Procedia Computer Science最新文献

Evaluating Energy Consumption for Routing Selection using the Multi-Routing Clustering Protocol using Timeslot Transmission in Dynamic Path Selection in Wireless Sensor Networks

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2024.12.027

M. Prakash , J. Abinesh , P. Malarvizhi , J. Jeba Emilyn , A. Sam Thamburaj , D. Vinod Kumar

{"title":"Evaluating Energy Consumption for Routing Selection using the Multi-Routing Clustering Protocol using Timeslot Transmission in Dynamic Path Selection in Wireless Sensor Networks","authors":"M. Prakash , J. Abinesh , P. Malarvizhi , J. Jeba Emilyn , A. Sam Thamburaj , D. Vinod Kumar","doi":"10.1016/j.procs.2024.12.027","DOIUrl":"10.1016/j.procs.2024.12.027","url":null,"abstract":"<div><div>Wireless sensor networks (WSNs) are cutting-edge technology that can be used in many fields requiring critical information. However, limited resource constraints, contextual connectivity, and lifecycle requirements drive designers to seek more efficient WSN infrastructures. Unbalanced energy utilization of sensor hubs during information steering in Wireless Sensor Networks (WSN). Accordingly, one of the principal configuration difficulties of remote sensor networks is to limit the energy utilization of sensor hubs. Therefore, many routing schemes are designed to efficiently utilize sensor nodes’ limited Energy. These schemes generally use low-power paths to transmit data. It turns out that using the same path is suboptimal given the network lifetime. To overcome this problem, a new method is introduced that uses a Multi-Route Clustering Protocol Using Timeslot Transmission (MRCP-TTDPS) with dynamic path selection. It consumes Energy on path selection. In the first step, multipath routing is configured to detect path quality, so this method uses Multipath Optimized Routing (MPRS) to send high-quality path data. The second stage uses Cluster-Based Optimal Path Selection (CORS) to establish the best energy path. The third stage develops energy consumption models. The amount of the hubs with the most noteworthy leftover Energy is chosen as the group top of each round. Every standard hub partakes in a group framed by neigh exhausting bunch heads. Every sensor hub sends identified information to the bunch head in each round. The group head sends the data to the base station. The results show that the model is better than the path model regarding path identifier energy utilization and path quality.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"252 ","pages":"Pages 251-259"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143376641","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

Implementation of Multi-Label Fuzzy Classification System using Topic Detection Data set

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2024.12.003

R. Kanagaraj , N. Krishnaraj , J. Selvakumar , J. Ramprasath

{"title":"Implementation of Multi-Label Fuzzy Classification System using Topic Detection Data set","authors":"R. Kanagaraj , N. Krishnaraj , J. Selvakumar , J. Ramprasath","doi":"10.1016/j.procs.2024.12.003","DOIUrl":"10.1016/j.procs.2024.12.003","url":null,"abstract":"<div><div>Multiclass Classification can be implemented by using consequent approaches to translate the multiclass problem into binary class classification problems and fuzzy classification methods. This work proposes a predictive analysis of the multiclass fuzzy Classification integrated with time series historical data and topic detection. The fuzzy classification techniques can be successfully applied to Topic detection and sub-topic detection. Text databases’ manual topic detection method must be more feasible, uncontrollable and effective. Thus, initiating the huge amount of data implemented by manual methods is idealistic. Fuzzy historical data is more significant for data analysis in different models to make predictions. Innumerable fuzzy logic on time series methods has been implemented for data prediction. A Multiclass Fuzzy Time Series Classification Algorithm has been implemented to analyze and predict the topic detection database. The outcomes of the Fuzzy classification technique have been implemented for the need for an extensive pattern of topic detection. An enhanced Multiclass Fuzzy Time Series Classification Algorithm has been applied to achieve the efficient de-fuzzification operation of the topic detection data set. To illuminate the forecasting method, the historical data of multi-labeled has been used for the predictive model. The investigation result illustrates that the MHTSC algorithm generates mode fuzzy classification and irregular rules, efficiently reducing the error rate from multi-labeled data.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"252 ","pages":"Pages 15-24"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143376844","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

