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检索条件"任意字段=4th International Conference on Soft Computing in Data Science, SCDS 2018"

共 1031 条记录，以下是21-30 订阅

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Proliferative Diabetic Retinopathy Detection and Progression Analysis 4

Proliferative Diabetic Retinopathy Detection and Progression...

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4th international conference on soft computing for Security Applications, ICSCSA 2024

作者： Rathinasamy, Dhivya Logesh, S. Shri Guru Varshini, S. Senthilkumari, P. PSNA College of Engineering and Technology Department of Artificial Intelligence and Data Science Dindigul India

ISBN: (纸本)9798331515720

Proliferative Diabetic Retinopathy (PDR) is a severe complication of diabetes that can lead to vision loss if not detected and managed promptly. this data analysis project aims to develop an advanced diagnostic system for PDR detection using retinal images. Going beyond conventional approaches, proposed a novel methodology that integrates multi-modal data and employs explainable artificial intelligence (AI) techniques to enhance accuracy and interpretability. the project utilizes a diverse dataset, including retinal images, patient histories, genetics, and lifestyle data. A multi-modal fusion framework extracts features capturing subtle Proliferative Diabetic Retinopathy (PDR) variations. Alongside precise diagnosis, the study introduces longitudinal analysis for PDR progression *** model not only detects PDR but also provides transparent insights into its decision-making process, empowering clinicians to validate and understand the predictions. the proposed design involves using a Convolutional Neural Network (CNN) to classify images of the retina into different stages of Diabetic Retinopathy. the model will be evaluated using the Confusion Matrix and True Value and Predicted Value Comparison. the outcomes of this project extend beyond conventional PDR detection by offering an interpretable AI-powered tool for clinicians. the system holds promise for revolutionizing PDR management, promoting early detection, and providing tailored interventions for patients. © 2024 IEEE.

关键词： Convolutional neural networks

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Integrating Cloud-based data Mining Algorithms for Smart City Infrastructure Management and Decision Support Systems 4

Integrating Cloud-based Data Mining Algorithms for Smart Cit...

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4th international conference on soft computing for Security Applications, ICSCSA 2024

作者： Guru Vimal Kumar, M. Jude Moses Anto Devakanth, J. Selvapandian, D. Venkatesan, R. Revathi, J. Aruna, R. Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Madanapalle Institute of Technology & Science Department of Computer Applications Chittoor India Academy of Higher Education Department of Computer Science and Engineering Coimbatore India Bannari Amman Institute of Technology Department of Information Technology Erode India Kongu Engineering College Department of Mathematics Erode India Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology School of Computing Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331515720

this study explores cloud-based data mining algorithm integration in elevating smart city infrastructure management and decision support systems. Specifically, the authors focus on optimizing traffic management through the analysis of real-Time traffic sensor data. Timestamped history records of vehicle count, traffic speed, density, weather condition, accidents, and traffic signals were utilized. Machine learning models such as Support Vector Machine, K-Nearest Neighbors, Decision Trees, and Random Forest were trained and tested. the analysis indicated SVM to be the most effective instrument, resulting in an accuracy of 98.98%. the model was also instrumental in reducing vehicle count by 16.7%, traffic density by 25%, and traffic speed was increased by 50%. KNN's overall accuracy experience was 94.5%, and the model was shown to be highly optimized for various forever traffic conditions. However, the overall impact on traffic speed and density was not as significant compared to the previously-discussed model. DT and RF demonstrated respective accuracies of 92.3% and 91.2%, offering varying levels of decision interpretation and ensemble learning opportunities. As a result, the study indicates that machine learning can play a critical role in improving urban traffic management via traffic congestion prediction and minimization, resulting in improved traffic flow, overall trip time reduction, and general urban mobility management. T. © 2024 IEEE.

关键词： Random forests

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Robust Statistical Models for Identifying Inauthentic Online Reviews 4

Robust Statistical Models for Identifying Inauthentic Online...

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4th international conference on soft computing for Security Applications, ICSCSA 2024

作者： Swapna, D. Shanmuga Sundari, M. Khadhyothi Sreeja, G. Sriya Varma, S. Rajeshwari, M. Department of Computer Science and Engineering BVRIT HYDERABAD College of Engineering for Women Hyderabad India

ISBN: (纸本)9798331515720

Review authenticity is a key factor in determining trustworthiness in the internet marketplace. However, the threat of fake reviews remains constant, endangering both fair competition and customer confidence. the rise of fake reviews undermines the credibility of these platforms and poses a significant challenge to users seeking genuine feedback. In response, this study proposes a machine learning (ML) approach for detecting fake *** research begins by collecting a dataset comprising both genuine and fake reviews from various online platforms. Features are extracted from the textual content of these reviews, including linguistic characteristics, sentiment analysis, and meta-data such as review length and rating distribution. Several ML models are employed for classification, such as Naive Bayes, Random Forest, and Support Vector Machines. these algorithms are assessed using measures like accuracy, precision, recall, and F1-score after being trained on the dataset. In addition, methods like hyperparameter tuning and cross-validation are used to improve the performance of ***, the findings suggest that ML-based approaches hold promise for mitigating the problem of fake reviews, thereby enhancing the reliability of online review platforms and empowering consumers to make informed decisions. © 2024 IEEE.

