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2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Motghare, Renuka Bhoyar, Shivam Verma, Prateek Tale, Abhay Barhate, Aditya Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Sawangi Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Sawangi Maharashtra Wardha442001 India

ISBN: (纸本)9798331528713

Stress, a widespread issue affecting both individual well-being and societal productivity, has attracted significant research interest. Defined as a state of mental or emotional strain, stress arises from various factors such as work, relationships, and health concerns, manifesting physically, emotionally, and behaviorally. This paper provides a review of Machine learning (ML) techniques to detect mental stress using physiological signals like ECG, GSR, and EEG. Through a review of existing studies, the paper highlights the progress made in this area and the challenges that remain, particularly concerning data quality, algorithm accuracy, and the practical deployment of stress monitoring devices. The study underscores the potential for real-time monitoring and personalized interventions, especially in daily activities. Future research should focus on enhancing model robustness, integrating multiple data sources, and addressing real-world applicability. This paper emphasizes the importance of early stress identification and personalized treatment to improve mental health outcomes © 2024 IEEE.

关键词： Adversarial machine learning

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Application of the Semantic Segmentation Method in the Assessment of Forest Carbon Balance 16

Application of the Semantic Segmentation Method in the Asses...

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16th International Conference Management of Large-Scale System Development, MLSD 2023

作者： Tulokhonova, Inna Mikhaylova, Svetlana Danilova, Soelma East Siberia State University of Technology and Management Department of Software Engineering and Artificial Intelligence Ulan-Ude Russia Financial University under the Government of the Russian Federation Department of Data Analysis and Machine Learning Moscow Russia

ISBN: (纸本)9798350337907

This article focuses on the problem of human impact on the natural environment and its solution by machine learning methods. The concept of carbon balance is key in assessing climate change on the planet, which is especially important in connection with global warming. The main causes of global warming are associated with anthropogenic emissions of carbon dioxide as a result of agricultural production, land use and burning of fossil fuels. One of the approaches to mitigate this environmental problem is to estimate the carbon balance based on neural network semantic segmentation of forests of terrestrial ecosystems. The proposed convolutional neural network (CNN) model is based on the U-Net architecture. This architecture surpasses traditional machine learning methods in solving computer vision and pattern recognition problems, and is also quite easy to learn. The input data of the model is a set of satellite images of the forest area. The images were obtained using the Sentinel-2 satellite, which provides high spatial resolution and a large number of spectral channels. A software prototype has been developed to implement the CNN model. Training experiments and calculations on changes in the carbon balance in forests led to the conclusion that the results of the study will contribute to the balanced management of reforestation and carbon deposition. © 2023 IEEE.

关键词： Global warming

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LSTM based Soft-Sensor for Estimating Nitrate Concentration in Aquaponics pond

LSTM based Soft-Sensor for Estimating Nitrate Concentration ...

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Innovation in technology (INOCON), IEEE International Conference for

作者： A. Dharshan Purushottam Kumar S. Ravimaran U Srinivasulu Reddy Department of Artificial Intelligence and Data Science Saranathan College of Engineering Trichy India CoE in Artificial Intelligence Machine Learning & Data Analytics Lab National Institute of Technology Trichy India Department of Computer Applications Machine Learning & Data Analytics Lab National Institute of Technology Trichy India

In the field of aquaponics, where fish and plants coexist in a symbiotic environment, closely monitoring nitrate levels in the water is crucial due to their profound impact on aquatic and plant well-being. Traditional nitrate measurement methods are often time-consuming and costly. Various approaches, including first principles, IoT-based sensors, and machine learning-based soft sensors, have been attempted to address this challenge. However, these efforts face challenges such as expensive sensors, infrequent data collection, multistage data processing using limited sensor types, and the need for regular maintenance like cleaning and calibration. Additionally, varied environmental conditions affect sensor suitability for different water environments, and even some machine learning-based soft sensors have proven inaccurate. In response, soft sensors, especially deep learning-based ones, have gained prominence in industrial applications for their adaptability and accuracy. These sensors provide real-time insights into complex processes without requiring expensive hardware. In this study, an innovative solution was introduced using Long Short-Term Memory (LSTM) technology, a neural network architecture in deep learning known for capturing complex temporal patterns. LSTM is well-suited for modeling and predicting nitrate concentration changes in aquaponics, trained with extensive data collected from various aquaponic ponds. Through rigorous evaluation, a remarkable MSE value of 0.00074 and an impressive R-squared score of 0.98 were achieved, holding potential for scaling up to commercial applications, benefiting aquaponics operations, supporting researchers, and enhancing sustainability and productivity in aquaponic systems.

