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Behavioral Analytics for Predictive Modeling of Mental Health Disorders: A Review of Machine Learning Techniques and Challenges

Behavioral Analytics for Predictive Modeling of Mental Healt...

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Intelligent Control, Computing and Communications (IC3), International Conference on

作者： Prashant Vats Pravin R. Kshirsagar Kamal Upreti Keshav Lalit Tan Kuan Tak Shubham Mahajan Dept. of Computer Science and Engg Manipal University Jaipur Jaipur India Department of Electronics & Telecommunication Engineering J D College of Engineering & Management Nagpur India Dept. of Computer Science CHRIST (Deemed to Be University) Delhi NCR India Dept. of Computer Science and Engg Manipal Institute of Technology Manipal Karnataka India Engineering Cluster Singapore Institute of Technology Singapore Amity School of Engineering and Technology (ASET) Amity University Gurugram India

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

Mental health issues, including anxiety, stress, and depression, may remain untreated until they escalate to a severe level. The issues significantly impact an individual's overall well-being and productivity. Timely identification is crucial for the effectiveness of both intervention and therapy. The application of machine learning techniques makes behavioral analytics a powerful tool for mental health disease prediction modeling. By analyzing behavioral data, this technology facilitates the early detection of various illnesses. This work aims to provide a thorough overview of the use of machine learning techniques, including models that employ Deep structured learning as well as both unsupervised as well as supervised learning, to behavioral data, including activity levels, speech patterns, and facial movements, in order to identify signs of mental health. The benefits and drawbacks of a broad range of machine learning algorithms are examined, with a focus on how these computer algorithms may be applied to identify patterns linked to illnesses like stress, anxiety, and emotional depression. This study looks into the problems that this business encounters as well. These difficulties include combining behavioral data with extra environmental issues and physiological features from the immediate surroundings, the necessity for large and diverse datasets, the need for security of information, and the capacity to understand models.

关键词： Deep learning Analytical models Machine learning algorithms Computational modeling Anxiety disorders Mental health Predictive models Depression Physiology Long short term memory

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Energy Harvesting from Railway Tracks: Technologies and Applications

Energy Harvesting from Railway Tracks: Technologies and Appl...

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Trends in Quantum Computing and Emerging Business Technologies (TQCEBT), International Conference on

作者： Abhishek Saxena Y Manohar Reddy Suman Avdhesh Yadav B Rajalakshmi Manish Gupta Kadim A. Jabbar Department of Mechanical Engineering ABES Engineering College Ghaziabad UP India Institute of Aeronautical Engineering Hyderabad Amity School of Engineering and Technology Amity University Greater Noida Uttar Pradesh India Department of Computer Science New Horizon College of Engineering Bangalore Lovely Professional University Phagwara India College of Engineering Technology National University of Science and Technology Dhi Qar Iraq

ISBN: (数字)9798350384277

ISBN: (纸本)9798350384284

Research on the creative use of kinetic energy in transport infrastructures has been directed by the constant search for sustainable energy solutions. Of them, railway tracks provide a particularly attractive pathway for energy collecting because of their vast network and the significant stresses applied during train movements. The latest technologies created for energy harvesting from railway tracks are explained in this article along with the workings, concepts, and efficiency of the different systems. The ability to transform mechanical stress and vibrations into electrical energy is closely examined in hybrid systems, electromagnetic devices, and piezoelectric transducers. A thorough analysis of the literature highlights both the technical developments and the difficulties, including those related to energy storage, affordability, and durability. In addition, this research investigates the uses of captured energy, which may improve the sustainability of railway systems by powering trackside sensors or adding to the grid. The appropriateness and efficacy of various harvesting systems under various operating circumstances may be inferred from a comparative study of their performance indicators. In order to fully use this renewable energy source, the report concludes by outlining future research areas and highlighting the integration of energy collecting technology with smart railway infrastructure.

关键词： Industries Electromagnetic devices Tracking Piezoelectric transducers Transforms Rail transportation Energy harvesting

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Hybrid Multimodal Machine Learning Driven Android Malware Recognition and Classification Model 7

Hybrid Multimodal Machine Learning Driven Android Malware Re...

