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Efficient Method for Personality Prediction using Hybrid Method of Convolutional Neural Network and LSTM 4

Efficient Method for Personality Prediction using Hybrid Met...

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

作者： Sujatha, N. Pramod, S. Bhatla, Shruti Thulasimani, T. Kant, Ravi Chauhan, Amit B. M. S. College of Engineering Department of Mathematics Karnataka Bengaluru India Nitte Meenakshi Institute of Technology Yelahanka Bangalore Department of Mathematics Karnataka India Graphic Era Hill University Department of Computer Science and Engineering Dehradun India Bannari Amman Institute of Technology Sathyamangalam Department of Mathematics Tamilnadu Erode India Shoolini University Cse Department Himachal Pradesh Solan India Department of Life Sciences Karnataka Bengaluru India

ISBN: (纸本)9798350300093

Users' contributions and the emotions conveyed in status updates may prove invaluable to studies of human behavior and character. A number of other research have taken a similar approach, and the field itself is still growing. The goal of this proposed is to create a technique for deducing a user's personality traits based on their social media profiles. Among the many customer services now available on SNSs are media and recommendations of user involvement. The need to give internet users with more specialized and customized services that meet their specific requirements, which sometimes depend heavily on the users' inner personalities, is significant. However, there hasn't been much work done on the psychological analysis that's needed to deduce the user's inner nature from their outward activities. In this instance, LSTM-CNN was fed pre-processed and vectorized text documents. SNF is used for feature extraction. The proposed method employs CFS for the purpose of Feature Selection. Finally, LSTM-CNN was used to train the model. While CNN is good at extracting features that are independent of time, LSTM is better at capturing long-term dependencies. combination of features for personality prediction, the LSTM-CNN model is superior to the individual models. © 2023 IEEE.

关键词： Forecasting

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The Application of Machine Learning Algorithms to the Classification of EEG Signals

The Application of Machine Learning Algorithms to the Classi...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Sasikumar Gurumoorthy Saritha Avikkal Rani M K Radha Rajesh N Department of Computer Science and Engineering J.J.College of Engineering and Technology Trichy India Department of Computer Science & Engineering GITAM School of Technology GITAM University India Department of Electronics and Communication Engineering Hyderabad Institute of Technology and Management Telangana India Department of Computer Science & Engineering K L University Vadeeshwaram Vijayawada India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India

In order to study cognitive states and detect deficiencies in thinking, planning, and judgement, automated cognitive assessment systems have been developed. Having access to the opportunity to collaborate with experts in their working environment is another benefit of these platforms, particularly in services where decisions have direct effects on people's lives. This dissertation assesses the brain's reaction to mental tasks including motor imagery, attention, working memory, mental fatigue, and attention deficit hyperactivity disorder (ADHD). Electroencephalography (EEG), which encodes neuron activity as action potentials, is a popular method for analyzing these cognitive functions. These EEG data are processed using state-of-the-art machine learning algorithms, automating cognitive ability testing in the process. Recent state-of-the-art approaches have shown problems with high dimensionality, irrelevant channels, subject and task variability, generalizability, and interpretability. Because of this, classifications of intelligence are often inaccurate. The use of meta-heuristics to the solution of both continuous and binary optimization problems was found to be very fruitful in this scenario. Despite increased performance in EEG signal processing for other applications, these methods have seen very little exploration in the field of cognitive evaluation. This encourages more research into these meta-heuristics approaches to intelligence testing. It is important to look for the most appropriate optimization strategy for a certain task, since all of them will not work in every circumstance. In order to address problems in cognitive testing, this research looks on parameter-free optimization algorithms that are tailored to each kind of test. One of the main problems that hinders the effectiveness of ML models is its dimensionality. Another is its lack of generalizability. The overarching goal of this proposed work is to build and construct an effective channel select

关键词： Visualization Machine learning algorithms Metaheuristics Brain modeling Feature extraction Electroencephalography Task analysis

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Hierarchical Federated Learning-Based Method for privacy-preserving in the Healthcare Environment

Hierarchical Federated Learning-Based Method for privacy-pre...

