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8th International Conference on Information and Communication technology for Intelligent Systems, ICTIS 2024

作者： Shwetha, C.H. Pushpalatha, K. Department of Computer Science and Engineering Mangalore Institute of Technology and Engineering Affiliated to Visvesvaraya Technological University Belagavi India Department of Computer Science and Engineering Sahyadri College of Engineering and Management Affiliated to Visvesvaraya Technological University Belagavi India

ISBN: (纸本)9789819766741

In recent years, the widespread occurrence of psychological state issues, especially depression, has turned into a growing concern worldwide. The increasing reliance on online media platforms as a way of communication has caused to an influx of user-generated data, presenting a fresh chance for timely diagnosis and intervention in intellectual health conditions. Initial recognition and identification are very much important in preventing adverse outcomes and raising the standard of living of depressed individuals. However, majority of group who are struggling from depression do not seek professional help due to various barriers such as stigma, cost, or lack of access. Therefore, alternative methods of identifying and monitoring depression are needed, particularly in the period of online platform, where people often express their thinking and emotion online. This literature survey aims to review the state-of-the-art methods and applications of deep learning and NLP techniques in pointing out depression from online social interactions. By analyzing recent advancements in this field, including neural network architectures and language models, this study seeks to offer a comprehensive insight into difficulties in automatic detection of depression-related cues from textual data and to inspire further innovative work in leveraging online media for psychological well-being assessment. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Classification Deep learning Depression Feature extraction Machine learning NLP Sentiment analysis

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Optimization Algorithms in Diabetes Prediction: A Comparative Work 3

Optimization Algorithms in Diabetes Prediction: A Comparativ...

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3rd International Conference on Sentiment Analysis and Deep Learning, ICSADL 2024

作者： Kannan, E. Carmel Mary Belinda, M.J. Vel Tech Rangarajan Dr. Sagunthala RandD Institute of Science and Technology Department of Computer Science and Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350396157

Accurately predicting chronic conditions such as diabetes is essential for prompt treatment and improved patient outcomes. Using the UCI diabetes dataset, this work assesses four optimization algorithms: Adam, RMSprop, Nadam, and Stochastic Gradient Descent (SGD). The objective is to evaluate their efficacy in creating precise prediction models by empirical means. These algorithms have a significant impact on the stability and speed of convergence of deep learning models during training. With patient data, machine learning and deep learning models can be used to assess the risk of diabetes. Diabetes is a complex metabolic disease that requires early detection to prevent complications;however, the choice of optimizer has a significant impact on the models' efficacy. As the optimizer with the highest accuracy, precision, and F1 Score, Nadam stands out. SGD does exceptionally well in recall, while Adam and RMSprop do well as well. The choice of optimizer should take trade-offs between measures into account and be in line with specific therapeutic goals. They can be more effectively tuned as hyperparameters in diabetes prediction models. This work improves patient outcomes in the management of diabetes and healthcare risk assessment. © 2024 IEEE.

关键词： Forecasting

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Detecting IoT Network Breach Using the Random Forest Classifier

Detecting IoT Network Breach Using the Random Forest Classif...

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2024 International Conference on Integration of Emerging Technologies for the Digital World, ICIETDW 2024

作者： Sasikala, K. Vasuhi, S. Saveetha Engineering College Department of Computer Science Engg India Madras Institute of Technology Department of Electronics Engg India

ISBN: (数字)9798331541170

ISBN: (纸本)9798331541170

Intrusion detection faces particular issues due to the interconnection and resource constraints of Internet of Things (IoT) devices. A complete collection of network traffic statistics pertaining to IoT security situations may be found in the CIC-IDS 2018 dataset. In this work, the Random Forest classifier, a flexible machine learning technique renowned for its high accuracy in handling heterogeneous data is used to address the critical problem of IoT network security. The intrusion detection system is developed using features taken from the CIC-IDS 2018 dataset, and it achieves an astounding accuracy rate of 99.96%.This remarkable outcome shows how well the Random Forest classifier works to identify and classify different types of network intrusions, including those that affect Internet of Things devices. The study enhances the security stance of Internet of Things networks by offering valuable perspectives on the application of machine learning methods for dependable intrusion detection in IoT environments. © 2024 IEEE.

关键词： Network intrusion

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Dynamic Data Placement Strategy with Network Security Issues in Distributed Cloud Environment for Medical Issues: An Overview

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Recent Advances in computer Science and Communications 2024年第6期17卷 25-38页

作者： Pradhan, Devasis Behera, Majusha Gheisari, Mehdi Department of Electronics and Communication Engineering Acharya Institute of Technology Karnataka Bangalore560107 India School of Electronics Engineering Campus 12 KIIT deemed to be University Odisha Bhubaneswar India Department of Cognitive Computing Institute of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

