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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Vispute, Sushma Potdar, Manasi Pawara, Pranjal Patil, Shreya Patil, Satvika Ahirrao, Dinesh Bhaskar Dept. of Computer Engineering Pimpri Chinchwad College Of Engineering Pune India KCES’s College Of Engineering and Management Jalgaon India

ISBN: (纸本)9798331527549

In recent years, the proliferation of private vehicles has led to a significant challenge in finding adequate parking space, with unauthorized parking becoming a prevalent issue in many residential societies. To address this problem, we propose a novel solution that integrates machine learning techniques and motion detection technology to effectively manage parking spaces and curb unauthorized parking. Our approach utilizes advanced machine learning models such as RCNN for object detection and YOLOv8 for license plate detection, complemented by optical character recognition (OCR) for character recognition. Additionally, the ESP32-CAM webcam module is employed to capture real-time images, ensuring seamless integration with our system. This combination of technologies allows for the accurate detection of unauthorized parking instances in real time, providing efficient management of parking spaces within societies. Key features of the system include automated vehicle identification, real-time monitoring, and notification alerts, all of which contribute to a robust verification system for resident and guest vehicles. By leveraging these advanced technologies, our solution not only streamlines the parking process but also promotes a more organized and peaceful living environment. This innovative approach addresses a critical issue in urban settings, paving the way for more efficient and sustainable parking management practices. © 2025 IEEE.

关键词： Convolutional neural networks

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Simplifying Digital Streaming: An Innovative Cross-Platform Streaming Solution 15

Simplifying Digital Streaming: An Innovative Cross-Platform ...

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

作者： Muduli, Debendra Sahoo, Saroj Kumar Mishra, Jayanta Kumar Pradhan, Debasish Sharma, Santosh Kumar C.V Raman Global University Dept. of Computer Science Engineering Odisha Bhubaneswar India Centurion University of Technology and Management Dept. of Computer Science Engineering India GITA Autonomous College Dept. of Computer Science Engineering Odisha Bhubaneswar India

ISBN: (纸本)9798350370249

Our research aims to investigate how reinforcement learning methods can be applied to develop a movie recommendation system. We frame the issue of interactive recommendation as a contextual multi-armed bandit, where the system learns user preferences by suggesting new movies and receiving feedback. Through reinforcement learning, we address challenges such as balancing exploitation and exploration and tackling the coldstart problem. Additionally, we incorporate the novelty factor of movies into our model. We evaluate both content-based and collaborative filtering approaches which achieved accuracy 72.33(%), both of which demonstrate effective recommendation outcomes. © 2024 IEEE.

关键词： Contrastive Learning

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Classification of Foods based on Ingredients

Classification of Foods based on Ingredients

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Guria, Sudipa Banerjee, Sudarshan Bhattacharjee, Suddhasattwa Paul, Swarup Halder, Sobhan Das, Priyanka Institute of Engineering and Management Dept. of Computer Application Kolkata India

ISBN: (纸本)9798350348217

Due to the rise of recipe sharing websites, it is now quite easy to find online cooking instructions that list ingredients and cooking techniques. The development of recipe suggestion systems has also been a focus of several recipe sharing websites. Little has been done to analyse the relationship between cuisines and ingredients, while the majority of contemporary food-related research works concentrate on recipe suggestion systems. The work presented here offers an insight into which cuisines are the most related and what are a cuisine's important elements because we know that each cuisine is unique and has a separate list of ingredients. While the cuisine of food has been regarded as the class label, the ingredients have been seen as the features. In order to categorise the cuisine of unlabeled data, this work is focused on selecting the most important ingredients using various feature selection strategies and classification approaches. This has been done to investigate the fundamental connection between ingredients and cuisine. © 2023 IEEE.

关键词： Classification (of information)

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PhishGuard: A Convolutional Neural Network-Based Model for Detecting Phishing URLs with Explainability Analysis 3

PhishGuard: A Convolutional Neural Network-Based Model for D...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Islam, Md Robiul Islam, Md Mahamodul Afrin, Mst. Suraiya Antara, Anika Tabassum, Nujhat Amin, Al Uttara University Dept. of Computer Science and Engineering Dhaka Bangladesh University of Oslo Dept. of Informatics Oslo Norway Dept. of Building Engineering and Construction Management Khulna Bangladesh BRAC University Dept. of Electrical and Electronics Engineering Dhaka Bangladesh North South University Dept. of Electrical and Computer Engineering Dhaka Bangladesh Dept. of Computer Science American International Dhaka Bangladesh