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Evolutionary Algorithms and Soft Computing Techniques (EASCT), International Conference on

作者： Manoj Kumar D P Ananda Babu. J Jaladi NagaSireesha Bhattu Venkatesh Ayesha Siddiqua Department of Computer Science and Engineering Kalpataru Institute of Technology Tiptur India Department of Information Science and Engineering Malnad College of Engineering Hassan India Malla Reddy Engineering College for Women Autonomous India Department of Computer Science and Engineering (Data Science) CMR Technical Campus Hyderabad India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India

The COVID-19 pandemic has led to increase the demand for medical equipment with internet of things (IoMT) capabilities forcing doctors to diagnose and treat patients remotely. This has increased awareness of health, leading to a surge in purchasing of IoMT-capable medical equipment. However, medical data is sensitive and valuable on the dark web, making it vulnerable to cybercriminals. Due to limited storage and processing capabilities of network devices, system administrators struggle to enhance security measures. This work aims to investigate rapidly spreading attacks before they compromise wellness systems. Hierarchical federated learning is (HFL) proposed using a model built on Dew-cloud, which offers improved data privacy due to the increased availability of IoMT critical applications. Dew servers with distributed architecture and a cloud computing-backed backend implement hierarchical long-term memory (HLSTM) method. The implemented HFL-HLSTM method’s performance is evaluated using NSL-KDD dataset. Implemented method achieved better performance than the existing intrusion detection methods including MiTed, LS2DNN, and MetaCID and the metrics are 98.62% of f-measure, 99.53% of accuracy, 99.21% of precision, and 99.10% of recall.

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Multi-Scale Feature Fusion Module for Change Detection in Remote Sensing Images

Multi-Scale Feature Fusion Module for Change Detection in Re...

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Integrated Intelligence and Communication Systems (ICIICS), International Conference on

作者： Parameshachari B. D Laith H. Alzubaidi Sujata N Patil Hemalatha K. L Manaswini Pradhan Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India The Islamic University of Babylon Babylon Iraq Department of Radiation Oncology Thomas Jefferson University Philadelphia USA Department of Information Science and Engineering Sri Krishna Institute of Technology Bangalore India Department of Computer Science Fakir Mohan University Odisha India

ISBN: (数字)9798331504960

ISBN: (纸本)9798331504977

Change detection refers to an identification of pixels that have undergone semantic changes among two remote sensing images captured at different times in a same area. The problem statement is that remote sensing images has high-resolution causes a challenge for current deep learning-based change and missed detection of small targets. It is overcome by the proposed method with Multi-scale Feature Fusion (MSFF) for feature fusion; semantic variation data is extracted from the Global Semantic Aggregation Module (GSAM); during a decoding phase, Attention Fusion Module (AFM) is applied to extract features; and Cross-scale Fusion Module (CFM) is used to enhance accuracy by removing background noise and the Levir-CD dataset is used. The numerical validation of this approach has a precision of 95.43%, a recall of 97.80%, an IoU of 93.49%, an OA of 99.67%, and a Kappa of 96.59%. The Attention-Guided Siamese Network (SAGNet), Multi-Stage Progressive Change Detection Network (MSP-CD), Teacher-Student Framework (TSF), and Global Attention Module (GAM) are the existing models; the limitations of these methods are overcome by the proposed method.

关键词： Deep learning Accuracy Uncertainty Image edge detection Semantics Feature extraction Numerical models Data mining Detection algorithms Remote sensing

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Energy-Efficient Task Scheduling based on Whale Optimization Algorithm Cyber-Physical Systems

Energy-Efficient Task Scheduling based on Whale Optimization...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Hassan M. Al-Jawahry Smitha Sharath Shankar Kanegonda Ravi Chythanya K. Maharajan Akuthota Swathi The Islamic University Najaf Iraq Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India School of Computer Science and Artificial Intelligence SR University Warangal India Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Krishnankoil India Department of CSE CVR College of Engineering Telangana India

The extensive application of Cyber-Physical Systems (CPS) requires efficient optimization of computational units and physical plants. Task scheduling (TS) is critical to improving resource utilization and system efficacy within CPS. Standard task schedulers in embedded real-time techniques fall short of achieving CPS performance criteria due to task diversity and system heterogeneity. This research proposes a novel task scheduling technique called Whale Optimization Algorithm based Energy-Efficient TS (WOA-EETS) that is specifically designed for the CPS context. WOA-EETSS involves assigning number of autonomous tasks to various resources during the TS process. The objective function focuses on reducing task completion time and optimizing resource use. A series of simulations were run to demonstrate WOA-EETSS' improved performance. The comparison analysis demonstrated that the WOA-EETSS system performed better across a variety of parameters. The proposed approach attains the better performance in Task Execution (ms) of 347ms, Convergence Rate of 511ms and Average Load Balancing Ratio (%) of 69.91% from 100 iterations.