A GAN-Enhanced Multimodal Diagnostic Framework Utilizing an Ensemble of BiLSTM, BiGRU, and RNN Models for Malaria and Dengue Detection

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2024.12.039

Rathnakar Achary, Chetan J Shelke, Alluru Lekhya

{"title":"A GAN-Enhanced Multimodal Diagnostic Framework Utilizing an Ensemble of BiLSTM, BiGRU, and RNN Models for Malaria and Dengue Detection","authors":"Rathnakar Achary, Chetan J Shelke, Alluru Lekhya","doi":"10.1016/j.procs.2024.12.039","DOIUrl":"10.1016/j.procs.2024.12.039","url":null,"abstract":"<div><div>Quick detection of Malaria and Dengue is crucial for doctors to start treatment and manage patients effectively. As patient conditions become more complex with overlapping symptoms, traditional diagnostic tools become inefficient, slow, and less accurate. Modernizing diagnostics with AI-powered systems is essential. Inaccurate or delayed diagnoses lead to transmission and sustained spread of these diseases. Improving diagnostic tools with accuracy, precision, recall, and speed enhances patient outcomes, reduces infection spread, and streamlines health sector operations. Despite advances, current diagnostic algorithms have weaknesses, especially in applying machine learning to diverse datasets at granular levels. Continuous effort is needed to improve accuracy and recall. This research proposes a GAN-Based Synthesized Multimodal Diagnostic System, combining BiLSTM, BiGRU, and RNN approaches. Utilizing GANs for data augmentation and recurrent networks, this framework shows innovative infectious disease detection. It improves diagnostic precision by 4.9%, accuracy by 3.5%, recall by 3.5%, and AUC by 4.5%, while reducing the gap between disease progression and detection by 8.3%. These outcomes can reduce triage time, misdiagnoses, and lead to faster, quality healthcare. The GAN-Enhanced Multimodal Diagnostic Framework shows promise for diagnosing Malaria, Dengue, and other infectious diseases.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"252 ","pages":"Pages 381-393"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143376857","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

Stray Dog Detection System using YOLOv5

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.01.041

Ashwini Bhosale , Pranav Shinde , Yash Firke , Shivprasad Patil , Pranav Mitake , Samruddhi Shinde

{"title":"Stray Dog Detection System using YOLOv5","authors":"Ashwini Bhosale , Pranav Shinde , Yash Firke , Shivprasad Patil , Pranav Mitake , Samruddhi Shinde","doi":"10.1016/j.procs.2025.01.041","DOIUrl":"10.1016/j.procs.2025.01.041","url":null,"abstract":"<div><div>Stray dogs present significant public health and safety risks, particularly in developing countries like India, where the stray dog population is the largest globally. This paper details the implementation of a Stray Dog Detection System using the YOLOv5 object detection model to automatically detect and track stray dogs in real time via CCTV feeds. YOLOv5’s high accuracy and real-time processing capabilities make it well-suited for detecting stray dogs in complex, crowded urban environments. The system leverages a YOLOv5 model trained on custom datasets tailored to local conditions, including specific dog breeds and deployment environments. It integrates an alert mechanism that triggers when stray dog populations surpass predefined thresholds, allowing timely interventions. Additionally, the system incorporates geographic mapping to provide data-driven insights for municipal authorities to manage stray populations effectively and ethically. Experimental results demonstrate an F1 score of 0.97, validating the system’s robustness for practical deployment. This paper discusses system architecture, implementation, and performance, highlighting its scalability and cost-effectiveness for humane stray dog population control.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"252 ","pages":"Pages 806-813"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143376863","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

Influencer Ranking Framework Using TH-DCNN for influence maximization

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.01.018

Vishakha Shelke , Ashish Jadhav Dr.