关键词： Adversarial machine learning

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Analysis of Cardiac Disease based on AI Prediction Techniques using data analytics approach 4

Analysis of Cardiac Disease based on AI Prediction Technique...

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4th international conference on soft computing for Security Applications, ICSCSA 2024

作者： Sivanathbabu, R. Kamalakkannan, S. School of Computing Sciences Department of Computer Science Chennai India School of Computing Sciences Department of Information Technology Chennai India

ISBN: (纸本)9798331515720

Patients who have an increased likelihood of coronary heart disease can reduce their consequences by changing their lifestyle, with the support of early diagnosis. Healthcare expenses are rising above, both company budgets and the average cost of medical treatment nationally, due to asymptomatic conditions like cardiovascular disorders. Early detection and treatment of these illnesses are essential. Utilizing the data that is accessible in healthcare systems has been significantly impacted by recent technological advancements. As part of this, patients' Heart Disease (HD) may be predicted using the massive amount of data. However, due to concerns with prediction and diagnosis performance and accuracy, projecting HD has emerged as one of the main obstacles facing the healthcare sector. the main challenges were to apply homogeneous data mining techniques to define the most significant causes of heart disease and to accurately predict the overall risk. Using data Analytics (DA) to speed up the prediction process is one way to make use of massive amounts of health care data. Selecting the optimal analytical methods, however, is the most crucial step as it will impact the final result of the diagnostic and forecasting. In order to assess the effectiveness of the methods for HD forecasting, this study intends to implement DA approach in collecting of databases. In this research work, Best First Search (BFS) has been used to identify the most important attributes for HD prediction. this study employs a range of ML approaches based on AI to predict HD. the Random Forest (RF) method, which uses specific characteristics, has the highest accuracy rate (98.63%) in this experiment. the BFS extracted 14 relevant features and then compared them using AI approaches. Based on the analysis, feature selection has enhanced the DA technique by improving the efficiency and precision of the classification model's prediction. © 2024 IEEE.

关键词： Prediction models

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Improved Alzheimer's Disease Early Prediction using Machine Learning Method : A Comprehensive Diagnostic Approach with Random Forest Classifier 4

Improved Alzheimer's Disease Early Prediction using Machine ...

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4th international conference on soft computing for Security Applications, ICSCSA 2024

作者： Munawar, Madiha Monika Singh, T. Kishor Kumar Reddy, C. Hanafiah, Marlia Mohd Stanley College of Engineering and Technology for Women Department of Computer Science and Engineering Hyderabad India Universiti Kebangsaan Malaysia Faculty of Science and Technology Malaysia

ISBN: (纸本)9798331515720

Alzheimer's Disease (AD) often affects the elder persons and is the prevalent kind of Dementia. AD has a huge expense, especially when it comes to the treatment. AD is a main reason the older generations die whereas when Alzheimer Disease is detected early there will be minimal deaths and accurate diagnosing likely occurs in most cases. Using anonymized Oasis longitudinal MRI data, the study trains several machine learning algorithms to identify AD patients. Analyzing the test result includes utilizing different learning strategies or algorithms e.g., Ensemble Random Forest (RF), Hierarchical Decision Tree (DT), Logistic Regression (LR), Convolutional Neural Network (CNN), Deep Neural Network binary classifier (DNN), soft voting classifier (SVC), transfer learning approaches (TLA), Support Vector Machine (SVM) and boosting AdaBoost in reference of their Accuracy, Error Rate, AUC (area under the curve), Time Taken and Recall for each method. the obtained results concluded that the proposed Random Forest classifier achieves higher accuracy (92.84%), Error Rate (7.16%), AUC (95%) (Area Under Curve) and Recall (89%). the Random Forest classifier also achieves higher Time Taken that is 483.654 sec. © 2024 IEEE.

关键词： Alzheimer's Disease Decision Trees Deep Learning Dementia Learning Approaches Machine Learning Random Forest

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Fuzzy Logic-based Energy Management for WSNs in Smart Grid Applications 4

Fuzzy Logic-based Energy Management for WSNs in Smart Grid A...

