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Optimizing Network Traffic Prediction for Network Digital Twins: The Impact of Look-Back Period and Forecast Horizon

Optimizing Network Traffic Prediction for Network Digital Tw...

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2025 IEEE Wireless Communications and Networking Conference, WCNC 2025

作者： Sengendo, John Granelli, Fabrizio University of Trento Dept. of Information Engineering and Computer Science Trento Italy

ISBN: (纸本)9798350368369

Over recent years, many advancements in networks have taken place with now the advent of 6G. With these growing advancements, challenges in managing networks also emerge. Network Digital Twins (NDTs) is one of the potential technology in solving many of these challenges because of the capability to virtually replicate the physical network elements. One of the key challenges is predicting network traffic especially with the exponentially growing number of devices in the network. In this paper, we study and show how varying the 'look-back period' and 'forecast horizon' significantly affects traffic predictions. Look-back period or window size is how much of the historical data is used by a prediction algorithm to make predictions. Forecast horizon is how far in the future we can predict. These highly impact how accurately traffic is predicted in networks and as well significantly determine how well Digital Twins (DTs) for networks accurately reflect traffic of the physical network. © 2025 IEEE.

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Enhanced Parkinson's Disease Prediction Using Feature Selection, SMOTE, and Machine Learning with Deep Learning Models

Enhanced Parkinson's Disease Prediction Using Feature Select...

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International Conference on computer and Information Technology (ICCIT)

作者： Al-Amain Mirza Nadim Saad Esrat Jahan Rajon Bardhan Dept. of Computer Science and Engineering Southeast University Dhaka Bangladesh Dept. of Computer & Cyber Science Augusta University Georgia United States

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

This research leverages the UCL Parkinson's Disease clinical dataset to predict Parkinson's illness with the application of both deep learning and machine learning techniques. To address class imbalance, SMOTE was applied, and feature selection was conducted using the sci2 tool. The study assessed six deep learning algorithms, such as LSTM, PLSTM, and MLP, as well as six machine learning classifiers, including SVM, KNN, Logistic Regression, Naive Bayes, Decision Tree, and AdaBoost. MLP outperformed the others in terms of accuracy for deep learning models. With 97.20% accuracy, 97.44% precision, 97.20% recall, and 97.32% F1-score, KNN was the most successful overall, nonetheless. These findings show how well KNN detects Parkinson's disease and emphasize the technology's potential for early diagnosis and monitoring.

关键词： Deep learning Support vector machines Accuracy Machine learning algorithms Parkinson's disease Nearest neighbor methods Predictive models Feature extraction Prediction algorithms Monitoring

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An Advanced Smart Biotechnological Algal Plate Air Purification System for Use in Conjunction with Real-Time Air Quality Monitoring

An Advanced Smart Biotechnological Algal Plate Air Purificat...

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International Conference on computer and Communications Technologies (ICCCT)

作者： Bharathy S Srivatsaav Murali Jaswant Singh dept. Computer Science Sri Sairam Engineering College Chennai India

ISBN: (数字)9798331537579

ISBN: (纸本)9798331537586

Air pollution has become a health and environmental hazard as its effects are worsened by the increase inor pollution levels. Conventional air purification methods, although effective, are not environmentally friendly and can be expensive. We propose an Advanced Smart Biotechnological Algal Plate Air Purification System that combines the real-time monitoring of air quality with biological purification processes. An ATmega328 microcontroller, which synchronizes different parts of the system to function well together, powers the entire system. The gas sensor senses air contaminants and draws polluted air with a fan to the algal bioreactor chamber, onto which microalgae consume carbon dioxide and contaminants and output clean air. The biological system is controlled by a microcontroller where a DC fan is used to switch the flow of air in the system allowing the pollutants to be optimized to expose more and expose it to algae purification. It also handles the control of GPIO-based sensors and actuators to allow air quality metrics tracking and adaptive management. It contains an adjustable alarm and relay for turning on the fan in response to dangerous levels of pollution as well an LCD for easy reading of system status. The GPIO interface provides the ease of connecting other sensors and actuators making scalability possible for various applications. This simple and low-cost environmental friendly approach combines microcontroller-assisted technology with biological cleaning to give environmentally friendly air to urban, industrial and household consumers as the cost-effective alternative to existing conventional air purifiers.

