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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： Arockiya Selvi S T. Kamalakannan Department of Computer Science Vel's Institute of Science Technology and Advanced Studies Chennai Tamil Nadu India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Parkinson's disease(PDs) is a neurological disorder characterized by accidental or uncontrollable movements such as shaking, rigidity, and trouble with balance and coordination. Symptoms are usually minor at first and intensify over time. PD datasets tend to be compact and inadequately diversified. Several datasets contain incorrect values or discrepancies as a result of diagnostic procedure inconsistencies. Datasets frequently have fewer PD patients than healthy individuals, resulting in biased models. SVM is a common method for PD prediction due to its ability to handle tiny, high-dimensional data sets and its robust classification skills. SVM performs effectively even with small medical datasets, unlike deep learning models, which require enormous volumes of data. SVM minimizes overfitting, particularly when working with tiny datasets, making it more dependable for use in real-world medical settings. The study employed SVM algorithms to predict Parkinson's disease. Additionally, disease data is classified and compared using SVM with different kernel parameters. The study compares the performance of four alternative SVM models Linear SVM (LSVM), Quadratic SVM (QSVM), Cubic Gaussian SVM (CGSVM), and Medium Gaussian SVM (MGSVM) using five assessment metrics: accuracy, sensitivity, specificity, precision, and F1-Score. MGSVM is the most effective model for predicting PDs, with the highest precision and consistency.

关键词： Support vector machines Deep learning Sentiment analysis Accuracy Sensitivity Parkinson's disease Predictive models Prediction algorithms Classification algorithms Medical diagnostic imaging

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A prognostic thyroid disorder analysis based on thyroid test measure with ECG 1

A prognostic thyroid disorder analysis based on thyroid test...

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1st IEEE International Conference on Advances in Electrical, computing, Communications and Sustainable Technologies, ICAECT 2021

作者： Deepika, M. Department of Computer Science School of Computing Science Vels Institute of Science Technology and Advanced Studies Tamil Nadu Chennai India

ISBN: (纸本)9781728157900

Clinically the most prevailing relevant findings explaining the hyperthyroidism as well as the hypothyroidism are the anatomic symptoms of Thyroid disease. The concerned with cardiovascular system of cellular pathway for thyroid hormones will display changes to cardiac rate, heart contractility, blood pressure, vascular resistance and rhythms associated to thyroid dysfunction. The instruction to identify post - treatment and diagnostic methods required for the recovery of people with diagnosed hypothyroidism, the related thyroid dysfunction criteria are described in this article. Atrial fibrillation is a common type of heart disease. This paper looked at the threat of arrhythmia in thyroid problems verified patients aged 20 to 89 years, respectively. In a limited number of patients chosen, the interaction between hyperthyroidism and Atrial fibrillation has until now been evaluated. The report also identifies differences of tyrosine levels affecting chronic cardiac failure and the co-ordination of thyroid disease interventions in provisional modification situations. The results are obtained in all patients with a UCI hyperthyroidism event with logistic regression analysis for the estimation of odd ratios between sex, age group, cardiovascular disease and atrial or flutter-like effect. © 2021 IEEE

关键词： Blood pressure

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Score-Level Fusion Approach for Traffic Flow Prediction in Intelligent Transportation System

Score-Level Fusion Approach for Traffic Flow Prediction in I...

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Mobile computing and Sustainable Informatics (ICMCSI), International Conference on

作者： Axcellin. T S. Kamalakkannan Department of Computer Science Vel's Institute of Science Technology and Advanced Studies Chennai Tamil Nadu India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

Traffic flow prediction (TFP) for intelligent transportation system is essential for effective transportation planning and city development. Conventional models often have difficulty with the complex nature of traffic data, which includes nonlinear patterns, spatial dependencies, and external influences such as weather or road conditions. Prediction accuracy may be significantly increased by utilizing an ensembling strategy, which combines the capabilities of many models. The goal of the study is to improve TFP accuracy by integrating the results of different machine learning (ML) models. The study uses Random Forest(RF) Naïve Bayes(NB), K-Nearest Neighbor(KNN), Extreme Gradient Boosting(XGBoost) and Support vector machine(SVM) as prediction models and based on the prediction probabilities and the score level fusion is performed with RF and XGBoost ensembling. The new fusion procedure uses a weighted average technique in which weights are assigned dynamically depending on individual model performance criteria including accuracy, precision, recall, and F1-score. Higher-performing algorithms are given greater significance in the final forecast, resulting in a dynamic and data-driven assembly strategy. The findings show that the efficiency of the ensemble of RF-XGBoost is higher compared to the contrasted methods. The model also uses feature selection approaches like mutual information to find the most significant traffic-related characteristics, lowering computational cost and increasing forecast efficiency.