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4th international conference on soft computing for Security Applications, ICSCSA 2024

作者： Vengatesan, K. Vijayalakshmi, R. Samee, Sayyad Chopade, Neha Mahalle, Shilpa Bashkaran, K. Department of AIDS Sanjivani University Kopargaon India Department of Computer Science and Engineering Velammal College of Engineering and Technology Madurai India Dept.of Computer Science Al Sadara School Al Ain Abu Dhabi United Arab Emirates SIES College of Management Studies India Kongunadu College of Engineering and Technology Thottiam India

ISBN: (纸本)9798331515720

Fuzzy Logic-Based Energy Management (FLBEM) is an innovative approach for optimizing energy consumption in Wireless Sensor Networks (WSNs) utilized in Smart Grid applications. this technique employs fuzzy logic principles to dynamically adjust network parameters, such as transmission power and data sampling rates, to enhance energy efficiency while maintaining reliable communication. By intelligently adapting to network conditions and environmental factors, FLBEM extends the operational lifetime of sensor nodes and ensures the stability of the WSN, thus facilitating real-Time monitoring and control of Smart Grid systems. the proposed FLBEM approach encompasses a comprehensive framework that includes the design of fuzzy logic controllers, the development of energy-Aware routing protocols, and the integration of renewable energy sources for powering sensor nodes. Simulation results demonstrate significant improvements in energy efficiency, network lifetime, and overall performance, validating the effectiveness of the FLBEM strategy in the context of Smart Grid applications. As energy management remains a critical concern for WSNs, the implementation of FLBEM offers a promising solution to address the challenges of energy conservation and sustainability in modern power systems. © 2024 IEEE.

关键词： Smart power grids

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Brain Connectivity Analysis in Alzheimer's disease using Graph Convolutional Network 4

Brain Connectivity Analysis in Alzheimer's disease using Gra...

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4th international conference on soft computing for Security Applications, ICSCSA 2024

作者： Ango, Rithika Kishor Kumar Reddy, C. Fatima, Shugufta Nag, Anindya Stanley College of Engineering & Technology for Women Department of Computer Science and Engineering Hyderabad India Khulna University Bangladesh Department of Computer Science and Engineering Khulna Bangladesh

ISBN: (纸本)9798331515720

Alzheimer's disease (AD) can cause a lapse in cognitive capacity along with forgetfulness, and addressing this issue is vital for forthcoming treatment efforts. this study offers another approach to investigating brain links in Alzheimer's disease with Graph Convolutional Networks (GCN). the GCN technology enables more appropriate utilization of the connections topology that is constructed by brain networks based on fMRI data. the purpose of this paper is to design brain connection networks utilizing modern computational technology. Furthermore, this research interprets the model's predictions using layer-wise relevance propagation, focusing on key brain regions and connections involved in Alzheimer's disease. this paper uses the ADNI dataset for early predictions of AD. the metrics in which the model suggested outperformed the available approaches are, the accuracy is 88.90%, error rate is 11.10% while its precision reached 91, sensitivity of 88 and specificity 89 under NC vs AD;the accuracy of 87.80%, error rate of 12.20% while its precision reached 89, sensitivity of 87 and specificity 88 under NC vs MCI;accuracy is 86.50%, error rate is 13.50% while its precision reached 87, sensitivity of 86 and specificity 87 under AD vs MCI. Hence this work gives a valuable tool for studying brain connections, assisting in the early detection and understanding of AD. © 2024 IEEE.

关键词： Neurodegenerative diseases

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Machine Learning Algorithms used in Crop Yield Prediction for Precision Agriculture 4

Machine Learning Algorithms used in Crop Yield Prediction fo...

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4th international conference on soft computing for Security Applications, ICSCSA 2024

作者： Umamaheswari, R. Shanmugapriya, S. Vimala, P. Ganesan, R. Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Vel Tech High Tech Dr.Rangarajan Dr. Sagunthala Engineering College Department of Computer Science and Engineering Chennai India Saveetha Engineering College Department of Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798331515720

Agriculture is a crucial sector that plays a vital role in producing food for human consumption. However, predicting crop yield accurately can be a challenging task due to various natural factors, such as soil quality, climate, rainfall, and resource availability. In the past, farmers relied on their expertise and judgment to determine crop yield, but their traditional methods have become less effective due to the unpredictable nature of these factors. To address this challenge, researchers have developed various algorithms and models for predicting crop yield based on different parameters, such as soil quality, climate, and image analysis. these algorithms can help farmers make informed decisions about when and where to grow their crops, leading to improved productivity and higher yields. Among these algorithms, neural networks are showing great promise as they can analyze vast amounts of data and identify patterns that may not be immediately apparent to human observers. By training neural networks on historical crop yield data, researchers can develop models that accurately predict crop yield based on a variety of factors. Ensuring accurate crop yield prediction is critical to ensuring food security and preventing famine, particularly in developing countries such as India. As technology continues to advance, we can expect even more advanced algorithms and models for predicting crop yield that will help farmers make better decisions and ultimately lead to increased productivity and better outcomes for agriculture. © 2024 IEEE.

