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Smart Systems for applications in Electrical Sciences (ICSSES), International Conference on

作者： N. Bala Krishna R.Yaswanth Kumar M Indira Bhanu P Omprakash Reddy R. Anjali P Reethu Reddy Department of AIML Mohan Babu University(Erstwhile Sree Vidyanikethan Engineering College) Tirupati Department of CSE Mohan Babu University(Erstwhile Sree Vidyanikethan Engineering College) Tirupati

ISBN: (数字)9798350364040

ISBN: (纸本)9798350364057

In this digital world, the excessive use of social media is increasing the incidence of mental health problems, especially depression. Due to the widespread usage of online social media platforms, there is now a chance to proactively treat mental health issues by observing and comprehending users’ behavior. Early detection of depression symptoms is essential for prompt assistance and management. By employing advanced artificial intelligence techniques and machine learning algorithms, such as Long Short-Term Memory and Support Vector machine, this system effectively analyzes users’ social media behavior to determine whether they are experiencing depression and acts as a tool for the early identification of mental health problems, supporting a proactive strategy for enhancing people’s well-being in the digital era. By mining users’ social media interactions and classifying them as potentially depressed or not, this approach helps those in need in a timely manner and delivers insightful information on people’s mental health. Furthermore, the applications of this system extend beyond detecting and preventing online bullying; it can also be used to detect and manage other mental health issues, which in turn helps in emotional well-being. These ideas can be employed in social media applications to check mental stability. This chapter emphasizes the crucial role of textual emotional intelligence in mining users’ social media behavior for detecting depression and other mental health issues. By employing cutting-edge artificial intelligence techniques and machine learning algorithms, this system offers valuable insights and assistance to individuals in need and also helps in maintaining proper e-health.

关键词： Sentiment analysis machine learning algorithms Social networking (online) Static VAr compensators Mental health machine learning Linguistics

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A New Approach for Technological Processes Design Using machine learning algorithms

A New Approach for Technological Processes Design Using Mach...

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International Russian Automation Conference (RusAutoCon)

作者： Aleksandr Laptev Sergey Tretyakov Yuri Andreev Faculty of Control Systems and Robotics ITMO University Saint-Petersburg Russia

ISBN: (数字)9798350349818

ISBN: (纸本)9798350349825

In recent years, there has been an active introduction of digital and automated technologies in Russian mechanical engineering and instrumentation industries. Such systems are CAD, CAPP, MES systems, which allow to automate many processes in production. This study is devoted to CAPP (computer-aided process design systems), paying special attention to the methods used for the design of technological processes. The study showed that the search systems of existing CAPP are all based on manual entry of attributes and subsequent time-consuming analysis of search results, technological processes are dialogue mode. And one of the main factors determining the efficiency and competitiveness of the enterprise is a timely developed and established technological process. In order to increase the efficiency of the technological preparation stage of production, it is proposed a new method for designing technological processes in CAPP through the use of machine learning. Thus, the study made possible the development of a new automated process search system for analogue detail. Also the carried out study provides the algorithm, describing a new process engineering method with machine learning tools.

关键词： Process design Industries machine learning algorithms Automation Instruments Production machine learning Manuals Mechanical engineering

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Data Models based on Time-domain Multi-parameter Information Fusion and machine learning algorithms

Data Models based on Time-domain Multi-parameter Information...

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Intelligent algorithms for Computational Intelligence Systems (IACIS), International Conference on

作者： Yisheng Wang Wuhan University of Technology Wuhan China

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360677

In the case of uncertain system parameters, it is not possible to directly perform state estimation. The state estimation of the system model requires known model parameters and noise variance. Therefore, before conducting state estimation, it is necessary to identify the unknown parameters in the model and input the identification results into the system model for state estimation. This article applied various new recognition technologies to fuse time-domain multi-parameter information, thereby reducing data dimensions and improving recognition accuracy. However, the current data obtained is often not accurate and comprehensive enough to fully reflect system characteristics. Therefore, it is particularly important to use information fusion methods for state estimation. This study introduced a mathematical model based on principal component analysis (PCA) and clustering algorithm, which combined PCA and genetic algorithm (GA) to improve the clustering algorithm and enhance the effectiveness of data preprocessing and analysis. The effectiveness of the introduced GA-PCA model in improving classification accuracy and computational efficiency was verified through simulation experiments on multiple datasets.

