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6th International Conference on Inventive Computation Technologies, ICICT 2023

作者： Shankari, S. Hariharan, G. Kushwaha, Sumit Vidhya, K. Vijaya Kumar Reddy, R. Madan, Parul Department of Computer Science and Engineering R.M.K College of Engineering and Technology Tamil Nadu Puduvoyal601206 India Department of Artificial Intelligence and Machine Learning Malla Reddy University Telangana Hyderabad500043 India Department of Computer Applications University Institute of Computing Chandigarh University Punjab 140413 India Department of Computer Science and Engineering KPR Institute of Engineering and Technology Tamil Nadu Arasur641407 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522502 India Department of Computer Science and Engineering Graphic Era Deemed to be University Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350398496

Passive Optical Networks (PONs) are a type of fiber-optic network that use passive components, such as splitters and combiners, to distribute optical signals to multiple end-users. PONs typically consist of an Optical Line Terminal (OLT), and Optical Network Units (ONUs) or Optical Network Terminals (ONTs). PONs is also more energy-efficient and environmentally friendly than traditional networks, as they use passive components that do not require power. Overall, PONs is a cost-effective and efficient way to deliver high-speed broadband services to end-users. They offer several advantages over traditional networks and are expected to play a critical role in the deployment of next-generation broadband services. Optical code division multiple access (OCDMA) is an important candidate for implementing access control in PONs because it provides related features such as higher security and flexible on-demand bandwidth. In this context, this article presents a new method for classifying OCDMA codes using artificial neural networks, in particular Kohonen self-organizing maps (S OM), which is important for higher security of the network management system. for users, improving the quality of service (QoS). The code classification success rate was 87.5%, which justifies pattern identification methods to improve the optical code detection process. © 2023 IEEE.

关键词： Passive optical networks

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Resting-State Network Transitions in Temporal Lobe Epilepsy: Insights from MEG Based Dynamic Functional Connectivity

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IEEE Transactions on Neural Systems and Rehabilitation engineering 2025年 33卷 2286-2298页

作者： Suhas, M.V. Sinha, Sanjib Kotegar, Karunakar Ravindranadh Chowdary, M. Raghavendra, K. Asranna, Ajay Viswanathan, L.G. Mariyappa, N. Anitha, H. Manipal Academy of Higher Education Manipal Institute of Technology Department of Electronics and Communication Engineering Karnataka Manipal 576104 India National Institute of Mental Health and Neurosciences (NIMHANS) Department of Neurology Karnataka Bangalore 560029 India Manipal Academy of Higher Education Manipal Institute of Technology Department of Data Science and Computer Applications Karnataka Manipal 576104 India National Institute of Mental Health and Neurosciences (NIMHANS) MEG Research Centre Department of Neurology Karnataka Bangalore 560029 India Manipal Academy of Higher Education Manipal Institute of Technology Department of Computer Science and Engineering Karnataka Manipal 576104 India

Temporal Lobe Epilepsy (TLE), a common form of focal epilepsy, is associated with recurrent seizures originating in the temporal lobe, often leading to cognitive and psychological impairments. This study explores dynamic functional connectivity (dFC) patterns in TLE patients compared to Healthy Controls (HC) using resting-state Magnetoencephalography (MEG) data. dFC, which captures the temporal variability of brain networks, was analyzed across eight frequency bands (delta, theta, alpha, beta, low gamma, mid gamma, high gamma, and broadband) in 21 TLE patients and 21 HC. Nine dFC metrics, including state transitions, connectivity strength, network stability, and overall network movement, were derived using amplitude envelope correlations between 68 brain regions mapped to resting-state networks. Results reveal heightened variability in beta band transitions and increased entropy in delta band transitions, indicating unstable and diverse network configurations. TLE patients showed reduced dwell time in visual networks and increased dwell time in the dorsal attention network, suggesting compensatory mechanisms. Reduced connectivity in alpha and beta bands, coupled with increased variability in theta and low gamma bands, highlights widespread network instability. These findings emphasize dFC metrics as potential biomarkers for TLE, offering insights for targeted therapeutic interventions to stabilize brain dynamics. © 2001-2011 IEEE.