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Automated coding of student chats, a trans-topic and language approach

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Computers and Education: Artificial Intelligence 2023年 4卷

作者： de Araujo, Adelson Papadopoulos, Pantelis M. McKenney, Susan de Jong, Ton Department of Learning Data Analytics and Technology University of Twente Postbox 217 Enschede7500 AE Netherlands

Computer-Supported Collaborative learning (CSCL) is known to be productive if well structured. In CSCL, students construct knowledge by performing learning tasks while communicating about their work. This communication is most often done through online written chats. Understanding what is happening in chats is important from both research and practical perspectives. From a research perspective, insight into chat content offers a window into student interaction and learning. From a more practical standpoint, insight into chat content can (potentially) be used to trigger supportive elements in CSCL environments (e.g., context-sensitive tips or conversational agents). The latter requires real-time, and therefore automated, analysis of the chats. Such an automated analysis is also helpful from the research perspective, since hand-coding of chats is a very time and labour-consuming activity. In this article, we propose a new machine learning-based system for automated coding of student chats, which we labelled ConSent. The core of ConSent is an algorithm that uses contextual information and sentence encoding to produce a reliable estimation of chat message content (i.e. code). To optimize usability, ConSent was designed in such a way that it can cover various topics and various languages. To evaluate our approach, we used two sets of chats coming from different topics (within the domain of physics) and different languages (Dutch and Portuguese). We tested different algorithm configurations, including two multilingual sentence encoders, to find the model that yields the best reliability. As a result, analysis revealed that ConSent models can perform with substantial reliability levels and are able to transfer reliable coding of chats in a similar topic and different language. Finally, we discuss how ConSent can form the basis for a conversational agent, we explain the limitations of our approach, and we indicate possible paths for future work to contribute towards reliabl

关键词： Machine learning

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A Review of Machine learning Algorithms in Diabetes Management 4

A Review of Machine Learning Algorithms in Diabetes Manageme...

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4th International Conference on Sentiment Analysis and Deep learning, ICSADL 2025

作者： Gayaki, Unnati Daronde, Subodh Tale, Abhay Barhate, Aditya Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Engineering Maharashtra Wardha India Faculty of Engineering and Technology Department of Biomedical Engineering Maharashtra Wardha India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha India

ISBN: (纸本)9798331523923

Diabetes is a prevalent and chronic disease affecting millions worldwide, posing significant challenges in its management and treatment. This review article aims to explore the current and potential future roles of machine learning (ML) in diabetes management, focusing on progress, applications, and challenges. It discusses various ML techniques, including support vector machines (SVM), logistic regression, decision trees, ensemble methods, and advanced deep learning approaches such as long short-term memory (LSTM) networks and convolutional neural networks (CNN). These methods have demonstrated benefits in analytics, patient monitoring, decision support systems, personalised treatment, and patient engagement. However, significant challenges persist, such as computational complexity, data quality, limited interoperability of systems, and ethical concerns regarding AI use in healthcare. The article addresses these challenges by fostering collaboration between AI implementers and health care professionals. This collaboration is essential to develop long-term, legally compliant, and solution-oriented frameworks that enhance patient care and diabetes management and pave the way for more efficient and ethical AI applications in healthcare. © 2025 IEEE.

关键词： Support vector machines

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Functional and Non-Functional Requirement (FR and NFR) Formation Based on Requirement Elicitation Using Text Semantics in IdVar4CL Artifact 10

Functional and Non-Functional Requirement (FR and NFR) Forma...