{"title":"Influencer Ranking Framework Using TH-DCNN for influence maximization","authors":"Vishakha Shelke , Ashish Jadhav Dr.","doi":"10.1016/j.procs.2025.01.018","DOIUrl":"10.1016/j.procs.2025.01.018","url":null,"abstract":"<div><div>As the influencer gains more significance in social media marketing, companies raise their budgets for influencer campaigns. With business increasing day by day, finding efficient influencers is becoming the most prominent factor for success, but choosing the right influencer from these social media users is quite a challenge. This manuscript proposes a novel method to rank influencers by their effectiveness based on their posting behavior and social relations over time. Initially, the data from Twitter is collected from the Indian politics tweets and reactions dataset. This raw data undergoes preprocessing using various techniques including, tokenization, stemming, lemmatization, stop word removal, and data normalization using the Min-Max normalization approach to ensure the data is relevant and suitable format for analysis. Next, construct a heterogeneous network to represent the complex interactions between entities like users, tweets, hashtags, and mentions. Then Tree Hierarchical Deep Convolutional Neural Network (TH-DCNN) is applied to these networks to derive information representation for each influencer at each period. Finally, a Cosine similarity (CS) is used to learn from the network and predict the influencer rankings. The performance metrics such as accuracy, f1-score, mean average precision (MAP), Normalized Discounted Cumulative Gain (NDCG), Receiver Operating characteristic (ROC), Mean Reciprocal Rank (MRR), and Hit Rate are analyzed in experimental evaluations. The proposed method improved the accuracy compared with existing techniques.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"252 ","pages":"Pages 583-592"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143376898","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

Location Verification of Wireless Sensor Node using Integrated Trilateration in Outdoor WSN

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.01.016

Arjun , Supreeth N M , Akhil K M , Sougandh Sunil

{"title":"Location Verification of Wireless Sensor Node using Integrated Trilateration in Outdoor WSN","authors":"Arjun , Supreeth N M , Akhil K M , Sougandh Sunil","doi":"10.1016/j.procs.2025.01.016","DOIUrl":"10.1016/j.procs.2025.01.016","url":null,"abstract":"<div><div>This paper presents a novel method for enhancing localization accuracy in Wireless Sensor Networks (WSNs) through an improved trilateration approach. Despite advancements in localization techniques, challenges remain in achieving reliable accuracy, particularly in complex environments. The proposed method enhances traditional trilateration by integrating angle of arrival (AoA) measurements, leading to better positioning of sensor nodes. In this study, the aim is to confirm whether the final coordinates of trilateration can be supported by adding residual analysis and AoA observations. Experiments were conducted to assess the localization system’s effectiveness. The results showed that residual validation outperformed AoA localization, particularly in noisy outdoor environments, providing more reliable distance estimates even under challenging conditions. This method enhances the trustworthiness of the localization system while also minimizing hardware needs and reducing computational complexity, making it a practical choice for resource-limited WSNs. The AoA verification method achieved average accuracies of 54% for x-coordinates and 55% for y-coordinates. In contrast, incorporating residual analysis improved these figures to 79% for x-coordinates and 80% for y-coordinates. These insights focus on the localization process and demonstrate the value of residual analysis in boosting system performance.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"252 ","pages":"Pages 567-575"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143376955","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

Hybrid Binary SGO-GA for solving MAX-SAT problem

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.01.055

Rhiddhi Prasad Das , Anuruddha Paul , Junali Jasmine Jena , Bibhuti Bhusan Dash , Utpal Chandra De , Mahendra Kumar Gourisaria