关键词： Dimensionality reduction Accuracy machine learning algorithms Feature extraction Mathematical models State estimation Time-domain analysis Optimization Principal component analysis Genetic algorithms

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Leveraging machine learning algorithms to Optimize Diet Maintenance Based on User Food Intake and Fitness Data

Leveraging Machine Learning Algorithms to Optimize Diet Main...

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Sustainable Communication Networks and Application (ICSCNA), International Conference on

作者： K. Clarinda N. Deepa R. Dhamotharan R. Santhana Krishnan J. Relin Fransis Raj J. V. Anchitaalagammai Department of Management Studies SCAD College of Engineering and Technology Cheranmahadevi India Department of Artificial Intelligence and Data Science PSNA College of Engineering and Technology Dindigul India Department of Computer Science and Information Technology Kalasalingam Academy of Research and Education Krishnankoil India Department of Electronics and Communication Engineering SCAD College of Engineering and Technology Cheranmahadevi India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computer Science and Engineering Velammal College of Engineering and Technology Madurai India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530020

The increasing prevalence of lifestyle-related health issues has necessitated the development of innovative approaches to diet management. This research introduces the Adaptive Eating Pattern Predictor (AEPP), a novel machine learning algorithm designed to optimize diet maintenance by analyzing user food intake and fitness data. The AEPP combines the predictive capabilities of Artificial Neural Networks (ANN) with the probabilistic modeling of Markov Chains, enabling accurate forecasting of future eating patterns based on historical dietary and activity data. A comprehensive simulation analysis was conducted to evaluate the performance of the AEPP against existing algorithms, including traditional recommendation systems and basic machine learning models. Various simulation metrics, such as accuracy, precision, recall, and F1 score, were employed to compare the effectiveness of the AEPP in providing personalized dietary recommendations. The results demonstrate that the AEPP significantly outperforms existing algorithms, highlighting its potential as a valuable tool for individuals seeking to maintain a balanced diet in alignment with their fitness goals. This study contributes to the field of nutritional science by showcasing the advantages of integrating advanced machine learning techniques in personal diet management.

关键词： Adaptation models machine learning algorithms Accuracy Adaptive systems Collaboration Artificial neural networks Predictive models Prediction algorithms Maintenance Wearable devices

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Classification of Plant Disease using machine learning algorithms

Classification of Plant Disease using Machine Learning Algor...

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Computational Intelligence and Communication Technologies (CCICT), International Conference on

作者： Majji V Appalanaidu G Kumaravelan Department of Computer Science Engineering Hyderabad Institute of Technology and Management Hyderabad India Department of Computer Science Pondicherry University Karaikal Campus Karaikal

ISBN: (数字)9798350374629

ISBN: (纸本)9798350374636

The identification of plant diseases has become a key topic in smart agriculture research in recent years. The productivity of agricultural products will undoubtedly increase with early plant disease identification. For the farmers, it is difficult to determine the specific diseases affecting the plant leaves. Therefore, the development of an automated system becomes an essential practice for detecting diseases. So that the remedies can be suggested according to the disease’ s severity. In this situation, image processing methods combined with machine learning (ML) algorithms have lately offered an accurate and dependable way of identifying and categorizing many plant diseases. Hence, this work investigates the importance of the deployment of various ML algorithms such as Naïve Bayes (NB), Decision Tree (DT), Artificial Neural Networks (ANN), K-Nearest Neighbor (KNN), and Support Vector machine (SVM) in automatic classification of plant diseases on the benchmark plant village dataset.

