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Personality Prediction Using Machine Learning 1

Personality Prediction Using Machine Learning

1st International Conference on Computational Intelligence for Information, Security and Communication Applications, CIISCA 2023

作者： Pooja, K. Rakshitha, G.H. Spoorthi, S. Kamal, Shoaib Rao, Trupthi Ashwini, K. Global Academy of Technology Department of Computer Science and Engineering Bangalore India Global Academy of Technology Department of Artificial Intelligence and Data Science Bangalore India

ISBN: (纸本)9798350339727

With the growth of social networks, a wide range of methodologies have been developed to describe users' personalities only based on their language and social media use habits. Persona prediction is very popular these days. It examines consumer behaviour and records the user's ideas, emotions, and so on. There has been a sufficient amount of research in this field over the years. This survey provides an overview of the numerous methods tried to predict personality and behaviour from the use of the social media content. The capacity to anticipate the personality attributes of users can help create a variety of specialised goods or services. The concluding phase then provides the future characteristics and directives. © 2023 IEEE.

关键词： Machine learning

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Early Forest-Fire Detection by Linear Regression, Ridge Regression and Lasso Regression 1

Early Forest-Fire Detection by Linear Regression, Ridge Regr...

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1st International Conference on Computational Intelligence for Information, Security and Communication Applications, CIISCA 2023

作者： Mandre, Ananya. Hebbar, Deeksha.R. Rao, J. Shreya Keshav, Ananya Kamal, Shoaib Rao, Trupthi Global Academy of Technology Department of Computer Science Engineering Bangalore India Global Academy of Technology Department of Electronics and Communication Engineering Bangalore India Global Academy of Technology Department of Information Science Engineering Bangalore India Global Academy of Technology Department of Artificial Intelligence and Science Bangalore India

ISBN: (纸本)9798350339727

Forest fires are the wildfires that grow uncontrollably, burning plants, animals, grassland and bushlands. For reducing the losses caused by the disaster an effective fire detection device is required. The main agenda of This research will provide a model for early detection. of forest fire. Previously many methods were proposed for the detection such as Neural network model, Three dimension otsu method, NMDI methodology and many more, As we can see there are certain limitations for all these methods. We have implemented certain methodology such as Ridge regression, Lasso regression and Linear regression, equations and the methodology used in this system is studied and implemented during this study. Thus all these three systems demonstrate the required solution for the problem with a good percentage of accuracy, © 2023 IEEE.

关键词： Fires

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Dumpy: A Waste Management App

Dumpy: A Waste Management App

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2023 International Conference on IoT, Communication and Automation technology, ICICAT 2023

作者： Chetan, S.R. Karanth, Adithya B Dheeraj, D. Singh R, Rohan Arun, Jaideep Global Academy of Technology Information Science and Engineering Bengaluru India Dayananda Sagar College of Engineering Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350302820

A developing kind of trash management called smart waste management uses technology to streamline the operations of waste collection, transportation, and disposal. These kind of systems can increase operational effectiveness, lower costs, and promote environmental sustainability by utilizing sensors, analytics, and automation. Additionally, these systems can be used for route optimization, real-time monitoring of garbage levels, and preventative maintenance of waste collection equipment. An overview of the idea is indicated, along with a few of its possible advantages. Dumpy is a waste management app that helps users to dispose of and compost waste generated by a household. We collect, compost, and reward people for generating less waste. © 2023 IEEE.

关键词： Real time systems

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Cyber Security Threat Detection in Internet of Things Using Optimized Deep Learning Technique 3

Cyber Security Threat Detection in Internet of Things Using ...

