Procedia Computer Science最新文献

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Challenges and Innovations in Multimedia and Real-Time Networking: A Review of Modeling Approaches

Procedia Computer Science

Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2024.12.006

Milind Shah , Kajal Parmar , Priyanka Padhiyar , Naina Parmar , Monali Parikh , Dhruvansh Patni

Over the past decade, the rise of broadband and mobile Internet access has led to the widespread adoption of real-time networking and multimedia applications. These platforms have become essential for connecting individuals and supporting businesses, particularly with the growing trend of remote work. The demand for video streaming services has surged significantly and is expected to continue rising. However, this increased traffic has strained network performance, leading to congestion and diminishing the quality of service, especially during peak evening hours and the COVID-19 pandemic. Besides upgrading network infrastructure, it is crucial to develop intelligent streaming systems that adapt to network conditions and user expectations to enhance customer satisfaction. This review paper explores into the basics and introduction of Multimedia and Real-Time Networking. It also explores the advancements in Adaptive and Intelligent Networking for real-time multimedia communication. The paper formulates research questions based on the discussed topics, such as the distinctions between real-time networking and traditional networking, the ways multimedia applications adjust to fluctuating network conditions, and the challenges and limitations associated with these technologies.

{"title":"Challenges and Innovations in Multimedia and Real-Time Networking: A Review of Modeling Approaches","authors":"Milind Shah , Kajal Parmar , Priyanka Padhiyar , Naina Parmar , Monali Parikh , Dhruvansh Patni","doi":"10.1016/j.procs.2024.12.006","DOIUrl":"10.1016/j.procs.2024.12.006","url":null,"abstract":"<div><div>Over the past decade, the rise of broadband and mobile Internet access has led to the widespread adoption of real-time networking and multimedia applications. These platforms have become essential for connecting individuals and supporting businesses, particularly with the growing trend of remote work. The demand for video streaming services has surged significantly and is expected to continue rising. However, this increased traffic has strained network performance, leading to congestion and diminishing the quality of service, especially during peak evening hours and the COVID-19 pandemic. Besides upgrading network infrastructure, it is crucial to develop intelligent streaming systems that adapt to network conditions and user expectations to enhance customer satisfaction. This review paper explores into the basics and introduction of Multimedia and Real-Time Networking. It also explores the advancements in Adaptive and Intelligent Networking for real-time multimedia communication. The paper formulates research questions based on the discussed topics, such as the distinctions between real-time networking and traditional networking, the ways multimedia applications adjust to fluctuating network conditions, and the challenges and limitations associated with these technologies.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"252 ","pages":"Pages 53-62"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143376847","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Analysis of Solar Panel Power Investigation using Fixed Axis, Single Axis and Dual Axis Solar Tracker

Procedia Computer Science

Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.01.031

Md. Humayun Kabir , Md. Himel Abu Jihad , Suman Chowdhury

This study investigates the performance of PV panels using three configurations: a fixed, a single-axis, and a dual-axis tracker, all controlled by an Arduino UNO system. The primary aim is to find whether a stationary PV panel or a solar energy tracker yields better power output. The study is partitioned into 2 key stages: practical and software implementation. In the experimental stage, four LDRs are utilized to track sunlight. Based on the solar detection, 2 servo motors adjust the coordination of the solar panel. For the software component, the Arduino IDE is utilized to write a program that interprets signals from the LDRs and sends commands to the motors, ensuring optimal positioning of the panel. The solar tracking system’s performance was contrasted with that of a stationary panel. Results presented that the solar energy tracker significantly outperforms the fixed set up, delivering higher power output. As a result, it has been demonstrated that the solar tracker is more successful at maximizing solar radiation, improving overall energy production. The maximum average solar tracking efficiency obtained from the dual axis set up is 35.5% where it is 33.23% for the single axis rotation set up.

{"title":"Analysis of Solar Panel Power Investigation using Fixed Axis, Single Axis and Dual Axis Solar Tracker","authors":"Md. Humayun Kabir , Md. Himel Abu Jihad , Suman Chowdhury","doi":"10.1016/j.procs.2025.01.031","DOIUrl":"10.1016/j.procs.2025.01.031","url":null,"abstract":"<div><div>This study investigates the performance of PV panels using three configurations: a fixed, a single-axis, and a dual-axis tracker, all controlled by an Arduino UNO system. The primary aim is to find whether a stationary PV panel or a solar energy tracker yields better power output. The study is partitioned into 2 key stages: practical and software implementation. In the experimental stage, four LDRs are utilized to track sunlight. Based on the solar detection, 2 servo motors adjust the coordination of the solar panel. For the software component, the Arduino IDE is utilized to write a program that interprets signals from the LDRs and sends commands to the motors, ensuring optimal positioning of the panel. The solar tracking system’s performance was contrasted with that of a stationary panel. Results presented that the solar energy tracker significantly outperforms the fixed set up, delivering higher power output. As a result, it has been demonstrated that the solar tracker is more successful at maximizing solar radiation, improving overall energy production. The maximum average solar tracking efficiency obtained from the dual axis set up is 35.5% where it is 33.23% for the single axis rotation set up.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"252 ","pages":"Pages 708-714"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143376853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Advancing Bionic Solution through Artificial Intelligence in Healthcare IoT Environment

Procedia Computer Science

Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.01.032

Girish Wali , Chetan Bulla Dr.