The rapid integration of distributed cloud systems in the healthcare industry has profoundly impacted the management of valuable medical data. While this advancement has significantly improved data handling, protecting sensitive healthcare information in such a complex environment remains daunting. This comprehensive study explores the crucial intersection between dynamic data placement strategies and network security concerns in distributed cloud environments, particularly healthcare. After establishing the significance and context of this research, the survey delves into the growing need to safeguard medical data within the everevolving landscape of cloud-based healthcare systems. It lays out fundamental concepts, such as dynamic data placement and network security, highlighting their unique implications in the medical domain. Ultimately, this survey sheds light on the most effective approaches for balancing dynamic data placement and network security in the healthcare sector. This research delves into examining many tactics, evaluating their effectiveness in handling delicate medical information, and presenting tangible use cases. A key focus of this investigation is the fusion of data organization and network safety within the healthcare industry. It investigates the adaptability of dynamic data positioning techniques in fortifying network security and safeguarding against potential threats unique to the healthcare sector. Case studies of the successful implementation of these strategies in healthcare establishments are also included. © 2024, Bentham Science Publishers. All rights reserved.

关键词： Electronic health record

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An Overview of Methodologies and Prototypes involved in Speech Recognition Systems

An Overview of Methodologies and Prototypes involved in Spee...

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2023 International Conference on Advances in Computation, Communication and Information technology, ICAICCIT 2023

作者： Ojha, Ananta Pal, Ritu Batra, Raman Department of Computer Science and Information Technology Bangalore India Tula's Institute Department of Computer Science and Engineering Dehradun India Noida Institute of Engineering and Technology Department of Mechanical Engineering Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350344387

The discipline of voice recognition is characterised by its multidisciplinary nature, incorporating the domains of linguistics, electrical engineering, and computer science. Its primary focus lies in the analysis and interpretation of spoken language. At present, automated voice recognition systems are employed across several fields such as commerce, government, and education, to effectively process mathematical statements. The present study is centred on the identification of impediments in speech recognition systems for mathematical phrases. This article offers a comprehensive examination of the current status of voice recognition techniques, encompassing a broad scope as well as focusing on the specialised domain of mathematical expressions. In this study, we examine many components including feature extraction approaches, classifiers, datasets, recording tools, and performance assessments. © 2023 IEEE.

关键词： Artificial Intelligence (AI) Classification Feature extraction Pattern Recognition Speech Recognition

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Interdisciplinary perspectives on privacy awareness in lifelogging technology development

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Journal of Ambient Intelligence and Humanized Computing 2023年第3期14卷 2291-2312页

作者： Wilkowska, Wiktoria Offermann, Julia Colonna, Liane Florez-Revuelta, Francisco Climent-Pérez, Pau Mihailidis, Alex Poli, Angelica Spinsante, Susanna Ziefle, Martina Human-Computer Interaction Center RWTH Aachen University Aachen Germany Swedish Law and Informatics Research Institute Stockholm University Stockholm Sweden Department of Computer Technology University of Alicante Alicante Spain Department of Occupational Science and Occupational Therapy University of Toronto Toronto Canada Department of Information Engineering Marche Polytechnic University Ancona Italy

Population aging resulting from demographic changes requires some challenging decisions and necessary steps to be taken by different stakeholders to manage current and future demand for assistance and support. The consequences of population aging can be mitigated to some extent by assisting technologies that can support the autonomous living of older individuals and persons in need of care in their private environments as long as possible. A variety of technical solutions are already available on the market, but privacy protection is a serious, often neglected, issue when using such (assisting) technology. Thus, privacy needs to be thoroughly taken under consideration in this context. In a three-year project PAAL (‘Privacy-Aware and Acceptable Lifelogging Services for Older and Frail People’), researchers from different disciplines, such as law, rehabilitation, human-computer interaction, and computer science, investigated the phenomenon of privacy when using assistive lifelogging technologies. In concrete terms, the concept of Privacy by Design was realized using two exemplary lifelogging applications in private and professional environments. A user-centered empirical approach was applied to the lifelogging technologies, investigating the perceptions and attitudes of (older) users with different health-related and biographical profiles. The knowledge gained through the interdisciplinary collaboration can improve the implementation and optimization of assistive applications. In this paper, partners of the PAAL project present insights gained from their cross-national, interdisciplinary work regarding privacy-aware and acceptable lifelogging technologies. © 2022, The Author(s).

关键词： Human computer interaction

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A Comparative Analysis of Deepfake Detection Techniques: A Review 6

A Comparative Analysis of Deepfake Detection Techniques: A R...

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6th International Conference on Contemporary Computing and informatics, IC3I 2023

作者： Quadir, Munleef Agrawal, Prateek Gupta, Charu Lovely Professional University Department of Computer Science and Engineering Punjab India Bhagwan Parshuram Institute of Technology Department of Computer Science and Engineering Delhi India

ISBN: (纸本)9798350304480

Every person in this new generation has access to all of humanity's wisdom. Many technological possibilities are available. Nonetheless, we misuse this benefit by using deepfake for face swap. The deep fake method of machine learning, which is popular on social media, superimposes one person's face over another person's face. Deep learning, which has allowed researchers to produce deepfake images and videos considerably more rapidly and affordably, is the main ingredient in deepfakes. Even though the word 'deepfakes'has a terrible image, more people are using the technology privately and practically. Although it is still fairly new, recent technological advancements have made it more difficult to tell the difference between deep fakes and synthetic images. Deepfake technology is evolving, and there is rising unease. The analysis of deep fakes has already been done by a number of researchers utilizing techniques like GAN, Convolution Neural Network with or without LSTM, Support Vector Machine, and Machine Learning. This paper analyzes the many techniques that have been used by researchers to identify Deepfake videos and rates the performance of multiple Deepfake video detection systems. According to our analysis, integrating CNN and the LSTM together produces superior outcomes and accuracy, which could also be improved by applying the concept of image enhancement. © 2023 IEEE.

