection of Landslide-Resistant Retaining Structures through Heterogeneous Composite Nanomaterials: A Computerized Algorithmic Breakthrough;do Software Refactoring Reflects Developers’ Perception About Software’s Code Smells and Its Quality: An Empirical Study;Transfer Learning for Pneumonia Classification from DICOM Medical Imaging Data;A Study to Explore AI Techniques Used for Analyzing the Impact and Severity of Detrimental Human Health Effects Caused by Agrochemicals;Distance Metric Selection for AIS Data Clustering Using DBSCAN and ST-DBSCAN;fake News Detection and Classification Using Deep Learning Techniques for Social Media Text;real-Time Resource Allocation for Electric Vehicle Charging Stations Using Reinforcement Learning;data Sciences: Cutting Edge Tools and Techniques;Exploratory Data Analysis of Phishing URL Detection Using Gradient Boosting Classifier;advancing Healthcare Systems with Low-Power and Efficient Double Tail Dynamic Latch Comparator;SAMBHAV: Socially Sensed, Accessible, Mobile, Behaviour-Based and Human-Centric Smart Wearable Devices Aimed for Visually Impaired and Persons with Allied Disabilities;multi-disease Classification and Prediction Using Dense Visual Attention Network and Residual Network for: A Computer Vision Approach;Enhancing Text Extraction Using OCR with AWS Textract Service;a Study: Recognition of Facial Emotions Using Deep Learning;machine Learning-Based Systems for Diagnosing Multiple Diseases and Providing Dietary Recommendations;machine Learning Approaches for Evaluation of Chronic Kidney Disease.

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6th international conference on advanced informatics for computing research, icaicr 2023

6th International Conference on Advanced Informatics for Com...

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6th international conference on advanced informatics for computing research, icaicr 2023

ISBN: (纸本)9783031840586

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A Methodical Approach to Healthcare Cybersecurity: Handling Current Risks and Advancements 6

A Methodical Approach to Healthcare Cybersecurity: Handling ...

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6th international conference on Mobile computing and Sustainable informatics, ICMCSI 2025

作者： Husain Bathushaw, M. Nagasundaram, S. Department of Computer Applications VELS Institute of Science Technology and Advanced Studies Pallavaram Chennai600117 India

ISBN: (纸本)9798331522667

the methods were evaluated using machine learning-driven interruption detection, blockchain-enhanced access control for inconsistency localization, and structured threat insight data in a simulated healthcare environment. With an accuracy range of 0.88-0.94 and lift specified between 0.75 and 1, knowledge values ranging from.86 to.92, and F1 scores between and above, the test results demonstrated that all computations were possible. BACA strengthened access control with blockchain;ADA surpassed expectations in identifying discrepancies;TIAA succeeded in risk insights management;and ML-IDS achieved outstanding results in intrusion detection. A comparison with related research highlights the significance of these algorithms in tackling specific cybersecurity issues in the healthcare sector. Because the proposed algorithms provide a holistic approach to safeguard confidential health information, ensure the validity of assessment models, and strengthen organizations against a range of dynamic cyberthreats, they are pertinent to the expanding discussion about cybersecurity in the healthcare industry. © 2025 IEEE.

关键词： Electronic health record

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Empirical Examination of Mobile Ad Hoc Routing Protocol on Wireless Sensor Network 6

Empirical Examination of Mobile Ad Hoc Routing Protocol on W...

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6th international conference on Mobile computing and Sustainable informatics, ICMCSI 2025

作者： Nancharaiah, B. Krishnamoorthy, R. Kumar, Neeraj Patankar, Abhijit Janardan Ravikumar, K. Tiwari, Mohit Department Of ECE Usha Rama College of Engineering and Technology Telaprolu India Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Chennai India Department of Electrical Engineering Doon Institute of Engineering and Technology Uttarakhand India Department of Information Technology D Y Patil college of Engineering Akurdi Pune411044 India Department Of CSE RRASE College Of Engineering Chennai India Department of CSE Bharati Vidyapeeth’s College of Engineering Delhi India

ISBN: (纸本)9798331522667

A network is a distributed dynamic multi-hop MANET (mobile ad-hoc network). It's critical to create a successful ad-hoc network routing system in order to address the issue with MANET-based WSN (Wireless Sensor Network). In this proposal, various WSN communication routing algorithms are analyzed. there is also a brief description of significant routing and security issues. this research focuses on taxonomies associated with adhoc routing approaches and assesses the merits of suggested routing protocols for setting up sensor nodes and sending sensor signals. © 2025 IEEE.

关键词： Sensor nodes

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Real-World Bug Hunting Techniques for advanced Real-Time IP Address Redirection 6

Real-World Bug Hunting Techniques for Advanced Real-Time IP ...