The convergence of human and artificial intelligence (AI) holds great potential for revolutionary changes in healthcare. This study investigates the possibility of a mutually beneficial relationship between artificial intelligence algorithms and human knowledge in creating bionic healthcare solutions. This paper emphasizes the revolutionary effect of this multidisciplinary strategy on healthcare delivery, patient outcomes, and quality of services by reviewing extensively recent developments and case examples. The Deep learning model is designed to predict diabetics using a standard dataset. The Convolution LSTM model is used to predict diabetics to improve accura-cy and reduce the latency. The proposed model is simulated in the Google Colab framework with Python programming language. The simulation results show that the proposed model is more accurate and lesser communication delay as compared to existing works.

引用次数: 0

An Integrated Approach Based on Fuzzy Logic and Machine Learning Techniques for Reliable Wine Quality Prediction

Procedia Computer Science

Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.01.021

Narinder Kaur , Gaganjot Kaur , Prasanth Aruchamy , Neelu Chaudhary

In recent decades, wine quality has been one of the predominant problems in many wine industries. However, the analysis of wine quality is inherently complex owing to its multivariate characteristics and the instigation of several sensory features. Most of the existing prediction methods lagged in providing higher detection accuracy for multi-dimensional datasets. To overcome this, a novel Adaptive Wine Quality Prediction (AWQP) approach has been proposed to assess the quality of the wine in an accurate manner. The proposed AWQP methodology entails the development of a Hybrid detection model that encompasses the fuzzy logic principles with the predictive influence of machine learning techniques. Primarily, the sensory features like aroma, taste, and color are delineated by exemplifying the linguistic variables. The fuzzy rules are then determined to collare the qualitative relationships among these different variables. Subsequently, the finest machine learning algorithm can be carried out to train and test the prediction model. The proposed AWQP methodology ameliorates the comprehensibility of the decision-making process through the hybridization of fuzzy logic and the finest machine learning. This proper hybridization facilitates the proposed method to achieve a superior detection accuracy of 98.75% when compared to the existing prediction methods.

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引用次数: 0

Enabling Robust Security in MQTT-Based IoT Networks with Dynamic Resource-Aware Key Sharing

Procedia Computer Science

Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.01.023

Sharadadevi Kaganurmath , Nagaraj Cholli

This paper presents a novel approach aimed at developing a secure secret key-sharing system optimized for resource-constrained Internet of Things (IoT) devices. Focusing on the MQTT protocol, the research endeavors to establish secure communication channels between IoT devices and brokers, thereby enhancing the overall security of MQTT-based IoT deployments. This research introduces a novel Dynamic Lightweight Authentication for MQTT (DLA-MQTT) mechanism designed to the unique needs of IoT devices operating under the MQTT protocol. The DLA-MQTT mechanism leverages an innovative lightweight Generalized Feedback Shift Register (GFSR)-based Pseudo-Random Number Generator (PRNG) to generate ephemeral keys, ensuring secure communication while addressing the limitations of computational power and energy resources. Through a detailed comparative analysis with existing cryptographic solutions, the DLA-MQTT mechanism demonstrates superior performance in terms of computational overhead, energy consumption, and execution time, while maintaining robust security against common attacks such as Man-in-the-Middle (MitM) and Denial of Service (DoS). he proposed algorithm’s adaptability and scalability are validated using the Cooja simulator, where simulated IoT networks are subjected to various threat scenarios. The results confirm the DLA-MQTT mechanism’s efficacy, showcasing a significant reduction in resource utilization without compromising the strength of the security provided.

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引用次数: 0

Determining the blur factor of handwritten characters using a convolutional neural network

Procedia Computer Science

Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2024.12.030

Dina Tuliabaeva , Dmitrii Tumakov , Leonid Elshin

The images of handwritten digits and Latin letters from the MNIST and EMNIST datasets are considered. Each image, which has a size of 28x28 pixels, is convolved with a 3x3 matrix. The convolution matrices are symmetric with respect to the central element and are normalized so that all elements are non-negative and their sum is equal to one. Each convolution matrix is characterized by a central element whose value varies from zero to one, indicating the blur factor. The blur matrices are formed randomly according to the uniform distribution of a random variable. Thus, all images of the training and test sets of both datasets have different blur factors. In the next step, a LeNet-5 neural convolutional network is trained to find the blur factor of an image. In cases where the training and test sets are from the same dataset, the accuracy of determining the blur factor is 99.92% for MNIST and 97.95% for EMNIST. The accuracy deteriorates to 90.2% and 85.9% when the training and test sets are from different datasets. The accuracy of predicting the blur factor depending on blur amount is analyzed. It is concluded that the minimum and maximum blur factor values are determined best.