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7th International Conference on Electronics, Communication and Aerospace technology, ICECA 2023

作者： Arthi, A. Aggarwal, Kapil Karthikeyan, R. Kayalvili, S. Srimathi, S. Srivastava, Aviral Rajalakshmi Institute of Technology Department of Artificial Intelligence and Data Science Chennai India Department of Computer Science & Engineering Guntur Andhra Pradesh Vaddeswaram522302 India Dr NGP Institute of Technology Department of Artificial Intelligence and Data Science Coimbatore India Artificial Intelligence College Name & Address: Kongu Engineering College Perundurai India Saveetha School of Engineering SIMATS Department of Bio Technology Tamilnadu Chennai India Amity University Rajasthan Jaipur300202 India

ISBN: (纸本)9798350340600

Android mobile applications and devices can be extremely utilized and employed in industry and smart cities. Malware recognition is an efficient and powerful method for assurance security of Android schemes, mainly for industrial platforms and smart cities. In recent times, research utilizing machine learning (ML)-based approaches for Android malware recognition enhanced quickly. However, one of the performed methods to accomplish feature selection and analysis, so-called feature engineering is labour-intensive and depends on artificial knowledge. Therefore, this study presents a Hybrid Multimodal Machine Learning Driven Android Malware Recognition and Classification (HM3-AMRC) model. The proposed HM3-AMRC technique recognizes and categorizes the Android malware accurately and efficiently. In the presented HM3-AMRC technique, initial phase of data preprocessing is performed. Next, the classification process takes place using three ML techniques such as gradient boosting decision tree (GBDT), extreme learning machine (ELM), and multilayer perceptron (MLP). Moreover, Bayesian optimization (BO) technique is employed for optimal parameter selection of the ML approaches. The stimulation analysis of the HM3-AMRC method is tested on Android malware dataset. Extensive comparison analysis highlighted the betterment of the HM3-AMRC method over existing techniques. © 2023 IEEE.

关键词： Android malware

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An Advanced Approach for performing Cyber Fraud using Banner Grabbing 5

An Advanced Approach for performing Cyber Fraud using Banner...

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5th International Conference on Contemporary Computing and Informatics, IC3I 2022

作者： Kaushik, Keshav Punhani, Ishika Sharma, Smita Martolia, Mamta University of Petroleum and Energy Studies School of Computer Science Dehradun India Amity University Department of Electronics and Communication Engineering Uttar Pradesh India G L Bajaj Institute of Technology Management Greater Noida Department of Computer Science and Engineering Uttar Pradesh India

ISBN: (纸本)9798350398267

In this paper, the authors have discussed how cyber-attackers or hackers can do cyber fraud or can steal information from some systems using the banner grabbing approach. Cybercriminals and security personnel utilize the banner grabbing technique to gather data about a computer network or a system as well as the services operating on its open ports. An informational banner is a group of text shown by a web application that displays details like the kind and edition of software being used by a laptop or web application. The welcome screens pro-vide information about network hosts' software versions and other infrastructure components, giving attackers an edge in network assaults. In this paper, the authors have targeted the vulnerable metasploitable machine to perform the banner grabbing. Moreover, the authors have also highlighted the preventive measures against banner grabbing. The authors have employed several techniques for capturing banners, including telnet, Netcat, curl, Nmap, and Metasploit. This data may be utilized for reconnaissance and, eventually, improved exploitation. © 2022 IEEE.

关键词： Crime

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Data Driven Mental Health Predicting Depression Using Machine Learning

Data Driven Mental Health Predicting Depression Using Machin...

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Data science and Business Systems (ICDSBS), International Conference on

作者： Nitesh Kumar Anamika Kumari Neha Kumari Krishna Murari Prakash Anand Richa Verma Department of Information Technology L.N. Mishra College of Business Management Muzaffarpur Bihar India Department of Computer Science and Engineering Amity School of Engineering and Technology Patna Bihar India Department of Computer Science and Engineering National Institute of Technology Jamshedpur Jharkhand India Department of Computer Science in Artificial Intelligence Galgotias College of Engineering and Technology Greater Noida India Department of Computer Science and Engineering Galgotias University Greater Noida India Department of Computer Applications International School of Management Patna Bihar India

ISBN: (数字)9798331585600

ISBN: (纸本)9798331585617

The rise of information investigation methods such as machine learning is opening up unused openings in the field of mental wellbeing. This department of inquire about, known as data-driven mental wellbeing, is especially interested in applying machine learning to identify discouragement. Analysts trust to make models that can recognize individuals at chance of discouragement by analyzing information such as overview answers, social media action, and discourse designs. Early distinguishing proof is basic for his, her, their, etc. ideal treatment, and machine learning may play an imperative part in fulfilling this point. Be that as it may, it is basic to realize that these models are not aiming to supplant mental wellbeing pros. Instep, they are planning as disobedient to help in screening and intercession efforts.