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Facial Expression Recognition Using Grid Wise Attention and Distilled Swin Transformer

Facial Expression Recognition Using Grid Wise Attention and ...

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Network, Multimedia and Information technology (NMITCON) International Conference on

作者： K Anitha Hassan M. Al-Jawahry Smitha Sharath Shankar Jayapal Lande R. Revathi Dept of ECE GSSS Institute of Engineering & Technology for Women Mysuru India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of ECE NITTE Meenakshi Institute of Technology Bengaluru India School of Computer Science & Artificial Intelligence SR University Warangal India Department of Computer Science Karpagam Academy of Higher Education Coimbatore India

ISBN: (数字)9798350372892

ISBN: (纸本)9798350372908

The attention in Facial Expression Recognition (FER) is rise in interaction between human - computer that is to enhance ability of the machine to predict emotions of users. Many previous research works for automatic FER based on using Convolutional Neural Networks (CNN) and other Deep Learning (DL) models. However, the CNN and the existing models fails to acquire long-range learning among various facial areas in maximum layers. To resolve this, a Grid Wise Attention and the Distilled Swin Transformer (GWA-DST) model is proposed to enhance recognition of facial expressions of the image. The images from benchmark CK+ dataset is separated in the form of grids and features from different facial areas of image are extracted. Features are fused by ResNet-50 network and then recognized by proposed Swin transformer. The evaluation results of the proposed approach using performance metrics are Accuracy, F1-score, Precision and Recall. The presented GWA-DST model achieved high accuracy of $96.3 \%$, recall of $96.1 \%$, precision of $96.4 \%$ and F1-score of $\mathbf{9 6. 2 \%}$ is greater than previous DL models such as Resnet-50, FERDERnet, MERsiamC3D, Inception-Resnet+ Long Short Term Memory (LSTM).

关键词： Deep learning Emotion recognition Accuracy Face recognition Computational modeling Benchmark testing Feature extraction Transformers Convolutional neural networks Long short term memory

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Improving Accuracy in Network Intrusion Detection via Machine Learning Algorithms: An In-Depth Examination

Improving Accuracy in Network Intrusion Detection via Machin...

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Distributed Systems, computer Networks and Cybersecurity (ICDSCNC), International Conference on

作者： Suriya Prakash J Trishlaa S Soniya R V Lakshmanan Kiran S Department of CSE JAIN (Deemed-to-be-University) Ramnagara India Department of Artificial Intelligence and Machine Learning Rajarajeshwari College of Engineering Bengaluru India Department of Computer Science and Engineering Sri Eshwar College of Engineering Coimbatore India Department of Mathematics Nitte Meenakshi Institute of Technology Bengaluru India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

Intrusion detection is essential for securing computer networks by monitoring and analyzing activity to detect and respond to unauthorized access and malicious behavior. By examining network traffic and system logs, intrusion detection systems (IDS) help protect data integrity and availability, enabling swift responses to potential security incidents. In an ever-evolving cyber threat landscape, IDS plays a critical role in maintaining network security. This research investigates the performance of various machine learning algorithms for classification tasks using a dataset from Kyoto collected on December 31st, 2015. The algorithms explored include Naive Bayes, K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGBoost), AdaBoost, and CatBoost. Training and testing the model with different data splits (80/20, 70/30, 60/40) revealed that AdaBoost, LGBM, XGBoost, Decision Tree, Gradient Boosting, and Random Forest consistently delivered the highest accuracy. The study concludes that selecting the appropriate algorithms and data splits is crucial for enhancing IDS effectiveness, providing valuable insights for future research. Explore the code behind this study at https://***/Trishlaa07/Kyoto_2015_Dec_day31

关键词： Training Machine learning algorithms Accuracy Nearest neighbor methods Boosting Data models Classification algorithms Decision trees Random forests Testing

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HydroIoT: An IoT and Edge Computing based Multi-Level Hydroponics System 5

HydroIoT: An IoT and Edge Computing based Multi-Level Hydrop...