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3rd IEEE Asian Conference on Innovation in technology, ASIANCON 2023

作者： Mariappan, Umasree Balakrishnan, D. Rajendran, S. Alagusundar, Nanditha Sheriff, A. Ashiq Aadith, K. Sri Vidya College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Virudhunagar626001 India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu Krishnankoil626126 India Kalasalingam Academy of Research and Education Department of Electrical and Electronics Engineering Tami lNadu Krishnankoil626126 India

ISBN: (纸本)9798350302288

Cybersecurity threat detection in the Internet of Things (IoT) refers to the process of identifying and addressing potential security risks and attacks within IoT systems and devices. It involves monitoring IoT networks and devices for abnormal behavior, detecting intrusions or unauthorized access attempts, ensuring the integrity of data, managing vulnerabilities, analyzing behavioral patterns, and staying informed about emerging threats. This ongoing process aims to maintain the security and integrity of IoT infrastructure by proactively identifying and mitigating potential security threats. Recent studies have demonstrated that deep learning algorithms are highly efficient and offer numerous advantages compared to earlier methods when it comes to conducting security assessments of IoT devices. Dual Channel Convolution Neural Network with Spider Monkey Optimization (DCCNN-SMO), a deep learning technique which visualized colored images for finding harmful intrusions in IoT network. Here, DCCNN used two channels of CNN and combined in fully connected layer for detecting intrusions in IoT network. The hyper parameters of DCCNN were fine tune by SMO which enhance the performance of DCCNN. In this paper, the convergence speed and effectively fine tuning the hyper parameters of DCCNN an Adapted SMO (ASMO) is introduced for better detection of intrusions in IoT network. The position update equation of the basic SMO algorithm is modified by incorporating a dual seek scheme. This approach, employed in the local leader (LL) phase, global leader (GL) phase, and LL decision phase, facilitates the acquisition of fresh outcomes, representing best hyper parameters of DCCNN for better detection of intrusion in IoT network. The performance of the proposed DCCNN-ASMO is demonstrated to be superior in terms of accuracy, precision, and recall, as evidenced by the experimental results. © 2023 IEEE.

关键词： Internet of things

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Pager Explosion:Cybersecurity Insights and Afterthoughts

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IEEE/CAA Journal of Automatica Sinica 2024年第12期11卷 2359-2362页

作者： Chuan Sheng Wanlun Ma Qing-Long Han Wei Zhou Xiaogang Zhu Sheng Wen Yang Xiang Fei-Yue Wang Swinburne University of Technology Hawthorn VIC 3122Australia IEEE the School of Computer and Mathematical Sciences the University of AdelaideAdelaideSouth Australia 5005Australia the State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 the Department of Engineering Science Faculty of Innovation EngineeringMacao University of Science and TechnologyMacao 999078China

PAGER EXPLOSION:THE *** 2024 Lebanon pager explosions represent one of the most unexpected and devastating technological incidents in recent *** September 17 and 18,2024,thousands of pagers and walkie-talkies exploded simultaneously across Lebanon and parts of Syria,resulting in 42 deaths and more than 3500 *** handheld communication devices,previously regarded as secure and low-profile,were rigged with concealed explosives and remotely triggered by attackers.

关键词： injuries thoughts explosion

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Ensemble of Handcrafted and Deep Learning Model for Histopathological Image Classification

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computers, Materials & Continua 2022年第11期73卷 4393-4406页

作者： Vasumathi Devi Majety N.Sharmili Chinmaya Ranjan Pattanaik ELaxmi Lydia Subhi R.M.Zeebaree Sarmad Nozad Mahmood Ali S.Abosinnee Ahmed Alkhayyat Department of Computer Science Engineering Vignan’s Nirula Institute of Technology and Science for WomenGuntur522005India Department of Computer Science Engineering Gayatri Vidya Parishad College of Engineering for WomenVisakhapatnam530048India Department of Computer Science and Engineering Ajay Binay Institute of Technology CuttackOdisha750314India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Energy Department Technical College of EngineeringDuhok Polytechnic UniversityDuhokIraq Computer Technology Engineering College of Engineering TechnologyAl-Kitab UniversityIraq Computer Technical Engineering Department College of Technical EngineeringThe Islamic UniversityNajaf54001Iraq College of Technical Engineering The Islamic UniversityNajafIraq