关键词： Schedules Analytical models Social networking (online) Machine learning Mental health Data science Solids Depression Business

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Dual-Level Data Compression and Reduction in IoT Networks: Enhancing Efficiency through Sensor Node and Gateway Optimization

Dual-Level Data Compression and Reduction in IoT Networks: E...

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Artificial Intelligence and Emerging technology (Global AI Summit), International Conference on

作者： Pushan Kumar Dutta Pronaya Bhattacharya Sushil Kumar Singh El-Sayed M. El-kenawy Richa Pandey School of Engineering and Technology Amity University Kolkata Computer Engineering Department Marwadi University Rajkot Gujarat India Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Mansoura Egypt Computer Science and Engineering Graphic Era Hill University Haldwani Uttarakhand

ISBN: (数字)9798350379716

ISBN: (纸本)9798350379723

The proliferation of Internet of Things (IoT) devices has led to unprecedented challenges in data management and transmission efficiency. This paper introduces a novel dual-level approach to data compression and reduction in IoT networks, addressing these challenges by optimizing data handling at both sensor node and gateway levels. Unlike conventional single-level techniques, our method provides a more comprehensive solution to data optimization in IoT ecosystems. At the sensor node level, we implement Run Length Encoding (RLE) compression, while at the gateway level, we integrate progressive data clustering and reduction strategies. This dual-level approach demonstrates significant improvements in data reduction compared to single-point optimization methods. Our comparative analysis reveals the synergistic effects of combining node-level compression with gateway-level data reduction, resulting in enhanced overall system performance. We evaluate the scalability and adaptability of our approach across diverse IoT applications, considering energy efficiency implications for resource-constrained devices. Additionally, we discuss alternative data reduction techniques such as attribute reduction, canonical polyadic decomposition, and edge computing-based filtering, contextualizing our method within the broader landscape of IoT data optimization. The paper also examines the impact of our approach on network bandwidth utilization and explores future directions for multi-level data optimization in complex IoT architectures. Our findings suggest that the proposed dual-level method offers a promising solution for managing the growing data challenges in IoT networks, paving the way for more efficient and scalable IoT systems.

关键词： Temperature measurement Wireless communication Wireless sensor networks Data compression Bandwidth Logic gates Encoding Energy efficiency Internet of Things Artificial intelligence

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Investigation of SLA Management in Cloud Computing and Future Directions 2

Investigation of SLA Management in Cloud Computing and Futur...

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2nd International Conference on Computational Methods in science and technology, ICCMST 2021

作者： Singh, Mandeep Bhushan, Shashi Rani, Shanky Chandigarh Engineering College Department of It Mohali India Ikpgtu Kapurthala India Amity University Amity School of Engineering and Technology Department of Computer Science and Engineering Bihar Patna India

ISBN: (纸本)9781665458665

Cloud computing is an on-demand service provisioning technology in which computer and networking resources like software applications, computation power, storage space and software based network devices are provided to the customer under a service level agreement (SLA). In dynamic environment of cloud computing service level agreement and its management is very crucial component from customer's trust and long term business opportunity point of view. The SLA management process includes many activities such as SLA Negotiation, SLA Pricing and SLA Monitoring etc. Many SLA management approaches have been proposed by researchers. This paper systematically investigates different research publications branching form SLA management into different directions and assesses the relative extent of research done so far in each direction. The analysis reveals that latest trends in SLA related research are SLA violations, Auditing, and development of SLA frameworks. © 2021 IEEE.

关键词： Cloud computing

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A Concise Review Of MRI Feature Extraction And Classification With Kernel Functions

A Concise Review Of MRI Feature Extraction And Classificatio...

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International Conference on Innovative Sustainable Computational Technologies (CISCT)

作者： Jagendra Singh Chandrakala Arya Nagendar Yamsani Mohit Kumar Prabhishek Singh Vivek Kumar Bhagat School of Computer Science Engineering & Technology Bennett University Greater Noida India School of Computing Graphic Era Hill University Dehradun India School of Computer Science and Artificial Intelligence SR University Warangal India Amity School of Engineering and Technology Amity University Noida India

Magnetic resonance imaging is a common medical imaging tool for the identification and monitoring of brain diseases. Accurate classification of MRI brain pictures is extremely beneficial for early diagnosis and therapy planning. In this study, a method for MRI classification and feature extraction using a Support Vector Machine (SVM) and hybrid RBF which is radial Basis function kernel is proposed. Some of the interconnected steps in the suggested methodology include pre-processing, hybrid RBF kernel creation, feature extraction, SVM classification, feature selection, model evaluation, and model optimization. The GLCM technique is utilized to derive texture characteristics from MRI images. A blend of multiple RBF kernels with different parameter settings yields a hybrid RBF kernel. Performance indicators are used to assess the SVM classifier after it has been trained on a training dataset. The hybrid RBF kernel and the SVM's parameters are altered using model optimization. The suggested methodology intends to classify MRI brain pictures appropriately for enhanced treatment planning and disease diagnosis.