ISBN: (纸本)9798350372120

Cybersecurity is one of the global issues because of the extensive dependence on cyber systems of individuals, industries, and organizations. Among the cyber attacks, phishing is increasing tremendously and affecting the global economy. Therefore, this phenomenon highlights the vital need for enhancing user awareness and robust support at both individual and organizational levels. Phishing URL identification is the best way to address the problem. Various machine learning and deep learning methods have been proposed to automate the detection of phishing URLs. However, these approaches often need more convincing accuracy and rely on datasets consisting of limited samples. Furthermore, these black-box intelligent models' decision to detect Suspicious URLs needs proper explanation to understand the features affecting the output. To address the issues, we propose a 1 D Convolutional Neural Network (CNN) and trained the model with extensive features and a substantial amount of data. The proposed model outperforms existing works by attaining an accuracy of 99.85 %. Additionally, our explainability analysis highlights certain features that significantly contribute to identifying the phishing URL. © 2024 IEEE.

关键词： Convolutional neural networks

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Stratification of metastatic melanoma patients based on mutational signatures

Stratification of metastatic melanoma patients based on muta...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Alfano, Caterina Farina, Lorenzo Petti, Manuela Sapienza University of Rome Program in Network Oncology and Precision Medicine Rome Italy Sapienza University of Rome Program in Network Oncology and Precision Medicine Dept. of Computer Control and Management Engineering Rome Italy Sapienza University of Rome Dept. of Computer Control and Management Engineering Rome Italy

ISBN: (纸本)9798350337488

Genetic instability is one of the hallmarks of cancer, however mutations can occur for different causes and induce different effects. Mutational signatures are characteristic patterns of somatic mutations in cancer genomes, reflecting the underlying mutational processes. A mutational signature can be determined by studying the kind of mutations a patient has acquired during their life: patient stratification based on mutational signatures has become more and more useful in genomic studies, given its possible clinical implications. In this work we focused on Single Base Substitution (SBS) signatures to study a cohort of 115 metastatic melanoma patients. We inferred and identified two mutational signatures characterizing the patients. Based on these signatures we divided patients into two group: the bigger group was characterized by a signature associated with exposure to ultraviolet light, while the smaller group resulted to be mostly composed of patients which did not respond to immunotherapy (anti-PD1) and that presented a low mutational count. More importantly this second group showed a significantly worse survival outcome. The use of mutational signatures is clearly a powerful tool to identify disease sub-types that have a clinical relevance, however we believe that this topic needs further investigation focused on the characterization of patient subtypes with a multi-omics based approach. © 2023 IEEE.

关键词： computational medicine genomics metastatic melanoma mutational signatures patient stratification precision oncology

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Closed-Loop System Identification of an Unstable Nonlinear System Using Koopman Operator 4

Closed-Loop System Identification of an Unstable Nonlinear S...

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4th International Symposium on Instrumentation, control, Artificial Intelligence, and Robotics, ICA-SYMP 2025

作者： Maneeloke, Benjaset Boonto, Sudchai Sanposh, Peerayot Tipsuwan, Yodyium Chinthaned, Natthawut Tanaprasitpattana, Warit Kasetsart University Dept. of Electrical Engineering Bangkok Thailand King Mongkut's University of Technology Thonburi Dept. of Control Systems and Instrumentation Engineering Bangkok Thailand Kasetsart University Dept. of Computer Engineering Bangkok Thailand

ISBN: (纸本)9798331505400

This paper presents a novel methodology for closed-loop system identification of unstable nonlinear systems using the Koopman operator with Extended Dynamic Mode Decomposition with control (EDMDc). The study highlights the critical role of selecting appropriate observable functions to develop accurate and efficient Koopman models. We demonstrate that the resulting Koopman models exhibit excellent fitting and validation properties and retain the stabilizability of the original nonlinear systems. These models are verified through time and frequency responses under closed-loop control using a Linear Quadratic Regulator (LQR), confirming their effectiveness. Future work will extend this framework to more complex systems and incorporate machine learning techniques to refine the selection of observable functions. This approach aims further to enhance the adaptability and robustness of Koopman-based control strategies. © 2025 IEEE.

关键词： Dynamic mode decomposition

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Exploring the Feasibility of Sleep Quality Evaluation with a Reduced Parameter Set 9

Exploring the Feasibility of Sleep Quality Evaluation with a...

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9th IEEE Smart World Congress, SWC 2023

作者： Guagnano, Michele Groppo, Sara Pugliese, Luigi Violante, Massimo Politecnico di Torino Dept. of Control and Computer Engineering Turin Italy

ISBN: (纸本)9798350319804

Sleep covers approximately one-third of life, providing the necessary recovery to fundamental body functions. Sleep deprivation and poor sleep quality cause several side effects in everyday life;among others, the efficiency of the immune system progressively decreases, enhancing the arise of pathologies. Therefore, the evaluation of sleep quality is crucial for providing information about personal health status. However, a uniform and robust method for the assessment of sleep quality is still missing. In this preliminary study, Obstructive Sleep Apneas (OSAs), sleep macro pattern and sleep micro pattern are identified and then combined to provide a comprehensive sleep evaluation. For this purpose, a subset of physiological variables (HR, HRV, SpO2, body movements) is derived from Polysomnography (PSG) and exploited for the development of rule-based algorithms. This reduced parameter set is selected considering further implementations on wearable off-the-shelf commercial devices. © 2023 IEEE.