Histopathology is the investigation of tissues to identify the symptom of *** histopathological procedure comprises gathering samples of cells/tissues,setting them on the microscopic slides,and staining *** investigation of the histopathological image is a problematic and laborious process that necessitates the expert’s *** the same time,deep learning(DL)techniques are able to derive features,extract data,and learn advanced abstract data *** this view,this paper presents an ensemble of handcrafted with deep learning enabled histopathological image classification(EHCDL-HIC)*** proposed EHCDLHIC technique initially performs Weiner filtering based noise removal *** the images get smoothened,an ensemble of deep features and local binary pattern(LBP)features are *** the classification process,the bidirectional gated recurrent unit(BGRU)model can be *** the final stage,the bacterial foraging optimization(BFO)algorithm is utilized for optimal hyperparameter tuning process which leads to improved classification performance,shows the novelty of the *** validating the enhanced execution of the proposed EHCDL-HIC method,a set of simulations is *** experimentation outcomes highlighted the betterment of the EHCDL-HIC approach over the existing techniques with maximum accuracy of 94.78%.Therefore,the EHCDL-HIC model can be applied as an effective approach for histopathological image classification.

关键词： Histopathological image classification machine learning deep learning handcrafted features bacterial foraging optimization

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Malware Detection Employing Deep Neural Networks 10

Malware Detection Employing Deep Neural Networks

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Nayak, Sanjana Ganesh Kurup, Samir Andrew, J. Manipal Institue of Technology Manipal Academy of Higher Education Department of Computer Science and Engineering Karnataka Manipal India

ISBN: (纸本)9798350384369

Malware, malicious software designed to disrupt, damage, or gain unauthorized access to computer systems, poses a significant and evolving threat to cybersecurity. Malware detection is an essential component of modern cybersecurity, given the escalating complexity and diversity of malicious software threats. In this study, we present a novel approach to malware detection based on behavior-based datasets using a fully connected deep neural network. Our research is motivated by the need for robust and accurate malware detection models that can adapt to evolving threats. The behavior-based dataset, which captures the dynamic interactions of malware with the host environment, provides a rich source of information for training and evaluation. The model uses the hyperbolic tangent (tanh) activation function and the Nesterov optimizer, resulting in remarkable accuracy of 100%. This study offers a high- performing solution for malware detection using behavior- based datasets. As cybersecurity continues to evolve, our approach contributes to strengthening defenses against the ever- persistent threat of malware. © 2024 IEEE.

关键词： Cyber attacks

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A Robust Low-Rank Tensor Decomposition and Quantization based Compression Method 40

A Robust Low-Rank Tensor Decomposition and Quantization base...

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Ouyang, Yudian Xie, Kun Wen, Jigang Xie, Gaogang Li, Kenli College of Computer Science and Electronics Engineering Hunan University Changsha China Hangzhou Institute of Advanced Technology Hangzhou China School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China Institute of Computer Network Information Center Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798350317152

Tensor data is widely used in fields such as smart grids, cloud systems, and deep learning. As the scale of this data increases, storage and transmission costs rise significantly. Many tensor data exhibit low-rank structures, offering the potential for data compression through low-rank decomposition techniques. Tucker decomposition, a typical low-rank decomposition technique, achieves data compression and interpretability by capturing complex data correlations and representing the original tensor with a compact core tensor and factor matrices. However, the compression ratio provided by Tucker decomposition is often insufficient, particularly for large-scale tensors. To tackle this issue, we propose a robust low-rank tensor compression method that leverages Tucker decomposition with quantization and coding. Initially, we establish a robust Tucker decomposition framework that decomposes the low-rank tensor into a core tensor and factor matrices using well-designed Tucker rank-setting rules. This framework effectively handles noise and missing values. Subsequently, we conduct an in-depth analysis of the numerical characteristics of the core tensor and factor matrices within the Tucker decomposition framework. Based on their distinct characteristics, we design tailored quantization and coding schemes to compress the core tensor and factor matrices, respectively, thereby significantly improving the compression ratio of Tucker decomposition while maintaining high accu-racy. Through extensive experiments on four publicly available datasets (which can form 3 or 4 order tensors), we demonstrate that our approach can achieve compression ratios 4×-10× higher than the best competitor, with recovery errors improved by 8% -42%. © 2024 IEEE.