{"title":"Hybrid Binary SGO-GA for solving MAX-SAT problem","authors":"Rhiddhi Prasad Das , Anuruddha Paul , Junali Jasmine Jena , Bibhuti Bhusan Dash , Utpal Chandra De , Mahendra Kumar Gourisaria","doi":"10.1016/j.procs.2025.01.055","DOIUrl":"10.1016/j.procs.2025.01.055","url":null,"abstract":"<div><div>The Maximum Satisfiability Problem (MAX-SAT) is a crucial NP-hard optimization problem with applications in artificial intelligence, circuit design, scheduling, and combinatorial optimization. In this work, we provide a unique hybrid strategy that blends Genetic Algorithms (GA) with Social Group Optimization (SGO) algorithm to effectively solve the MAX-SAT problem. The SGO algorithm, inspired by the social behavior of groups, excels in exploring diverse regions of the search space. w used a binary variant of SGO i.e. Binary-SGO which is defined specifically for binary search spaces, while GA leverages evolutionary principles to exploit local optima through selection, crossover, and mutation. By integrating the exploration capabilities of SGO with the exploitation strengths of GA, the hybrid approach strikes an optimal balance between global and local search. Extensive experimental evaluations conducted on standard MAX-SAT benchmarks demonstrate that our hybrid algorithm outperforms several existing state-of-the-art meta-heuristic algorithms. Hybrid BSGO-GA achieved the highest average fitness values, with an average accuracy of 99.7% in Experiment 1, 99.61% in Experiment 2, and 99.21% in Experiment 3 and achieved complete satisfiability in 55 out of 75 cases in Experiment 1, 42 out of 75 cases in Experiment 2, and 7 out of 75 cases in Experiment 3. This approach demonstrates the potential of hybrid metaheuristics in addressing complex optimization problems and offers a robust framework for tackling other NP-hard problems.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"252 ","pages":"Pages 944-953"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143377128","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

Classification of Sarcoma Based on Genomic Data Using Machine Learning Models

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2024.12.034

Pratham Gala , Yash Pandloskar , Shubham Godbole , Fayed Hakim , Pratik Kanani , Lakshmi Kurup

{"title":"Classification of Sarcoma Based on Genomic Data Using Machine Learning Models","authors":"Pratham Gala , Yash Pandloskar , Shubham Godbole , Fayed Hakim , Pratik Kanani , Lakshmi Kurup","doi":"10.1016/j.procs.2024.12.034","DOIUrl":"10.1016/j.procs.2024.12.034","url":null,"abstract":"<div><div>The proposed work provides a new machine-learnt classification approach for the various types of soft tissue sarcoma based on genomics data which addresses a considerable gap in sarcoma diagnostics. The previous studies have investigated various aspects of sarcoma but this study is unique in that it targets the predicting sarcoma variant types using genetic information, which has not been done before. Random Forest was used as the meta-estimator and a stacking ensemble model comprising of Random Forest, Extreme Gradient Boosting and LightGBM were used for this study. The model which was trained and validated on a complete dataset of 206 adult soft tissue sarcoma samples containing genomic alterations, transcriptomic, epigenomic and proteomic data achieved an accuracy of 89.44% at a precision level as high as 91%. Stratified k-fold cross validation is employed to ensure that class imbalance is not a hindrance to performance. This innovative approach outmatches single classifiers and traditional single model methods at great length hence making it possible and effective to use machine learning on genomic data for predicting sarcoma variants. Thus, the findings from this research could change cancer diagnosis forever; they promise more accurate classification as well as personalized treatment modalities while also providing a framework for analogous applications in other rare complex cancers.</div></div>","PeriodicalId":20465,"journal":{"name":"Procedia Computer Science","volume":"252 ","pages":"Pages 317-330"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143376667","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

Hydroelectric Power Potentiality Analysis for the Future Aspect of Trends with R2 Score Estimation by XGBoost and Random Forest Regressor Time Series Models

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.01.004

Suman Chowdhury , Apurba Kumar Saha , Dilip Kumar Das

引用次数: 0

Predicting Institute Graduation Rate using Evolutionary Computing and Machine Learning

Procedia Computer Science Pub Date : 2025-01-01 DOI: 10.1016/j.procs.2025.01.036

Mala H. Mehta , N.C. Chauhan , Anu Gokhale

引用次数: 0