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5th IEEE International Conference on Computational Systems and Information technology for Sustainable Solutions, CSITSS 2021

作者： Salvi, Sanket Savitha, S. Sawargaonkar, Samyak Vishwas, B. Harshitha, T.K. Pramod Jain, S.A. Nitte Meenakshi Institute of Technology Department of Information Science and Engineering Karnataka Bangalore India Christ University Department of Computer Science Engineering Karnataka Bangalore India Vishwakarma Institute of Information Technology Department of Mechanical Engineering Maharashtra Pune India REVA University Department of Computer Science Engineering Karnataka Bangalore India

ISBN: (纸本)9781665406109

The depleting area of cultivable lands is increasing demands for implementing improved techniques that could use less space and produce more than traditional farming. This situation is common in all the developing and under developed countries. With a motivation to contribute towards providing solution to this growing problem of food scarcity, a Multi-Level Hydroponics System is proposed. The proposed system combines best of all trending technologies like IoT, Edge Computing and computer Vision and applies it to Hydroponics. A cultivation estimation system based on image processing is implemented and accuracy of the same is tested with actual produce. The crop used for the proposed system is corn as it serves as best fodder for cattle. It was observed that with proposed system up to 95% accuracy in estimating fodder produce was achieved. © 2021 IEEE.

关键词： Hydroponics

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Improved Deep Reinforcement Learning Based Network Slicing Method in 5G Networks

Improved Deep Reinforcement Learning Based Network Slicing M...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Haydeer Mohamad Abbas Shashidhara K S D Balamurugan Baraneetharan E K. S. Vigneshwaran Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science And Engineering Sona College of Technology Salem India Department of Electrical and Electronics Engineering Dhanalakshmi Srinivasan College of Engineering Technology Mamallapuram India Department of Mechanical Engineering New Prince Shri Bhavani College of Engineering and Technology chennai India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

In recent scenario, 5G network is designed as a multi-service network supports the wide spread of services like enhanced Mobile Broad Band (eMBB), Ultra Reliable and Low Latency Communications (URLLC) and massive Machine-Type Communications (mMTC). Network slicing enables a single physical network to be divided into multiple virtual slices, each supporting independent services. In 5G systems, primary goal of network slicing is to employ physical Resource Blocks (RBs) based on Quality of Service (QoS) necessities of eMBB, URLLC and mMTC services. The aim of every service class is different and computational complexity caused by maximized number of time slots is large, it is challenging to employ the RBs correctly. In this manuscript, developed the improve Deep Reinforcement Learning (DRL) - based network slicing method in identifying resolution allocation policy which increases the high throughput when satisfying QoS requirements in 5G systems. The main aim of the developed method it to minimize the action space through removing decisions that can't satisfy the QoS requirements. The experimental results showed that the developed method obtained network throughput of 1754 for 60% and 2315 for 70% resource utilization while compared to conventional DRL.

关键词： Integrated circuits 5G mobile communication Network slicing Quality of service Ultra reliable low latency communication Throughput Deep reinforcement learning Reliability engineering User experience Resource management

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Intrusion Detection in Industrial Internet of Things Based on Recurrent Rule-Based Feature Selection

Intrusion Detection in Industrial Internet of Things Based o...

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Network, Multimedia and Information technology (NMITCON) International Conference on

作者： Mohanarangan Veerappermal Devarajan Srinivas Aluvala Vinaye Armoogum S. Sureshkumar H T Manohara Ernst & Young (EY) Sacramento USA School of Computer Science and Engineering SR University Warangal India School of Innovative Technologies and Engineering University of Technology Mauritius Department of CSE Kalasalingam Academy of Research and Education Krishnankoil India Department of ECE Nitte Meenakshi Institute of Technology Bengaluru India

ISBN: (数字)9798350372892

ISBN: (纸本)9798350372908

The Industrial Internet of Things (IIoT) is experiencing rapid growth, and robust cyber security measures to protect against from the cyber-attacks with the help of Anomaly Detection System (ADS) and signature-based detection system. The sensors collect vast amount data from the environment, presenting functionality challenges for device. To overcome this problem, many Network Intrusion Detection Systems (NIDS) had developed for secure to IIoT systems. But NIDS faces challenges due to complexity of information collection required for threat detection. So, propose study work introduces a Recurrent rule-based Feature Selection (RFS) for IIoT system. NSL-KDD and UNSW-NB15 dataset are used for performing operation and relevant features are selected by using hybrid rule-based algorithm. Then RFS model classifies the data and predict the attacks. the proposed method performance is superior than existing method and better results in terms of accuracy rates of 99.0% and 98.9%, detection rates of 99.0% and 99.9%, and low false positive rates of $1.0 \%$ and $1.1 \%$ respectively.

关键词： Radio frequency Accuracy TCPIP Feature extraction NSL-KDD Mathematical models Threat assessment Sensors Cyberattack Industrial Internet of Things

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