关键词： Air cleaners Technological innovation Fans Purification Microcontrollers Algae Air pollution Real-time systems Liquid crystal displays Monitoring

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G-TRETA A Novel Multi-Objective Task Scheduling Algorithm in Cloud Computing

G-TRETA A Novel Multi-Objective Task Scheduling Algorithm in...

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IEEE International Conference on Utility and Cloud Computing (UCC)

作者： K. M. S. U. Bandaranayake Sunimal Rathnayake Dept. of Computer Science and Engineering University of Moratuwa Sri Lanka

ISBN: (数字)9798350367201

ISBN: (纸本)9798350367218

Cloud computing has revolutionized the IT industry by granting users to access computing resources and services over the Internet. Cloud offers an array of benefits like on-demand, scalable, cost-effective, and reliable resources and services to end users. This revolutionized technology enables various computing capabilities and data requirements to cloud users making it one of the most used computing models in the IT industry today. Task scheduling is considered an NP-complete problem. Genetic algorithms are widely used to find an optimal schedule for a task-scheduling problem with multiple objectives. None of the previous studies has considered the total resource execution time of the virtual machine as a scheduling parameter for finding a better schedule using metaheuristics like genetic algorithms. In this study, we propose a novel fitness function considering total resource execution time and cost in finding an optimum schedule. Hence we propose a novel multi-objective genetic algorithm G-TRETA with the above fitness function. The proposed algorithm was compared with FCFS, MaxMin, MinMin and MCT algorithms for the degree of imbalance, makespan, cost, and system throughput. CloudSim 5.0 simulator and DEAP framework with synthetic data traces and Nasa Ames/iPSC 860 real world traces were used to evaluate the algorithm. From the evaluation results the proposed algorithm gives better results. We can observe 36.37% and 50.46% for makespan, 57.16%, 101.88% for system throughput, 1.47 and 0.94 differences of index improvement for the degree of imbalance for synthetic and real world traces respectively while having efficient cost values.

关键词： Industries Cloud computing Schedules Job shop scheduling Costs Optimal scheduling Throughput Virtual machines Genetic algorithms Synthetic data

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Optimizing Kidney Stone Detection with Vision Transformers and CNNs: A Transfer Learning Strategy

Optimizing Kidney Stone Detection with Vision Transformers a...

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Pervasive Computational Technologies (ICPCT), International Conference on

作者： Supritha Kulkarni Suhaas Rao S Tushar Kumar Chopra Yeshwanth S P Bhanushree K J Suneetha K R Dept. of Computer Science and Engineering Bangalore Institute of Technology Bengaluru India

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

Kidney stones are a common and impactful urological condition, with timely and accurate diagnosis being critical to prevent complications such as renal damage and avoid invasive treatments. This study explores the use of advanced deep learning models, including Vision Transformers (ViT), Swin Transformer and Convolutional Neural Networks (CNNs)—specifically XResNet-50, VGG-16, MobileNetV2 and AlexNet—to enhance kidney stone detection from CT scan images. Leveraging a robust dataset of annotated CT scans in JPG format, the approach employs transfer learning which leverages features learnt from the ImageNet dataset to optimize model performance, focusing on classifying cases as either "Kidney Stone Present" or "Kidney Stone Absent". To further improve diagnostic reliability, the Grad-CAM (Gradient-weighted Class Activation Mapping) identifies and localizes kidney stones within CT images, enhancing understanding of the model’s decision-making and supporting interpretability. Addressing challenges related to dataset imbalance, model interpretability, and generalizability across healthcare settings. By refining these models for kidney stone detection, the main objective of this work is to build a reliable diagnostic tool that aids in clinical judgement and treatment planning, ultimately enhancing patient outcomes and encouraging broader clinical adoption.