关键词： Artificial Intelligence Deepfake Generative Adversarial Networks LSTM Security

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SSDP DDoS Attacks Detection using Naïve Bayes Classifiers with Wrapper Feature Selection Methods 3

SSDP DDoS Attacks Detection using Naïve Bayes Classifiers w...

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Bhadriraju, Manu Sharanya Dasari, Kishorebabu Keshav Memorial Institute of Technology Computer Science and Engineering Department Telangana Hyderabad India Department Telangana Hyderabad India

ISBN: (纸本)9798331518592

One of the most destructive cyber-attacks nowadays is Distributed Denial of Service (DDoS). A Simple Service Discovery Protocol (SSDP) DDoS attack is established by using the advantage of holes in the Simple Service Discovery Protocol to overwhelm a victim network. Early SSDP DDoS attack detection is essential for minimizing consequences. The SSDP DDoS attack detection is analyzed in this work using various Naïve Bayes classifier types, including Gaussian, Bernoulli, Multinomial, and Complement. For feature selection, this study used various wrapper methods such as Forward Feature Selection, Backward Feature Elimination, and Recursive Feature Elimination. The CIC-DDoS2019 dataset is used in this study. The assessment measures used in this study were accuracy, K-Fold Cross-Validation accuracy, ROC-AUC, and log-loss values. Bernoulli Naïve Bayes classifier gives best accuracy value 99.96 with all feature subsets. © 2024 IEEE.

关键词： Feature Selection

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A Conceptual introduction of Machine Learning Algorithms 1

A Conceptual introduction of Machine Learning Algorithms

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1st International Conference on Intelligent Computing and Research Trends, ICRT 2023

作者： Kumar, Manish Ali Khan, Shueb Bhatia, Abhay Sharma, Vibhor Jain, Parag Roorkee Institute of Technology Department of Computer Science & Engineering Uttarakhand Roorkee India KSVCEM Department of Computer Science & Engineering Uttar Pradesh Bijnor India

ISBN: (纸本)9798350336771

The study of statistical models and algorithms that computers use to complete a particular task without external guidance or being explicitly programmed is known as machine learning. Lots of applications that we are using on regularly use ML algorithms. When we use search engine like Google to search the internet, it uses a machine learning algorithm. Teaching computers to maximize a performance criterion based on prior knowledge or sample data is known as machine learning. We have a model with certain parameters stated, and learning is the process of utilizing training data or prior knowledge to run a computer programme to improve the parameters. The model might be pictorial to learn from the data, foretelling to make forecasting, or both. ML algorithms have figured out the way to rank websites. These are used for various type of things, including data collection, data preprocessing, visualization, prediction of data and take decision. The main aid of using machine learning algorithm is that formerly it learns how to handle data, it can finish work independently. This analysis provides a rapid review and viewpoint on the vast applications of machine learning techniques. © 2023 IEEE.

关键词： Machine learning

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Observing Different Machine Learning Approaches for Students’ Performance Using Demographic Features 6th

Observing Different Machine Learning Approaches for Students...

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6th International Conference on Innovative Computing and Communication, ICICC 2023

作者： Srivastava, Neeraj Kumar Pandey, Prafull Mishra, Manoj Kumar Mishra, Vikas Department of Computer Science and Engineering United Institute of Technology Prayagraj Naini India Department of Computer Science and Engineering United College of Engineering and Research Prayagraj Naini India Department of Computer Applications United Institute of Management Prayagraj Naini India

ISBN: (纸本)9789819930098

The use and importance of educational data mining (EDM) are growing very fast as the entire world is going digital. The predictions of EDM [1, 2] are playing very important role not only in improving the academic performance of individual students but also are very useful for institutions, policy makers, administrators to plan and design new policies that will certainly help them to achieve high academic performance of their students. On this basis, this paper approaches students’ performance in secondary school of two Portuguese schools. The informational elements comprise demographic, societal, and educational [3] features. Although there are many ML [4, 5] algorithms, yet in this paper, five ML classifiers such as random forest, Naïve Bayes, logistic regression, decision tree, and support vector machine have been used to predict the students’ performance that on the basis of given features, whether a student can be passed in secondary education or not. On the dataset, the Naïve Bayes classifier gives the best performance than other classifiers, when the dataset is partitioned into 80–20 ratio as training and test datasets. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Decision trees

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