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10th International Conference on Signal Processing and Communication, ICSC 2025

作者： Fatimatuzzahra, Nayla Priyadi, Yudi Nurtantyana, Rio Telkom University Department of Information Technology Bandung Indonesia Telkom University Department of Software Engineering Bandung Indonesia Center of Excellence Artificial Intelligence for Learning and Optimization Research Center for Data and Information Sciences Bandung Indonesia

ISBN: (纸本)9798331511890

Elicitations are mandatory in establishing both Functional Requirements (FR) and Non-Functional Requirements (NFR). One of the challenges in software engineering is to ensure that Requirement Elicitation (RE) is accurately converted to well-structured FRs and NFRs in Software Requirements Specification (SRS) documentation. However, traditional approaches for requirements gathering often face challenges such as incomplete information, evolving requirements, and lack of stakeholder involvement. To address this problem, we adopted agile methodologies in software development. Hence, this study proposed to form FR and NFR based on RE through a semantic text process. The results showed that we have been successful in establishing FR and NFR on the RE process using text semantics in the IdVar4CL artifact, which included 29 documents (d1-d29), consisting of 12 RE documents, 10 FR documents and 7 NFR documents. In detail, the list of Requirement Specification tabulations in Subject, modals, Verb, Object format. Semantic similarity through comparison of the elicitation documentation with the SRS documentation results in a difference of 0.09 as a reference to subsequent SRS artifact formation improvements. Hence, this approach improves the clarity and alignment of requirements using agile development. © 2025 IEEE.

关键词： Specifications

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Smart Telemedicine Using IoT by Integrating 5G and Block-Chain Techniques 6

Smart Telemedicine Using IoT by Integrating 5G and Block-Cha...

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

作者： Choudhary, Shailee Lohmor Dixit, Rinku Sharma Das, Deepak Ranjith Singh, K. Dinesh Babu, V. New Delhi Institute of Management Department of Artificial Intelligence & Machine Learning - Data Science New Delhi India Karunya Institute of Science and Technology Agricultural Engineering Coimbatore India Karpagam Academy of Higher Education Department of Computer Science Coimbatore India Karpagam Institute of Technology Department of Information Technology Coimbatore India

ISBN: (纸本)9798350304480

There are many medical applications for the Internet of Health Things (IoHT). In order to offer patients with more intelligent and effective health diagnoses, contemporary IoHT incorporates wellness items such sensors with continuously monitored medical equipment for the evaluation and handling of a medical chart. Using the combination of 5G networks and ethereum technology, we presented the IoT using a fog medical decision assistance system in this study for the forecast and monitoring of illness along with its severities. Due to its visibility, a frame provides a method for keeping and exchanging knowledge which is safe. Ethereum has various uses in the medical industry and therefore can enhance monitoring equipment, clinical study storage systems, mobile medical software, exchanging and keeping of digital media files, and insurer details stockpiling. The suggested structure will assemble a cloud provider will hold client information from the health equipment that's going to be connected to the individual, along with any pertinent health records. The use of 5G with cryptocurrency technologies allows the reliable transfer of patient information at high transmission rates with short reaction times. In addition, a Deep Neural (NN) classification is employed to forecast illnesses as well as the degree of underlying severity. The suggested model is tested using a variety of classifiers. In order to determine which classification is appropriate for the information, the effectiveness of various classifier is compared by contrasting the results. The NN improved a 98.98 average accuracy. This NN is additionally trained on the information such so this can forecast the outcome of the information category that carries no labels. A taught Naive Bayes is more accurate than conventional classifications at making predictions and effectively displaying the findings. © 2023 IEEE.