关键词： Dynamic functional connectivity magnetoencephalography resting state network temporal lobe epilepsy

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Enhancing Stock Price Prediction Through Integration of Astrological and Astronomical Data using XGBoost

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Procedia computer science 2025年 258卷 3970-3979页

作者： Rekha R Nair Tina Babu Kishore S Vishal S Sheila Mahapatra Department of Computer Science and Engineering Alliance School of Advanced Computing Alliance University Bengaluru India Department of Computer Applications Nitte Deemed to be university Nitte Institute of Professional Education Mangalore Karnataka India Department of Computer Applications School of Engineering Dayananda Sagar University Bengaluru India Department of Electrical and Electronics Engineering Alliance University Bangalore India

Predicting stock prices continues to be a significant challenge in the financial industry, prompting researchers to investigate various methods to improve accuracy. Hence proposed a novel approach by incorporating astrological and astronomical data into XGBoost models for stock price classification and prediction, utilizing deep neural networks (DNN). The dataset is collected from NASA JPL Horizons and Jaganath Hora software. The raw data is preprocessed with feature engineering, data cleaning, []and normalization to obtain a better forecasting stock prices and classification results. The proposed data executed with machine learning models, such as logistic regression, decision trees, random forest, SVM, XGB Classifier, LSTM, RNN, and CNN other than XGB Regressor for the forcasting. The proposed research is evaluated for classification with accuracy, precision, recall, F1 score, and forecasting with RMSE, MAE, R 2 , AIC, and BIC. The XGBoost classifier achieved 99% accuracy in predicting stock market movements, surpassing other models in determining whether the next day’s stock price would be higher or lower than the previous day’s price, while the DNN performed the best among all algorithms for overall stock price prediction. This approach could influence investment strategies, potentially affecting wealth distribution and financial market stability.

关键词： Independent Component Analysis Principal Component Analysis Probable Neural Network Expectation Maximization algorithm Natural language processing

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Effective Short Packet Communication for Resource Allocation in Wireless Powered Iot Networks 9

Effective Short Packet Communication for Resource Allocation...

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9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Selvi, K. Kanchana, D. Sankar, R. Princy, B Anni Saravanan, S. Balamurugan, A. R M K Engineering College Department of Information Techology Tamil Nadu India SRM Institute of Science and Technology Department of Computer Applications Tamil Nadu India Chennai Institute of Technology Department of Electrical and Electronics Engineering Tamil Nadu India Panimalar Engineering College Department of Computer and Communication Engineering Tamil Nadu India Jeppiaar Institute of Technology Department of Aritificial Intelligence and Data Science Tamil Nadu India

ISBN: (纸本)9798350397376

The Internet-of-things (IoT) is a technology that might be used to link huge amounts of equipment and gadgets in the future. This research uses a hybrid access point (HAP) to explore short packet communication (SPC), in which the IoT devices broadcast their short data packets to the HAP using the energy they gather. As compared to regular wireless networks, SPC has a lower transmission rate and a higher packet error rate. With unlimited block length codes, the allocation of resources in the literature is no longer the best it could possibly be. Based on a user's unique effective amount of information r, their transmission time and packet error rate are tuned to maximize or decrease total effective throughput or total transmission time. Nonconvex issues need the development of efficient ways for unacceptable solutions. These algorithms outperform current benchmarks and are on par with an exhaustive search for the optimum solution in terms of performance. © 2023 IEEE.

关键词： Resource allocation

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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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Improving Quality of Services of Fog Computing Through Efficient Work Flow Scheduling 3

Improving Quality of Services of Fog Computing Through Effic...