关键词： Radio frequency Accuracy Computational modeling Heuristic algorithms Transportation Forestry Predictive models Feature extraction Vectors Data models

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Machine Learning Models for Android Malware Detection–A Comparative Study

Machine Learning Models for Android Malware Detection–A Com...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： Arun N T.R. Nisha Dayana Department of Computer Science Technology and Advanced Studies Vel's Institute of Science Chennai Tamil Nadu India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

In the mobile ecosystem, Android apps are expanding quickly, but malicious software for Android is also proliferating in ever-increasing quantities. The issue of identifying malware on Android has been examined by several researchers, who have proposed ideas and approaches from various angles. According to current research, machine learning (ML) is a promising and successful method of detecting Android mal ware. This study is performed to compare the various ML methods for Android malware detection, including Random Forest (RF), Decision Tree,(DT) Random Tree(RT), Support Vector Machine (SVM), and XGBoost. The study assessed these models using a Androzoo dataset of Android apps, taking into account the important performance parameters as detection latency, accuracy, precision, and false alarm rate (FAR).

关键词： Support vector machines Radio frequency Accuracy Runtime Biological system modeling Static analysis Malware Vectors Robustness Decision trees

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Emotional Contribution to Parkinson's Disease Diagnosis Using Machine Learning for EEG Dataset Generation

Emotional Contribution to Parkinson's Disease Diagnosis Usin...

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Mobile computing and Sustainable Informatics (ICMCSI), International Conference on

作者： S. Arockiya Selvi T. Kamalakannan Department of Computer science VEL'S Institute of Science Technology and Advanced Studies Chennai Tamil Nadu India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

Emotion is the root cause of nearly all harmful illnesses in humans. Emotion affects blood pressure, heart rate, kidney function, and a few disorders of the nervous system. Parkinson's disease, which causes impairment of movement, is one of the brain disorders. This issue is caused by a reduction in dopamine release in the cortex of the brain. Dopamine production deviations have been related to essential hypertension. Dopamine has a crucial role in anxiety modulation in multiple regions of the brain. Patients with Parkinson's disease frequently indicate that acute stress worsens their motor symptoms, especially gait freezing, dyskinesia, and tremors and the emotions play a substantial role in Parkinson's disease. This study focuses on the Classification Learner algorithms and the Electro Encephalogram (EEG) dataset, both of which covered the diagnostic range of PD development. The classifiers were evaluated using key performance indicators such as error rate, accuracy, sensitivity, specificity, and precision. The findings illustrate the convergence of emotional analysis, machine learning (ML), and neuroscience in improving diagnostic capabilities by demonstrating how a combination of EEG datasets with KNN algorithms offers a reliable and accurate approach for early Parkinson's disease identification.

关键词： Machine learning algorithms Accuracy Sensitivity Parkinson's disease Error analysis Machine learning Nearest neighbor methods Electroencephalography Classification algorithms Reliability

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Node Mobility and Encounter Rate Metrics to Enhance Stability in MANET 2

Node Mobility and Encounter Rate Metrics to Enhance Stabilit...

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2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Rajaram, Gnanajeyaraman Archana Vishveswari, R.S. Deepa, R. Packialatha, A. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Applied Machine Learning Tamil Nadu Chennai India Varuvan Vadivelan Institute of Technology Department of Computer Science and Engineering Tamil Nadu Dharmapuri India Vels Institute of Science Technology & Advanced Studies Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350325799

Mobile Ad hoc Networks (MANETs) have been broadly functional in a wide variety of scenarios, for example, in disaster recovery, health care, video conferencing, and battlefield transmissions. MANET is an independent and peer-to-peer network which builds a chain of nodes to enable data transmission from sender to receiver. Hop count is measured as the most significant metric in MANET. Though several metrics have been introduced as the replacement for the hop count metric, there is a need for outperformance in mobile scenarios. The node mobility will affect the link stability, and it creates congestion. To solve these issues, a Node Mobility and Encounter Rate metrics (NMER) to enhance the stability of the network. It selects the route based on the node mobility and Encounter Rate (ER) metrics. The congestion ingredient measures the congestion level in the MANET. The objective of this route has the least cost for forward data packets and minimizes congestion. The NMER approach simulation results demonstrate that the NMER approach minimizes the packet loss ratio and reduces the network delay. © 2023 IEEE.