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6th international conference on Mobile computing and Sustainable informatics, ICMCSI 2025

作者： Umesh, R. Akchara, T.D. Naglakshmi, G. Velammal College of Engineering and Technology Madurai India

ISBN: (纸本)9798331522667

this research study builds the state-of-art cyber security platform that eventually relied on the internet. It focuses on building this from high-end approaches with practical bug-hunting awareness in its aim toward effective threat detection. It continuously monitors network traffic through several algorithm models for analyzing and stopping malicious activities at real time. In doing this, it blocks and dynamically changes IP addresses in such a way that unwanted access cannot happen. Key Features: Anomaly detection through User and Entity Behavior Analytics, Security Adaptation, Automation, and Response for effective threat management, a threat intelligence feed keeping it abreast of new attack vectors, and the platform comes with deep logging for forensic investigations purposes. Predictive analytics, along with its continuous learning module, allows the detection algorithms to get optimized. Its friendly user interface promotes teamwork, data aggregation, and collective protection and, henceforth, provides the all-encompassing and flexible cybersecurity solution that can help to strengthen the businesses' defenses against constantly changing threats. © 2025 IEEE.

关键词： Cybersecurity

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Deep Learning based Air Quality Forecasting using Satellite Data 6

Deep Learning based Air Quality Forecasting using Satellite ...

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6th international conference on Mobile computing and Sustainable informatics, ICMCSI 2025

作者： Sree, K. Navya Reddy, Mancharla Rakesh Saraswathi, S. Department of Computer Science and Engineering Specialization with AI & ML Sathyabama Institute of Science and Technology Chennai India Department Computer science and engineering Sathyabama Institute of Science and Technology Chennai India

ISBN: (纸本)9798331522667

Air pollution is a significant threat to human health and the environment. Accurate air quality forecasting is essential for effective mitigation strategies, including public health advisories, emission control measures, and policy interventions. this research focuses on the development of an advanced air quality forecasting system using machine learning techniques. the system uses historical data on pollutants such as PM2.5, NO2, and O3, along with meteorological parameters, to predict future air quality levels. the model is enhanced with the inclusion of sophisticated machine learning algorithms to better capture the intricate spatiotemporal patterns and enhance the accuracy of forecasting. the system may potentially provide real-time air quality forecasts that enable proactive measures for protecting public health and inform decisions for environmental policy. this research contributes to sustainable and resilient urban environments by providing valuable insights into air quality dynamics and supporting effective air pollution mitigation strategies. © 2025 IEEE.

关键词： air pollution Air quality forecasts cardiovascular diseases data-driven strategies environmental policy global climate international collaboration machine learning NO2 O3 PM2.5 policy-making pollution mitigation proactive alerts public health real-time monitoring respiratory diseases spatial variability temporal variability transboundary pollution

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Detection of Brain Tumor using Transfer Learning using Conventional Autoencoder with Long Short Term Memory Method 6

Detection of Brain Tumor using Transfer Learning using Conve...

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6th international conference on Mobile computing and Sustainable informatics, ICMCSI 2025

作者： Agalya, D. Kamalakkannan, S. Department of Computer Science School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Pallavaram Chennai India Department of Information Technology School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Pallavaram Chennai India

ISBN: (纸本)9798331522667

Brain tumor (BT) has generate a significant health challenge by putting pressure on healthy brain parts or spreading into other areas as well as blocking the flow of fluid around the brain. BT diagnosis is an extensive and time consumption process that depends primarily on radiologists experience and interpretive capabilities. For the area of medical imaging, automatic segmentation of images by means of Deep Learning (DL) approaches. the DL approaches assist in transforming the sector, resulted in improving precision and effectiveness of investigations. However, there are various feature extraction mechanisms available. therefore, the Recurrent Neural Network (RNN) is utilized for feature extraction and image classification. In model execution, the data have trained in transforming the test image as well as data features for minimizing the domain shift is calculated through the Convolutional Autoencoder (CAE) for reconstruction loss. this research has concentrated in building a model with VGG16 as a single test that subjected at inference and existing method is adopted as neural networks for AE as Transfer Learning (TL) that performs an image analysis task such as segmentation and even set as an adopter for pre training the model. the AE used to train from the source dataset and perform as the adaptors in optimizing during testing using a test subject for effective computation. Moreover, the Long Short Term Memory (LSTM) is utilized as RNN model with CAE for providing improved detection of BT in healthcare industries. Hence, the proposed CAE with LSTM is compared with AE with Convolutional Neural Network (CNN) for evaluating BT detection using MRI dataset with various BT type classifications. © 2025 IEEE.