关键词： Agriculture Crop yield prediction (CYP) Machine learning Neural Network (NN)

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Autonomous Robot Navigation in Unknown Terrains using Generative Adversarial Networks 4

Autonomous Robot Navigation in Unknown Terrains using Genera...

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4th international conference on soft computing for Security Applications, ICSCSA 2024

作者： Chitra, P. Sridar, K. Govindan, Sudha Britto Raj, S. Akshya, J. Choudhry, Mani Deepak Sundarrajan, M. St Joseph's Institute of Technology Department of Artificial Intelligence and Data Science Chennai India Veerammal Engineering College Department of Computer Science and Engineering Dindigul India Sona College of Technology Department of Computer Science and Engineering Salem India RRASE College of Engineering Department of Computer Science and Engineering Chennai India SRM Institute of Science and Technology Department of Computational Intelligence Chennai India SRM Institute of Science and Technology Department of Computing Technologies Chennai India SRM Institute of Science and Technology Department of Networking and Communications Chennai India

ISBN: (纸本)9798331515720

Autonomous Robot Navigation in unknown and dynamic environments is considered one of the grand challenges in robotics. these motions that guide it span from rescue operations to planetary exploration. Classical methods for path planning, including A*, Dijkstra, and RRT, were unable to balance the efficiency and adaptability of motion planning over complex terrain. this may have a powerful solution using Generative Adversarial Networks (GANs) with advanced machine learning techniques to improve the ability of robotic navigation. this study proposes a GAN-based approach for designing an effective self-adaptive path planning and obstacle avoidance system for autonomous robots. Despite performing well in structured environments, traditional techniques normally fail in analysing the unstructured terrains with real-time adjustments and dynamic changes due to the high computational costs and lack of flexibility involved. In this regard, the proposed GAN-based system resolves these issues by rating navigation paths concerning safety and efficiency. Quantitative analysis demonstrates that within a forest, desert, urban, or off-road environment, the computational times by using the developed strategy turn out to be 0.12 seconds, 0.10 seconds, 0.14 seconds, and 0.13 seconds, respectively, behaving much lower than what can be achieved with other algorithms. this approach proves much superior in terms of computational efficiency path smoothness and adaptability under different environmental conditions in applications related to autonomous robot navigation systems. © 2024 IEEE.

关键词： Generative adversarial networks

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Enhanced Bone Cancer Detection in MRI Scans via a Hybrid Quantum-Classical Convolutional Neural Network and War Strategy Optimization 4

Enhanced Bone Cancer Detection in MRI Scans via a Hybrid Qua...

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4th international conference on Ubiquitous computing and Intelligent Information Systems, ICUIS 2024

作者： Harshavardhan, A. Sudalaikani, S. Saravanakumar, M. Saritha, S. Rao, Pothamsetty Kasi V. Vnr Vignana Jyothi Institute of Engineering and Technology Department of Computer Science and Engineering Telangana Hyderabad India Mvj college of Engineering Whitefield Department of Mathematics Karnataka India Nehru Institute of Engineering and tech Department of Artificial Intelligence & Data Science Tamil Nadu Coimbatore India Immaculate College for Women Department of Computer Science and Engineering Tamil Nadu Cuddalore India Koneru Lakshmaiah Education Foundation Department of Mechanical Engineering Andhra Pradesh Vaddeswaram India

ISBN: (纸本)9798331529635

Bone cancer is a relatively rare and aggressive form of cancer that originates in the bones and the adjacent soft tissues;the disease is characterized by marked morbidity and mortality. Frequently, early detection is possible, and such medical conditions should be treated to enhance the result of the therapy. MRI is very useful in diagnosing bone cancer since they give clear images of bones and soft tissues. In this work, a new method of bone cancer detection from MRI images is presented with unsharp structure guided filtering for preprocessing and a CSwin transformer to extract features from the preprocessed images. this method further improves visualization and selection of MRI images by improving the representation of features in the image. the main part of the designed method is the Hybrid Quantum-Classical Convolutional Neural Network with War Strategy Optimization (HQCCNN-WSO), which combines the fundamentals of quantum computing and traditional neural networks to enhance the classification performance. Based on the parameters of the War Strategy Optimization algorithm, the model distinguishes between cancerous lesions and healthy bone tissue, and the parameters are further refined for improved performance. the suggested approach demonstrates remarkable accuracy and dependability in the identification of bone cancer, achieving over 99% across all performance criteria. © 2024 IEEE.

关键词： Convolutional neural networks

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