关键词： Mobile ad hoc networks

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Deep Learning Model with Optimization Strategies for DDoS Attack Detection in Cloud computing

Deep Learning Model with Optimization Strategies for DDoS At...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： Radhika. P S. Kamalakkannan Department of Computer Science Vel's Institute of Science Technology and Advanced Studies Chennai Tamil Nadu India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Cloud computing (CC) remains as a chief research area for analysts because of its numerous applications and advantages. The dispersed nature of CC and its dependence on internet service poses several attacks and challenges. Mainly, the attacks like DDoS attempts to disrupt the online operations. Insider attacks cannot be detected using conventional detection techniques like firewalls. This study suggests a DDoS detection method for reducing the DDoS activity. The proposed work contributes under big data perspective by handling certain procedure for detection process. Data Generation phase is the initial step, and according to the work, DDoS dataset is considered under big data perspective. As the work is considered under the big data perspective, Map reduce (MR) framework is used, Mapper handles the data and process the feature extraction, which includes the extraction of raw features, Packet feature extractor, Improved Correlation and statistical Feature. Reducer provides the combined feature set. From the extracted feature set, appropriate features are selected via Weightage based Improved hybrid model combining the models like Long short term memory(LSTM) and Deep Maxout(DMO) networks is used. The weights of LSTM and DMO are optimally chosen using White Shark-Remora Optimization (WSROA) algorithm. Recursive Feature Elimination (RFE) process is used to precisely select the features for DDoS attack detection. The findings shows that the proposed hybrid bio-inspired algorithm outperforms with an accuracy of 94 % compared to LSTM, Convolutional Neural Networks(CNN) and Deep Belief Networks(DBN)

关键词： Cloud computing Biological system modeling Computational modeling Big Data Feature extraction Denial-of-service attack Data models computer crime Long short term memory Optimization

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Quantum Algorithms and Lower Bounds for Finite-Sum Optimization 41

Quantum Algorithms and Lower Bounds for Finite-Sum Optimizat...

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41st International Conference on Machine Learning, ICML 2024

作者： Zhang, Yexin Zhang, Chenyi Fang, Cong Wang, Liwei Li, Tongyang School of Electronics Engineering and Computer Science Peking University China Computer Science Department Stanford University United States National Key Lab of General Artificial Intelligence School of Intelligence Science and Technology Peking University China Institute for Artificial Intelligence Peking University China Center on Frontiers of Computing Studies Peking University China School of Computer Science Peking University China

Finite-sum optimization has wide applications in machine learning, covering important problems such as support vector machines, regression, *** this paper, we initiate the study of solving finite-sum optimization problems by quantum ***, let f1, ..., fn : d → be -smooth convex functions and ψ: d → be a µ-strongly convex proximal *** goal is to find an ϵ-optimal point for F(x) = n1 Pni=1 fi(x) + ψ(x).We give a quantum algorithm with complexity Õ(Equation presented) 1 improving the classical tight bound (Equation presented).We also prove a quantum lower bound Ω˜(n + n3/4(/µ)1/4) when d is large *** our quantum upper and lower bounds can extend to the cases where ψ is not necessarily strongly convex, or each fi is Lipschitz but not necessarily *** addition, when F is nonconvex, our quantum algorithm can find_an ϵ-critial point using (Equation presented) queries. Copyright 2024 by the author(s)

关键词： Quantum computers

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An Effective Diagnosis of Alzheimer's Disease with the Use of Deep Learning based CNN Model 7

An Effective Diagnosis of Alzheimer's Disease with the Use o...