关键词： Image segmentation

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Smart Systems for Identifying Brain Tumors with MRI Images using LSTM 6

Smart Systems for Identifying Brain Tumors with MRI Images u...

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6th international conference on Mobile computing and Sustainable informatics, ICMCSI 2025

作者： Sudhakaran, Pradeep Yeshovardhan, N. Ranga Swamy, T. Department of Computing Technologies SRM Institute of Science and Technology Chengalpattu Tamil Nadu Kattankulathur603203 India

ISBN: (纸本)9798331522667

the detection and classification of brain tumors are vital components of medical imaging, with timely and accurate diagnoses having profound implications for patient health. this research introduces a novel approach that employs Long Short-Term Memory (LSTM) networks for detecting and classifying brain tumors using imaging data. As a specialized form of Recurrent Neural Networks (RNNs), LSTMs excel at processing sequential data, which is particularly relevant for medical images. the methodology involves processing a series of MRI images through a feature extraction pipeline, with the extracted features subsequently analyzed by the LSTM model for tumor identification. We have trained the model on a dataset consisting of synthetic brain tumors, allowing it to effectively distinguish between false positives and negatives with good accuracy. Experimental findings indicate that the LSTM technique outperforms conventional machine learning algorithms in both detection and classification tasks. the robustness of this system suggests it could serve as a valuable tool for radiologists, providing critical diagnostic support and enhancing patient care. Furthermore, the adaptability of the LSTM model allows for future integration with advanced imaging techniques, potentially broadening its applicability across various medical conditions. By improving diagnostic precision and efficiency, this approach may pave the way for more personalized strategies and good overall patient outcomes. © 2025 IEEE.

关键词： Long short-term memory

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Design of a Smart Home Door Security System with Face Detection and Smart Bell using ESP32-CAM 6

Design of a Smart Home Door Security System with Face Detect...

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6th international conference on Mobile computing and Sustainable informatics, ICMCSI 2025

作者： Salsabila, Nurhusna Siswanto, Apri Bayuaji, Luhur Informatics Department Faculty of Engineering Universitas Islam Riau Pekanbaru Indonesia Fac. Data Science and Information Technology INTI International University Malaysia

ISBN: (纸本)9798331522667

this research introduces the design and implementation of an advanced IoT-based home security system aimed at improving both convenience and safety for homeowners. the system integrates a smart doorbell with a mobile application, enabling real-time alerts for guest arrivals. Key features include facial recognition for automated door unlocking and notification of unrecognized visitors. When an unknown face is detected, the system sends an image along with a notification to the homeowner via a Telegram bot. the architecture incorporates the ESP32-CAM microcontroller for face detection, a solenoid lock for automatic door access, and an ultrasonic sensor to detect visitor presence. Extensive testing under various environmental conditions was conducted to assess the system's performance, particularly in terms of face recognition accuracy and notification delivery. the results indicate that the system significantly enhances home security by ensuring timely notifications and automating door access for verified individuals. While the system performs reliably in well-lit environments, challenges remain in low-light conditions. Software testing confirmed the robustness of the underlying logic. this innovative IoT solution overcomes the limitations of traditional security systems, offering a more secure, user-friendly, and efficient alternative for modern home security. © 2025 IEEE.

关键词： Ultrasonic sensors

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Customer Acquisition by Hybrid Intelligence: Explainable HM2CA Framework for Optimizing E-Commerce Marketing with LIME and SHAP 6

Customer Acquisition by Hybrid Intelligence: Explainable HM2...

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6th international conference on Mobile computing and Sustainable informatics, ICMCSI 2025

作者： Sonake, Unnita Amol Manoharan, Saravanapriya Business Analytics & CVM e& UAE/Business Dubai United Arab Emirates Aarohana Institute of Advanced Technological Training and Research Excellence Chennai India

ISBN: (纸本)9798331522667

this research proposes a Hybrid Modeller for Maximizing Customer Acquisition (HM2CA), a machine learning framework designed to enhance e-commerce marketing by identifying high-potential buyers. Using the "Online Purchase Intention" dataset of 12,330 sessions, the model integrates SMOTE with Gradient Boosting, achieving 93.47% prediction accuracy, a 3.1% improvement over the baseline. K-Median clustering segments customers into three categories: High Intention with Revenue, High Intention without Revenue, and Low Intention without Revenue, enabling precise targeting and reducing cart abandonment. Tools like LIME and SHAP enhance interpretability, explaining feature importance and classification reasoning. this transparency allows marketers to refine strategies effectively. By combining classification, clustering, and interpretability, the HM2CA framework optimizes marketing efforts, improving ROI and efficiently identifying high-value customers. © 2025 IEEE.

关键词： Marketplaces

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