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引用次数: 0

Advanced Phishing Website Detection with SMOTETomek-XGB: Addressing Class Imbalance for Optimal Results

Procedia Computer Science

Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2024.12.031

Kamal Omari , Ayoub Oukhatar

In the evolving field of cybersecurity, detecting phishing websites poses a unique challenge, primarily due to the issue of class imbalance. This research proposes a novel approach by combining the SMOTETomek resampling technique with the XGBoost classifier to tackle this challenge. SMOTETomek, a hybrid technique that combines SMOTE with Tomek Links, addresses both oversampling and undersampling by enhancing minority class representation while eliminating ambiguous instances. The proposed SMOTETomek-XGB model consistently surpasses traditional classifiers across key performance metrics, including accuracy, F1 score, recall, precision, and ROC-AUC. This combination significantly improves phishing detection, advancing the state of the art in mitigating online threats. The results suggest that SMOTETomek-XGB is an essential tool for enhancing detection capabilities in cybersecurity.

引用次数: 0

A Hybrid Learning Model for Tomato Plant Disease Detection using Deep Reinforcement Learning with Transfer Learning

Procedia Computer Science

Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2024.12.036

Kadambari Raghuram , Malaya Dutta Borah

Plant diseases play a significant role in damaging crop production and food security. Detecting and diagnosing plant diseases in the early stages obtains better management of diseases. Many plants are affected by these diseases, which are very dangerous for crop yield. This paper introduces advanced plant disease detection using an advanced preprocessing technique and a Hybrid Learning Model (HLM). The advanced preprocessing method uses a digital camera to capture high-resolution images of plant leaves from multiple angles. These images are then processed using an enhancement algorithm to improve the visual quality and clarity. The preprocessed images are subjected to a HLM model, which utilizes Deep Reinforcement Learning with Transfer Learning (DRL-TL). The DRL-TL architecture is designed to extract features from the preprocessed images in a three-dimensional manner, considering the spatial information of the leaves. It enables the model to capture precise patterns and variations indicative of disease symptoms. The pre-trained model MobileNetV2 trained on a tomato disease dataset belongs to labeled images; it consists of standard and affected plant leaves to learn the discriminative features associated with different diseases. Results obtained the effectiveness of the hybrid learning model. The preprocessing technique significantly increases the input images’ quality, enhancing the subsequent HLM’s performance. The model accurately identifies and classifies various plant diseases, outperforming existing methods. Furthermore, the hybrid learning model shows robustness and abstraction, successfully detecting diseases across plant species and environmental conditions.

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引用次数: 0

Automated Paddy Leaf Disease Identification using Visual Leaf Images based on Nine Pre-trained Models Approach

Procedia Computer Science

Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2024.12.013

Petchiammal A , D. Murugan Dr.

The recent rise in paddy leaf diseases poses significant challenges, emphasizing the need for focused research and rapid implementation of an artificial intelligence technique for the identification of crop leaf disease. As a staple food for over half the global population and a key ingredient in global cuisines, paddy offers numerous health benefits but is hindered by diseases like brown spot and blast disease. Effective paddy leaf disease management requires precise classification. This study used a public dataset and Artificial Intelligence to identify and classify these diseases. We applied a deep Convolutional Neural Network (CNN) and nine transfer learning models (VGG19, VGG16, DenseNet121, MobileNetV2, DenseNet169, DenseNet201, InceptionV3, ResNet152V2, and NASNetMobile) using TensorFlow. Each model’s performance was assessed to find the most effective classification system, covering four disease categories and one non-disease category. The research aimed for shorter training times, higher accuracy, and easier retraining, with DenseNet121 achieving the highest classification accuracy of 97.6% on the paddy leaf image dataset.

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引用次数: 0

Greener and Energy-Efficient Data Center for Blockchain-based Cryptocurrency Mining

Procedia Computer Science

Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2024.12.021

Asif Mahmud , K.M. Safin Kamal , Ahmed Wasif Reza

Cryptocurrency mining data centers consume 100-200 times more energy than conventional office areas annually. Regulating power consumption, cooling mechanisms, and thermal control performance is crucial to creating a greener and more energy-efficient crypto-mining data center. This paper presents a new cryptocurrency mining data center design that is both environmentally friendly and energy-efficient. The design considers popular green and energy-saving data center cooling and temperature management approaches, as well as cost-effective operations. The total monthly cost of the proposed data center is 358025 USD, with renewable energy generating 68520 kW of electricity. The monthly profit from Bitcoin mining is 3200806.969 USD, while Ethereum mining is 2317353.503 USD. The PUE number is 1.04, and the DCiE is 96.15 percent. These statistics help determine the model’s conclusion.

引用次数: 0

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