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7th International Conference on Intelligent computing and Control Systems, ICICCS 2023

作者： Sharmili, K.C. Suja, G.P. Pandian, E. Walid, Md. Abul Ala Arunachalam, Sripriya Babu, G.Charles Computer Science Stella Maris College 17 Cathedral Road Chennai India Computer Science Muslim Arts College Thiruvithancode629174 India Sri Muthukumaran Institute of Technology Mangadu Master of Computer Application Chennai69 India Department of Computer Science and Engineering Khulna9203 Bangladesh Computer Applications-PG School of Computing Sciences VELS Institute of Science Technology & Advanced Studies - VISTAS Velan Nagar P.V. Vaithiyalingam Road Pallavaram Chennai600 117 India GRIET Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350397253

In recent years, Alzheimer's disease has become a major health concern. Over 45 million individuals worldwide suffer from this illness. Alzheimer's disease is a neurodegenerative illness of unidentified etiology and process that causes cognitive deterioration, and it mostly affects the elderly. Dementia, the gradual and irreversible loss of brain cells, is the leading cause of Alzheimer's disease. Those with this illness lost the ability to think, read, and do other basic tasks. A machine learning system may aid in this situation by making accurate predictions about the onset of illness. Dementia diagnosis in a broad patient population is the major objective. This research presents the findings and analysis of many Machine Learning models for identifying dementia. To alleviate this obstacle and help in the diagnosis of AD will create a deep-learning architecture that uses stacked auto-encoders and a softmax output layer. The proposed technique can analyze numerous classes in a single setting, unlike earlier procedures, and only needs a small number of labeled training samples and basic domain expertise to get started. In the present studies, a substantial improvement in performance when it came to classifying all diagnostic subgroups. Using the proposed technique can run these time series through a Convolutional Neural Network (CNN) and ResNet50 model to extract the distinguishing patterns across stages. With an F1-score of 0.99 and an accuracy of 99.91 percent, the CNN-based technique outperformed the standard feature-based classifiers by a significant margin. © 2023 IEEE.

关键词： Convolutional neural networks

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Stimulating creativity and grit of high school students with creative STEM activities: an RCT with noncompliance

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Quality and Quantity 2024年第1期59卷 605-634页

作者： Ballerini, Veronica Dominici, Alice Ferracane, Martina Francesca Menchetti, Fiammetta Noirjean, Silvia Department of Statistics Computer Science Applications “G. Parenti” University of Florence Florence Italy Department of Economics European University Institute Fiesole Italy Dondena Centre for Research on Social Dynamics and Public Policy Bocconi University Milan Italy School of Computing Engineering & amp Digital Technologies Teesside University Middlesbrough United Kingdom Robert Schuman Centre for Advanced Studies European University Institute Fiesole Italy

Creativity and grit are widely recognized as vital components for the success and prosperity of individuals, even more so for younger people who will have to deal with the complex challenges connected to the digital era. Yet, whether these skills can be learnt or are innate traits is still subject to debate, which is further complicated by the difficulty of defining and, in turn, assessing these concepts. In this study, we challenge the idea that creativity and grit cannot be learnt and show that creative pedagogy activities, such as those offered by FabLabs, are powerful tools to enhance these non-cognitive skills. We conduct a randomized controlled trial in which 710 students from five Italian high schools are randomly assigned to creative STEM courses. The courses are delivered by FabLabs, small-scale workshops that offer access to tools for digital fabrication and employ a hands-on pedagogical approach expected to impact students’ creativity and grit positively. Assignment to the courses is at the class level: only students in classes randomly selected to join the Fablab activities can participate, and they may decide whether to enrol or not on a voluntary basis. We address noncompliance by adopting an instrumental variable approach. We use two modalities to measure creativity: a self-assessment through the Short Scale of Creative Self and an assessment made by an independent expert using an index of creativity developed by the authors. While the effect on self-assessed creativity is not significant, results show that the external, independent assessment of students’ creativity can capture a significant effect of Fablabs’ courses. It also emerges that FabLab activities have a positive significant effect on students’ grit, measured through the Duckworth scale. The encouraging results obtained for a relatively small sample of students should prompt a replication of the experiment on a broader scale. © The Author(s) 2024.

关键词： Causal inference Creative pedagogy FabLabs Instrumental variables Noncompliance

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