关键词： 5G Blockchain CDSS DNN IoHT IoT

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Abnormal Load Patterns Detection in Smart Grids Using Temporal Convolutional Neural Network Based Gated Recurrent Units Networks with Multi-Head Temporal Attention 4

Abnormal Load Patterns Detection in Smart Grids Using Tempor...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Balassem, Zayd Srikanteswara, Ramya Esakki Madura, E. Anjaneyulu, Madugula Devi, M. College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bengaluru India Bannari Amman Institute of Technology Department of Artificial Intelligence and Data Science India Gokaraju Rangaraju Institute of Engineering and Technology Department of Artificial Intelligence and Machine Learning Hyderabad India New Prince Shri Bhavani College of Engineering and Technology Department of Electrical and Electronics Engineering Chennai India

ISBN: (纸本)9798350352931

In recent era, the advancement in smart grid technologies and the integration of renewable energy sources have modernized the power distribution landscape. The efficiency and stability of overall system is ensured by the controlling of power converters. In existing, Support Vector Machine (SVM) is utilized but it assumes the normal data follows Gaussian distribution which is not be the same for smart grid data. In this research, Temporal Convolutional Neural Network based Gated Recurrent Units Networks with Multi-Head Temporal Attention (TCN-GRU-MTA) is proposed. Initially, the input signals are considered from Smart Grid Stability (SGS) dataset. Then, the signals are further preprocessed with autoencoders to reduce noise and to improve quality of signals. The Siamese Neural Networks (SNN) feature extractor efficiently learns relevant features from smart grid data which are indicative of abnormal behavior. After that, the SNN is introduced for extracting features from preprocessed data. Next, the proposed TCN-GRU-MTA is utilized to detect the abnormal load patterns in smart grid networks. From the results, the proposed-established better results including accuracy (99.10%), precision (89.98%), recall (99.05%) and F1-score (95.20%) when compared to existing Quadratic SVM. © 2024 IEEE.

关键词： Smart power grids

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Impact of Immersive technology on Physiological Parameters in Psychiatric Disorders

Impact of Immersive Technology on Physiological Parameters i...

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International Conference on System Modeling & Advancement in Research Trends (SMART)

作者： Anjali Kataria Gurjinder Singh Jasminder Sandhu Chitkara Institute of Engineering and Technology Punjab India Department of Machine Learning and Data Science IILM University Noida

ISBN: (数字)9798350380583

ISBN: (纸本)9798350380590

The brain is an essential component that regulates the general functioning of the body. The brain consists of millions of neurons that govern human behavior in response to sensory stimuli. To understand the mental functioning of the brain, it is important to assess its neural signals. Presently, the exponential expansion of prescription medications and substances, including alcohol and drugs, is exerting a significant influence on human psychology. The rising prevalence of different health disorders can be attributed to the increasing number of cases. The sensorimotor cortex, which in humans is in charge of coordinating both physical and cognitive activities, can be used to visualize brain signals. The present research describes an empirical investigation that employs Virtual Reality therapy to examine the affective condition of individuals suffering from drug addiction. The study utilized the Emotiv Insight 5-channel EEG headset to collect EEG data from 50 participants, and the Emotiv Pro software was employed for data analysis. The study findings indicate that Virtual Reality treatment has a substantial positive impact on the stress levels and concentration of persons suffering from drug addiction.

关键词： Drugs Wavelet transforms Addiction Neurons Psychology Medical treatment Virtual reality Electroencephalography Software Physiology

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A Review on Machine learning Assisted Drying Behaviour Prediction of Agricultural Seeds and Fruits 2

A Review on Machine Learning Assisted Drying Behaviour Predi...

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2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Jawre, Sandip Verma, Prateek Kothare, Chandrakant B. Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha India Shri Shankarprasad Agnihotri College of Engineering Department of Mechanical Engineering Maharashtra Wardha India

ISBN: (纸本)9798331528713

Machine learning approaches have been more popular in recent years across many scientific and engineering fields, with the potential to improve drying behavior and thermal property predictions in terms of accuracy and efficiency. Agricultural seeds and fruits must be handled carefully after harvest because drying has a considerable impact on their quality, shelf life, and overall worth. To optimize drying processes and guarantee product quality, drying kinetics and thermal characteristics must be accurately predicted. This review article outlines the basic principles of drying, emphasizes their significance in determining the ideal drying conditions, and provides a thorough overview of the application of machine learning methods for predicting the drying kinetics and thermal properties of agricultural seeds and fruits. Then, in the context of their applications to drying prediction, a summary of several machine learning approaches, including artificial neural networks, support vector machines, and decision trees have been discussed. © 2024 IEEE.

关键词： Fruits

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