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3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Prasanth, G. Ravi Kumar, M. Sathish Ali, Rabei Raad Ali, Aqeel Sabah, Hawraa Ali Al-Tahee, Mustafa Hindustan Institute of Technology and Science Department of Computer Applications Tamil Nadu Chennai India National University of Science and Technology Collage of Engineering Dhi Qar Iraq Al-Farahidi University Medical Laboratory Techniques Department Iraq

ISBN: (纸本)9798350399264

A more complex technique named fog computing is aimed at resolving a number of issues with vast IoT networks. Fog Computing is likely to provide superior cloud services to mobile customers. Devices that are prone to complex computations and processing must use fog computing. Fog's major purpose is to bring data closer to users who are positioned at network edges by delivering it to specific locations. It is used when only selected data is needed to transfer to the cloud. The task of assigning workflow to the target system's devices in such a way as to provide an optimal schedule while balancing energy usage and make span minimization is known as workflow scheduling with in fog-cloud system. Workflow systems have such a wide variety of unique tasks and complex frameworks. Processing, entering, and gaining access to storage and software operations could be a part of every task. The Quality of Services (QoS) in fog computing is determined by the process of assigning resources to cloud clients, thus the cloud service provider must accomplish this as effectively as feasible. The findings have been compared to those obtained from the cloud-based model of the smart parking system, and this method is employed in iFogSim to mimic the parking system. According to the findings, the efficient parking system's proposed fog-based implementation successfully reduces latency. Furthermore, it has been discovered that, in contrast to the smart parking system's cloud-based implementation, the recommended fog-based implementation minimises total network consumption. © 2023 IEEE.

关键词： Fog computing

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Arduino UNO Based OTP Lock for Integrated Home Security System 2

Arduino UNO Based OTP Lock for Integrated Home Security Syst...

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2nd International Conference on Edge Computing and applications, ICECAA 2023

作者： Nivethika, S. Deepa Pandian, M. Senthil Parameswaran, N.G.S. Elakiya, E. Naresh, M. Dhamodharan, S. School of Computer Science and Engineering Vellore Institute Technology Chennai India School of Civil Engineering Vellore Institute Technology Chennai India V. H. N. Senthikumara Nadar College Department of Computer Applications Virudhunagar India Sri Sai Ram Engineering College Department of Electronics and Communication Engineering Chennai India Sri Sai Ram Engineering College Department of Computer and Communication Engineering Chennai India

ISBN: (纸本)9798350347579

This research study intends to enable additional security measures that may be applied in homes and workplace. The A2-way verification approach is used in the proposed system. Initially, the Electrically Erasable Programmable Read Only Memory (EEPROM) stores the security password. A randomly generated OTP is delivered through Bluetooth to the user's smartphone for two-way verification if the user inputs the proper password. The system will be opened and the necessary job may be started if the entered OTP matches. Access is banned and the user will only be given a limited number of opportunities if either the password or OTP are incorrect (3 chances in the proposed system). Major goal is to create a comprehensive home security system employing embedded (Microcontroller) technologies and wireless devices. Comparing the suggested system to the current security systems, it offers low cost service while still guaranteeing excellent security. This mechanism, which doubles as a safety lock, will be quite helpful in protecting pricey gems. © 2023 IEEE.

关键词： Bluetooth

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A Resilient Overlay for Human Emotion Recognition Using Mixed Frameworks in Machine-Human Interactions

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SN computer science 2024年第4期5卷 414页

作者： Fayaz, Fayaz Ahmad Malik, Arun Batra, Isha Ansarullah, Syed Immamul National Institute of Electronics and Information Technology (NIELIT) Jammu and Kashmir Srinagar 191132 India Department of Computer Applications Lovely Professional University (LPU) Punjab Phagwara 144402 India Department of Computer Science and Engineering LPU Punjab Phagwara 144402 India Department of IMBA North Campus University of Kashmir Kashmir Baramullah 193101 India

Human sentiments are irrational responses to things or situations associated with various physiological, behavioural, and cognitive processes. Understanding human sentiments is pivotal in diverse fields such as human–computer interaction, virtual reality, self-driving technology, and health monitoring. The scientific community is becoming more and more interested in emotion recognition. Emotion recognition, mainly through brain-generated electroencephalogram (EEG) waves, has gained significant attention in developing brain-computer interfaces. This article addresses critical research gaps in existing cross-subject emotion detection methods based on EEG signals and multi-modal techniques that combine EEG data with other modalities. We proposed an effective hybrid technique for cross-subject sentiment identification using EEG and facial movements. The suggested method combines a spectrum of directed variations and local binary pattern characteristics with spectral and statistical features from the facial dataset. The next step is classifying emotions using support vector machines, k-nearest neighbour, and ensembles. Notably, our method tackles the challenge of category imbalance using an up-sampling approach. Our results are promising after evaluating the efficiency of our proposed technique on a sentiment analysis dataset with physiological data and employing tenfold cross-validation. Notably, the accuracy levels for valence and arousal reach impressive highs at 96.25% and 97.10%, respectively. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Artificial intelligence EEG Human–computer interaction Machine learning Multimodal