关键词： Tensors

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Classification of Social Anxiety Disorder Using Explainable Machine Learning and Pearson's Correlation Technique 8

Classification of Social Anxiety Disorder Using Explainable ...

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8th IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2024

作者： Bathula, Srivarsha Chadaga, Krishnaraj Prabhu, Srikanth Sampathila, Niranjana Manipal Institute of Technology Manipal Academy of Higher Education Manipal Department of Computer Science and Engineering Karnataka India

ISBN: (纸本)9798350350593

Social Anxiety Disorder (SAD) is a widespread mental health issue marked by significant fear or discomfort during social interactions. It can greatly affect a person's life and overall happiness, causing problems like emotional distress, low self-esteem and depression. Thorough examination by a mental health expert is often required to diagnose SAD. The diagnostic and statistical manual of mental disorders' particular criteria are used to diagnose SAD that includes clinical interviews, self-report questionnaires, and a thorough evaluation of the individual's symptoms like severe anxiety while engaging or conversing with strangers. In this work, we employ explainable artificial intelligence (XAI) and machine learning techniques to identify SAD in individuals. Critical attributes were identified using Pearson's correlation technique. Random Forest yields optimal outcomes with 88% accuracy, 93 % precision, 83 % recall, 84 % F1-score, and 94% Area Under Curve (AUC). Furthermore, XAI methods such as Shapley Additive Values (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) have been applied to improve the models' accuracy, comprehensibility, and precision. Automated SAD diagnosis helps in early detection and increased accessibility that allows for timely intervention, treatment, and facilites access to social anxiety testing and screening for an individual. © 2024 IEEE.

关键词： Adversarial machine learning

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Visual Computing Unified Application Using Deep Learning and computer Vision Techniques

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2024年第1期18卷 59-74页

作者： Sowmya, B.J. Meeradevi Seema, S. Dayananda, P. Supreeth, S. Shruthi, G. Rohith, S. Department of Artificial Intelligence and Data Science Ramaiah Institute of Technology Karnataka Bengaluru India Department of Artificial Intelligence and Machine Learning Ramaiah Institute of Technology Karnataka Bengaluru India Department of Computer Science and Engineering M S Ramaiah Institute of Technology Karnataka Bengaluru India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Karnataka Manipal India School of Computer Science and Engineering REVA University Karnataka Bengaluru India Department of Electronics & Communication Engineering Nagarjuna College of Engineering & Technology Karnataka Bengaluru India

Vision Studio aims to utilize a diverse range of modern deep learning and computer vision principles and techniques to provide a broad array of functionalities in image and video processing. Deep learning is a distinct class of machine learning algorithms that utilize multiple layers to gradually extract more advanced features from raw input. This is beneficial when using a matrix as input for pixels in a photo or frames in a video. computer vision is a field of artificial intelligence that teaches computers to interpret and comprehend the visual domain. The main functions implemented include deepfake creation, digital ageing (de-ageing), image animation, and deepfake detection. Deepfake creation allows users to utilize deep learning methods, particularly autoencoders, to overlay source images onto a target video. This creates a video of the source person imitating or saying things that the target person does. Digital aging utilizes generative adversarial networks (GANs) to digitally simulate the aging process of an individual. Image animation utilizes first-order motion models to create highly realistic animations from a source image and driving video. Deepfake detection is achieved by using advanced and highly efficient convolutional neural networks (CNNs), primarily employing the EfficientNet family of models. © 2024 by the authors of this article. Published under CC-BY.

关键词： Animation

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