关键词： Support vector machines Smart agriculture Productivity Plant diseases machine learning algorithms Image processing Artificial neural networks

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Revolutionizing Culinary Quality: An Intelligent Food Grading System Employing Advanced machine learning algorithms

Revolutionizing Culinary Quality: An Intelligent Food Gradin...

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International Conference on Advanced Computing and Communication Systems (ICACCS)

作者： C Benin Pratap Aren D'Souza Athulya Amblish Jane Mary Mathew Srivenu K Leno Varghese Babu Department of Electrical and Electronics Engineering Karunya Institute of Technology and Sciences Coimbatore India Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Department of Food Processing Technology Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384376

In this pioneering research, a transformative paradigm in culinary quality assessment through the implementation of an Intelligent Food Grading System (IFGS) powered by state-of-the-art machine learning algorithms is introduced. The primary objective is to revolutionize conventional food quality evaluation methods by introducing a dynamic, precise, and automated approach. The IFGS employs advanced machine learning techniques to comprehensively analyze diverse food attributes, including taste, texture, freshness, and nutritional content. This system provides a standardized and objective framework for evaluating food quality. Moreover, it caters to consumer expectations by ensuring transparency and reliability in the assessment of food products. The study delves into the intricacies of the machine learning algorithms incorporated in the IFGS, emphasizing their adaptability and continuous learning capabilities. The synergy between technological innovation and culinary expertise presented in this work encourages a broader conversation within the culinary community, inviting chefs, researchers, and enthusiasts to embrace a future where cutting-edge technology enhances and refines the culinary experience.

关键词： Technological innovation Privacy machine learning algorithms Prevention and mitigation Oral communication machine learning Quality assessment Reliability Optimization Testing

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A Comparative Study of machine learning algorithms for Classifying Personality Type based on Carl Jung's Cognitive Function

A Comparative Study of Machine Learning Algorithms for Class...

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International Electronics Symposium (IES)

作者： Entin Martiana Kusumaningtyas Tirta Rumy Swandaru Tri Hadiah Muliawati Iqbal Bimantoko Department of Informatics and Computer Engineering Politeknik Elektronika Negeri Surabaya Surabaya Indonesia Clinical Psychology Puskesmas Balas Klumprik Surabaya Indonesia

ISBN: (数字)9798350391992

ISBN: (纸本)9798350392005

According to Carl Jung, every human being has a personality preference from each dichotomy dimension of cognitive function to live life. Cognitive function is mental attitudes and actions that always coexist with humans. This theory is the basis for developing personality assessment instruments, such as the Myers-Briggs Type Indicator (MBTI). On the other hand, machine learning (ML) approach is often used in psychology to classify personality types automatically. However, selecting the most suitable algorithm and developing the instruments for data collection is not an easy task. This paper aims to conduct experiments choose the most appropriate algorithm for classifying personality types based on Carl Jung's theory of cognitive function. In collaboration with a psychologist, we adapt Carl Jung's theory of cognitive function into a text-based personality assessment instrument consisting of several items. The experimental results show that the Stochastic Gradient Descent algorithm with hyperparameter tuning consistently yields good performance in carrying out cognitive function personality type classification tasks.

关键词： Support vector machines machine learning algorithms Accuracy Instruments Psychology Stochastic processes Prediction algorithms

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Determining Positive-Negative Emotions in Male and Female Based on EEG Signals using machine learning algorithms

Determining Positive-Negative Emotions in Male and Female Ba...

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International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)

作者： Yuri Pamungkas Edwin Nugroho Njoto Medical Technology Institut Teknologi Sepuluh Nopember Surabaya Indonesia Department of Medicine Institut Teknologi Sepuluh Nopember Surabaya Indonesia