关键词： ECG Hypopnea IOT Obstructive Sleep Apnea Obstructive Sleep Apnea Syndrome Sleep Sleep Macro Pattern Sleep Micro Pattern Sleep Quality Smartwatch SpO2

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Enhancing Question-Answering with Knowledge Graph Retrieval and Generation using LLMs

Enhancing Question-Answering with Knowledge Graph Retrieval ...

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2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Applications, ICAIQSA 2024

作者： Gijre, Supraj Agrawal, Rishi Laddha, Priyash Keswani, Gunjan Shri Ramdeobaba College of Engineering and Management Dept. Computer Science and Engineering Nagpur India School of Computer Science and Engineering Ramdeobaba University Nagpur India

ISBN: (纸本)9798331517953

This paper describes a novel technique to improving Large Language Models (LLMs) for document analysis that employs knowledge graphs and retrieval-augmented generation (RAG). We are working on constructing a chatbot system that can handle and analyze large documents from a variety of fields. Our approach addresses basic LLM issues including context maintenance and hallucination avoidance. The system combines document chunking, vector embedding, and similarity search with graph-based knowledge representation. Users can upload large papers and answer questions accurately. We show that integrating standard information retrieval approaches with graph-based storage and LLM capabilities improves context awareness and response accuracy across a wide range of document genres. This strategy is especially promising for complicated publications such as financial reports. © 2024 IEEE.

关键词： Knowledge graph

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IMPLEMENTATION OF BAYESIAN APPROACHES IN 5G/6G FOR CELLULAR COMMUNICATION USING MULTIPLE TIME SERIES MODELS

UPB Scientific Bulletin, Series C: Electrical Engineering an...

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UPB Scientific Bulletin, Series C: Electrical engineering and computer Science 2023年第1期85卷 209-226页

作者： Al-Dulaimi, Omer Mohammed Khodayer Al-Dulaimi, Aymen Mohammed Khodayer Maiduc Osiceanu, Alexandra Al-Dulaimi, Mohammed Khodayer Dept. of Telecommunication Engineering University POLITEHNICA of Bucharest Romania Dept. of Technical Communication Engineering Al-Farahidi University College Iraq Director for management of scientific research University POLITEHNICA of Bucharest Romania Dept. of Computer Engineering Al-Rafidain University of college Iraq

This research investigates the time-series dynamics of the Earnings per share (EPS) Radio Bearer Setup Failure Rate and the applicability of certain commonly used Time-Series prediction models. The regular part-time series prediction and the outliers’ prediction are two major issues of proactive network management that have been explored. We have utilized Holt-Winters Exponential Smoothing, extreme Gradient Boosting (XGBoost), Support Vector Regression (SVR), Python Bayesian dynamic linear mode (PyDLM), and Seasonal Auto-Regressive Integrated Moving Average with exogenous factors, (SARIMAX) to predict the regular component. Median Absolute Error, Mean Absolute Error, Mean Square Error, and Root Mean Square Error were used to examine the error performance. The prediction of outliers has been suggested as a two-stage process. © 2023, Politechnica University of Bucharest. All rights reserved.

关键词： Python

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Deep Analysis of Machine Learning Techniques for the Effective Retrieval of Images Based on Content 2

Deep Analysis of Machine Learning Techniques for the Effecti...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Jain, Ashish Varshney, Sudeep Set Sharda University Dept. of Computer Science and Engineering Greater Noida India G L Bajaj Institute of Technology and Management Dept. of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350371055

Our research aims to investigate how machine learning techniques can be applied for efficient image retrieval based on content. It is a very difficult process to obtain accurate images from vast digital image collections. CBIR has been particularly interested in this area. It has been shown that content- or feature-based approaches are more effective than text-based query techniques. Research has shown that the performance of these methods varies depending on the type and size of the data set analyzed, with some algorithms performing well for specific small data sets and others for larger data sets. This article provides a comprehensive and systematic review of effective methods for obtaining the image. The paper's objective is to examine different ML classifiers that are applied via the researchers and compare their results based on accuracy and performance. These algorithms include SVM, KNN, random forest, BoVW, Naive Bayes, and LDA. This review paper can assist researchers in selecting the best classifier for their research, as well as in improving existing classifiers by combining them with other algorithms. © 2024 IEEE.

关键词： CBIR classifier feature extraction Random Forest SVM

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