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User Behaviour based Insider Threat Detection using a Hybrid Learning Approach

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Journal of Ambient Intelligence and Humanized Computing 2023年第4期14卷 4573-4593页

作者： Singh, Malvika Mehtre, B.M. Sangeetha, S. Govindaraju, Venu Centre of Excellence in Cyber Security Institute for Development and Research in Banking Technology Established by Reserve bank of India Telangana Hyderabad500057 India Department of Computer Applications National Institute of Technology Trichy Tamil Nadu Tiruchirappalli620015 India Center for Unified Biometrics and Sensors Department of Computer Science and Engineering State University of New York at Buffalo New York BuffaloNY14260 United States

Insider threats constitute a major cause of security breaches in organizations. They are the employees/users of an organization, causing harm by performing any malicious activity. Most of the existing methods to detect insider threats are based on machine and deep learning and have the following limitations: they use predefined rules or stored signatures and fail to detect new or unknown threats;they require explicit feature engineering, which results in more false positives;they require a large amount of training data, and are computationally expensive. In this paper, an improved user behavior-based insider threat detection method is proposed using a hybrid learning approach that overcomes the above limitations. It uses bi-directional long-short-term memory for feature extraction, a feed-forward artificial neural network (using distance measurements) for feature selection, and a support vector machine for classification-normal user or malicious user. The genetic algorithm’s fast global search strategy is used for the support vector machine’s initial kernel selection. Finally, alerts are generated for each user based on their combined anomaly score. The proposed method is tested using the CMU-CERT r4.2 insider threat dataset, and its performance is evaluated using the following parameters: accuracy, precision, recall, f-measure, and area under curve-receiver operating characteristic curve. The results show a significant improvement over the existing methods. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Support vector machines

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An Innovative Method for Fake News Classification using LSTM-RF Approach 4

An Innovative Method for Fake News Classification using LSTM...

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4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Yadav, Prashant S Subba Reddy, K. Ashwini, S. Santhosh, J. Nirmala Devi, M. Singh, Kamlesh Mmit Hathras Computer Science and Engineering Uttar Pradesh India Prakasam Engineering College Science and Engineering Andhra Pradesh Kandukur India Srm Institute of Science and Technology Kattankulathur Campus School of Computing College of Engineering and Technology Department of Computing Technologies Chennai India Sri Krishna Adithya College of Arts and Science Department of Computer Applications Tamilnadu Coimbatore India St.Martin's Engineering College Telangana Secunderabad India Graphic Era Hill University Department of Computer Science and Engineering Dehradun Campus Dehradun India

ISBN: (纸本)9798350300093

The dissemination of news and coverage of events, both domestically and internationally, is now mostly the responsibility of print, web, and broadcast media. Because of the explosion of information available online, verifying the veracity of claims is more difficult than ever. It is extremely difficult to verify the veracity of fake news using conventional data processing techniques without seriously misinforming the public. Indeed, the difficulty for the government and the public is a matter of case by case argument. Several sites were built specifically for this purpose, each using its own unique logic and algorithm to determine which stories were more likely to be true or false. There needs to be a system in place for verifying the veracity of online claims, especially those that garner thousands of shares and clicks before being disproved by credible sources. In this proposed approach to an LSTM-RF classifier was used to determine whether or not a news story was fabricated. The three-step process that makes up the suggested method is as follows: preprocessing;feature extraction and feature selection;and LSTM-RF model training. In preprocessing, you'll clean the data, get rid of stop words, tokenize it, and stem it. The TF-IDF and Extra Tree Classifier are used for feature selection. The model is trained with LS TM-RF. The proposed method achieves superior results compared to LSTM and RF. © 2023 IEEE.

关键词： Feature extraction

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