ISBN: (数字)9798350355314

ISBN: (纸本)9798350355321

Emotionsare vital in everyday human life as a controller of behavior, decision-making and as a means to determine product marketing strategy/ market research. All of these things are very dependent on human emotional conditions. In addition, in the development of the computational affective field, brain signal-based emotion recognition (EEG) has become a trending topic of current research. Therefore, we attempted to compare positive-negative emotions in men and women based on EEG using a machine learning algorithm in this study. A total of 20 male and 20 female participants recorded their EEG signals in the frontopolar and frontal areas of the brain. Then the EEG data is processed by filtering, removing artifact, and decomposing it into three sub-bands (alpha, beta, and gamma). The extracted signal features are Mean Absolute Deviation and Power Spectral Density. Based on the signal feature analysis results, it is known that the signal feature values (MAD and PSD) for women tend to be higher than for men. Meanwhile, several algorithms are used to classify positive and negative emotions, such as Naive Bayes, K-Nearest Neighbor, Support Vector machine, and Random Forest. Based on the results of classification, the best accuracy rate was 95.8% (on positive emotions for male & female gender), 92.2% (on negative emotions for male & female gender), and 79.8% (on positive-negative emotions for male & female gender) using Random Forest algorithm.

关键词： Support vector machines Emotion recognition machine learning algorithms Accuracy Nearest neighbor methods Feature extraction Market research Electroencephalography Classification algorithms Random forests

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machine learning algorithms in shipping: improving engine fault detection and diagnosis via ensemble methods

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WMU JOURNAL OF MARITIME AFFAIRS 2020年第1期19卷 51-72页

作者： Tsaganos, G. Nikitakos, N. Dalaklis, D. Olcer, A., I Papachristos, D. Merchant Acad Athens Dept Marine Engn Athens Greece Dept Shipping Trade & Transport Chios Greece World Maritime Univ WMU Malmo Sweden Dept Ind Engn & Prod Chios Greece

Detection and diagnosis of marine engines faults are extremely important functions for the optimized voyage of any sea-going vessel, as well as the safe conduct of navigation. Early detection of these faults is a prerequisite for reliability: incidents of engine breakdowns can be avoided, since the timely resolving of these faults can ensure the non-interrupted tempo of the sail. Avoiding malfunctions could also improve the ship's overall environmental "footprint" and even ensure reduced fuel consumption. Initial results of the analysis at hand were presented during the 3rd International Symposium on Naval Architecture and Maritime (INT-NAM 2018), in Istanbul-Turkey. Further exploring the use of machine learning algorithms in shipping and by elaborating more on that effort, an evaluation of intelligent diagnostic methods applicable for a two-stroke slow-speed marine diesel engine is taking place, with the aim to facilitate effective detection and classification of occurring faults. This research was carried out via the cost-free Weka data mining tool, which was used to analyze the data of the engine's operating parameters that were found outside of the prescribed boundaries. The proposed method is based on the construction of an ensemble classification model "AdaBoost", which further improves the performance of a basic Simple Cart classifier. During the related experimental activities, the overall recorded performance was 96.5%, clearly establishing this method as a very appropriate choice.

关键词： Marine diesel engine Fault detection machine learning algorithms Ensemble methods Weka Cross-validation Confusion matrix

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Improving Performance of Supervised machine learning algorithms on Small Datasets

Improving Performance of Supervised Machine Learning Algorit...

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Ubiquitous Computing and Intelligent Information Systems (ICUIS), International Conference on

作者： B.Rajesh Kumar R. Ramya Albin George Kurian Jithin J Ram Prasad Sana Fathima Ameesha Suresh Dept.of CSE Dhanalakshmi Srinivasan College of Engineering Coimbatore Tamilnadu

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

In most cases, machine learning involves working with large volumes of data in order to perform optimally. However, in some cases, there is only a limited amount of training data available, resulting in a decrease in the performance of the model. The current research aims to enhance the effectiveness of machine learning models, which have been developed utilizing limited services. In this case study, the focus is on learning using a multi-iteration approach along with Gaussian Mixture Model GMM-based Synthetic Data Generation, which were suggested as solutions to the problem of data scarcity. The research explores how much these methods boost a model's performance in the performance metrics

关键词： Measurement Accuracy machine learning algorithms Active learning Training data machine learning Ubiquitous computing Data models Gaussian mixture model Synthetic data

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