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SSVBT: Secure Certificateless Aggregate Signature Scheme in VANETS with Block Chain technology

SSVBT: Secure Certificateless Aggregate Signature Scheme in ...

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International Conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions)

作者： Ruchi Mittal Varun Malik Manisha Aeri Kuldeep Singh Kaswan S Vikram Singh Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Computer Science and Engineering Graphic Era Hill University Graphic Era Deemed to be University Dehradun Uttarakhand Department of Computer Science and Engineering Galgotias University Greater Noida Uttar Pradesh India Amity School of Engineering and Technology Amity University Greater Noida Uttar Pradesh India

ISBN: (数字)9798350350357

ISBN: (纸本)9798350350364

The proliferation of Vehicular Ad-hoc Networks (VANETs) highlights the crucial need for secure, privacy-protecting inter-vehicle communication. By integrating blockchain technology with a safe certificate-less aggregate signature process, this research presents a novel approach to enhancing VANET security. Traditional VANET signature systems that rely on PKI and certificates have their limitations solved by the suggested approach. Using a certificate-less paradigm eliminates the requirement for a certificate authority when creating keys, making key exposure and compromise less probable. By combining several signatures into one smaller one, the aggregate signature method significantly decreases network communication cost. The paper introduces blockchain technology as an extra component to the certificate-less aggregate signature, further ensuring the honesty and openness of the VANET. The blockchain records and validates transactions, aggregate signatures, key alterations, and more using its distributed and immutable ledger. This not only improves the signature scheme but also creates a VANET environment that is trustworthy and robust.

关键词： Privacy Costs Aggregates Merging Vehicular ad hoc networks Public key Market research

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Optimized Dilated Convolutional Neural Network with Quantum Self-Attention Based on Hyper Spectral Remote Sensing Image Classification 4

Optimized Dilated Convolutional Neural Network with Quantum ...

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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Santhosha, T. Ali, H. Mohammed Seeniappan, K. Yamsani, Nagendar Maranan, R. Kaliappan, S. T.S Medchal district501301 India Srm Institute of Science and Technology Department of Mechanical Engineering Ramapuram Campus Chennai89 India Amity University Department of Mechanical Engineering Dubai345019 United Arab Emirates Sr University School of Computer Science & Artificial Intelligence Telangana Warangal506371 India Saveetha School of Engineering Simats Department of Research and Innovation Tamil Nadu Chennai602105 India Lovely Professional University Division of Research and Development Punjab Phagwara144411 India

ISBN: (纸本)9798331518981

Hyperspectral remote sensing (RS) involves capturing and analyzing data across numerous, narrow spectral bands in the electromagnetic spectrum. With this technology, it is possible to identify what has been on the Earth surface with unprecedented depth that cannot be done with the mere multispectral images. A Hyperspectral sensor is able to capture hundreds of bands of data, and is therefore able to distinguish between very small differences in surface materials that we cannot even see or conventional multispectral sensors. Hyperspectral remote sensing is widely used in applications like agriculture, environmental monitoring, geology, and defence. To overcome the limitations of current approaches, new strategies are needed. In this manuscript Optimized Dilated Convolutional Neural Network with Quantum Self-Attention Based on Hyper Spectral Remote Sensing Image Classification (DCNN-QSA-PDOA) is proposed. The Indian Pine dataset is where the data is first gathered. The multidirectional shearlet transform based feature extraction (MDST) model is then utilized to extract the hyperspectral image (HSI) features. Following the completion of the feature extraction procedure, the Dilated Convolutional Neural Network with Quantum Self-Attention (DCNN-QSA) is trained on the feature extracted images and used to classify the category information. Here, (PDOA) is utilized for tuning hyper parameters of DCNN-QSA. The proposed DCNN-QSA-PDOA method provides 37.23%, 18.74% and 24.84% higher accuracy compared with existing method like Classifying images from hyper spectral RS using a DCNN-HIS, Dense residual three-dimensional convolutional neural networks are used for hyper spectral RS picture classification (DR-3DCNN-HIS), Fully convolutional neural networks are used for the categorization of hyper spectral remote sensing pictures (CNN-HIS) respectively. © 2024 IEEE.

关键词： Convolutional neural networks

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