关键词： computer vision Visualization Accuracy Computed tomography Transfer learning Medical services Transformers Data models Reliability Convolutional neural networks

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Handling Rollbacks with Separated Response Control Service for Microservice Architecture 3

Handling Rollbacks with Separated Response Control Service f...

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3rd International Informatics and Software engineering Conference, IISEC 2022

作者： Gordesli, Mustafa Nasab, Ahad Varol, Asaf Maltepe University College of Engineering and Natural Sciences Department of Computer Engineering Istanbul Turkey The University of Chattanooga College of Engineering and Computer Science Dept. of Engineering Management and Technology ChattanoogaTN United States

ISBN: (数字)9781665459952

ISBN: (纸本)9781665459952

Working with large scale microservice based applications may be hard to maintain and control. The services in those applications must be isolated from each other and they must work in harmony. There must be an intelligent transaction management system to implement this kind of collaborative work of microservices. There are two main methodologies for transaction management, namely Two Phase Commit (2PC) and Saga pattern. There are two phases in 2PC that prepare phase and commit phase. In Saga pattern, there are two ways of implementing Saga pattern;choreography based, and orchestration based. When there is a problem in the whole transaction, controlling the rollback scenario may be hard because each microservice has its own rollout and rollback solutions in both ways of Saga pattern. If we separate the rollback system from the microservices, we might have simpler microservices and microservices that can run in parallel. So whole system works safer and faster. In this work we focus on parallel execution of microservices with separate rollback system that controls the whole application in error condition. © 2022 IEEE.

关键词： Federated learning Microservice architectures computer architecture Control systems Informatics Software engineering

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Advancing Online Safety: AI-Powered Hate Speech and Offensive Language Detection in Social Media

Advancing Online Safety: AI-Powered Hate Speech and Offensiv...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Md.Nahid Hasan Abid Hossain Masum Sakibul Hasan Rony Habiba Dewan Arpita Tapasy Rabeya Dept. of Computer Science & Engineering Daffodil International University Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Hate speech encompasses verbal or written expressions that demean or attack individuals or communities due to their identity, including religion, race, gender, nationality, or other defining traits. Social media's emergence as the prevailing communication vector has coincidentally enabled faster distribution of damaging content. The escalation of hateful and abusive language online has transcended virtual spaces, frequently triggering tangible societal repercussions. The present work contributes to creating protective digital communities by implementing detection and intervention systems for toxic communication in fluid social media contexts. A key contribution of this research lies in advancing hate speech identification on social networks through systematic analysis of Twitter data, including tweets and retweets, to derive actionable insights for enhancing content moderation systems. A curated dataset of 21,010 instances—spanning hate speech, offensive language, and neutral speech—was assembled for evaluation. Multiple machine learning models, such as Logistic Regression, Random Forest, Support Vector Machines (SVM), Naïve Bayes, and XGBoost, were employed for classification. The Random Forest classifier outperformed others with 98% accuracy, whereas Logistic Regression, SVM, and XGBoost achieved moderate accuracy rates between 78% and 86%. Naïve Bayes trailed with 71% accuracy, highlighting varying efficacy across algorithmic approaches.

关键词： Support vector machines Logistic regression Accuracy Systematics Social networking (online) Hate speech Blogs Vectors Bayes methods Random forests

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The Smart IoT based Automated Irrigation System using Arduino UNO and Soil Moisture Sensor 4

The Smart IoT based Automated Irrigation System using Arduin...

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4th International Conference on Smart Systems and Inventive Technology, ICSSIT 2022

作者： Gnanavel, S. Sreekrishna, M. Murugan, N. Durai Jaeyalakshmi, M. Loksharan, S. Dept of Computing Technologies Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur India Dept of Computer Science Engineering Rajalakshmi Engineering College Thandalam India

ISBN: (纸本)9781665401180

Agriculture is the important job in India. It needs water for irrigation. The main job of the farmer is to irrigate the field. In this research, the irrigation system is automated using the Internet of Things technology. Therefore, it reduces the needfor human intervention. This study will find the best crops suitable for the particular soil. When water is needed, the field is irrigated with sufficient water. When the water level in the field reaches a certain height or the water level in the well reaches the minimum level, the system sensor detects it and automatically shuts off the pump motor. From this, a physical exertion is reduced and the field is watered effectively. Moreover the working life of the pump motor is extended. © 2022 IEEE

关键词： Internet of things

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Predictive Modeling of Cryptocurrency Prices Using Machine Learning Algorithms

Predictive Modeling of Cryptocurrency Prices Using Machine L...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Tanjim Mahmud Tahmina Akter Rishita Chakma Tikle Barua Mohammad Shahadat Hossain Karl Andersson Dept. of Computer Science and Engineering Rangamati Science and Technology University Rangamati Bangladesh Dept. of Computer Science and Engineering Port City International University Chittagong Bangladesh Dept. of Computer Science and Engineering University of Chittagong Chittagong Bangladesh Cybersecurity Laboratory Luleå University of Technology Luleå Sweden

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Cryptocurrencies, such as Bitcoin, Binance, Ethereum, FTX, and XRP, are decentralized digital assets known for their volatile nature and potential as investment instruments. Accurate price prediction is crucial for informed investment decisions. This study explores the feasibility of various modeling techniques on diverse data structures and features for predicting the prices of these cryptocurrencies. We utilize daily and high-frequency price data to classify and predict prices using deep learning and machine learning techniques, including LSTM, Bi-LSTM, GRU, linear regression, and SGD regression. Our findings indicate that daily price projections achieve an accuracy of 0.99, outperforming more complex deep learning and machine learning models. Compared to benchmark results, our approach demonstrates superior performance, with the highest scores achieved by the applied statistical methods and advanced algorithms. This research highlights the effectiveness of deep learning and machine learning models in cryptocurrency price forecasting, offering a foundation for further exploration in the field and emphasizing the significance of sample size in predictive modeling.

关键词： Deep learning Machine learning algorithms Accuracy Statistical analysis Computational modeling Linear regression Predictive models Prediction algorithms Long short term memory Investment

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Glaucoma Insights: a Visual Comparative Analysis of Fundus Images Across Diverse Datasets

Glaucoma Insights: a Visual Comparative Analysis of Fundus I...

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Smart Systems for applications in Electrical sciences (ICSSES), International Conference on

作者： Chunduri Yoshitha G Vishwa Prakashini Neha S Aiswariya Milan K Dept. of Computer Science and Engineering Amrita School of Computing Bengaluru India

ISBN: (数字)9798331507671

ISBN: (纸本)9798331507688

Glaucoma is a major global health issue that can lead to irreversible vision loss if untreated. Early diagnosis and continuous monitoring are crucial to mitigating its impacts. Fundus images are necessary to evaluate, diagnose glaucoma and offer an in-depth perception of retinal structures and features indicative of the condition. The project aims to compare the features of fundus images from three widely recognized datasets, they are REFUGE, ODIR, and DRONIS-DB focusing on analyzing critical metrics such as pixel intensity, brightness, image channel analysis, edge mean, and saturation means. Advanced visualizations are employed to ensure a comprehensive analysis of the features. The main objective of the work is to provide a structured data-driven approach for feature extraction and visualization, enhancing our understanding. This project contributes to advancing diagnostic precision, supporting clinical decision-making, and enabling researchers to explore innovative solutions in ophthalmology by bridging the gap between medical imaging data and actionable insights.

关键词： Glaucoma Visualization Decision making Data visualization Feature extraction Smart systems Retina Ophthalmology Biomedical imaging Resistance heating

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