bluuebunny/crossref_metadata_2025 huggingface dataset:
Kharkiv International Competition
Authors: Smolenska Svitlana
Published: April 2024

One of the sides of a large study dedicated to the forgotten international competition of 1930 for the Project of the State Ukrainian Theater of Mass Musical Action in Kharkiv is touched upon in this article. Its purpose is the reconstruction of the competition events, identifying the features of the process of their organization. The competition attracted a record number of foreign contestants not only from many European countries, but also from other continents (from the USA and Japan). Why did it generate such widespread interest? Which famous architects took part in it? Who evaluated the projects and according to what criteria were the awards distributed? The author of this article is looking for answers to these questions. The problems of the research are due to the fact that the originals of the competition projects have not survived, and their photocopies and preparatory sketches are scattered in the archives of many countries. Information about the competition and its contestants is scarce and is documented in different languages: Ukrainian, German, French, Russian, Japanese, English, Croatian, Swedish. Only painstaking gathering, meaningful and comparative analysis of textual and graphical information obtained during the study, allows the author to reproduce the course of the competition, to reveal its significance for the development of architecture in Ukraine and for the world Modern Movement. The article analyses the methods that ensured a high level of organization of the competition and an open, unbiased assessment of its results. The distribution of prizes and the authors of the winning projects are also listed. The Kharkiv competition took place at a crucial period for the Soviet avant-garde: 1930-1931 were the last years of its heyday, after which it was banned and persecuted for many years. That is why it is so important to collect these lost puzzles of architectural history.
Heat Stress and Cultivable Intestinal Bacteria of Lehmann Brown Hens
Authors: Harutyunyan N.A. , Chitchyan Zh.T. , Badalyan M.V. , Pepoyan A.Z.
Published: April 2024

The use of genetic methods seems to have briefly pushed back the use of culture methods. Currently, a new technique developed based on the latter – culturomics, enables the discovery and study of expanded bacterial diversity using a large number of culture conditions and media. This study aims to investigate the influence of heat shock on the qualitative and quantitative content of cultivable gut bacteria of Lehmann Brown chickens from the “Arax” chicken factory.The chickens (5 months old, weighing 1.4-1.7 kg) of the “Arax” poultry factory in Armenia were transferred to a sterilized test room with the necessary conditions. After four days of maintenance in a new environment (temperature 25 0C), fecal samples were taken from the hens. After that, the hens were kept at 35 0 C for a day, followed by another sampling conducted a day later. The current investigations have emphasized that the bacterial number and diversity of hen gut microbiota undergo changes under the influence of heat stress.
Psychology of doctor-patient interaction in the context of medical specialist's communicative tolerance
Authors: Shcherban Tetiana D. , Dolynay Tyberiy
Published: April 2024

The relevance of the study is due to the need to find ways to ensure high-quality interaction between a doctor and a patient in the context of a medical professional's communicative tolerance. The purpose of the study was to determine the psychological characteristics of the interpersonal relationship between a doctor and his/her patients. The basis of the theoretical and methodological approach was a qualitative combination of methods of structural and functional analysis of the problem of psychological training of medical professionals and analytical research on the formation of constructive relationships between doctor and patient. Several surveys were conducted on the tolerance of healthcare professionals and their emotional well-being in the context of individual psychological characteristics. The results are presented, which reflect the methods of doctor's influence on the psychological state of the patient and his/her family. The question of how trust in a doctor and his/her professional competences is formed is highlighted. The problem of ethical principles, professional motivation of medical professionals and the specifics of their communication in the team are revealed. The subjective feelings of doctors about the work they do are revealed. The issues of modernization of medical education and digitalization of services are revealed. The conditions, components, and approaches to effective interaction between doctor and patient in the healthcare system are identified. The problems of developing diagnostic tools for assessing the psychological state of a medical professional in his/her professional activity are detailed. An experiment was conducted to substantiate the need to implement programmes to improve the communication skills of healthcare professionals, including interns and nurses. The practical value of the results of the research work is the possibility of using the outlined approach to improve the methods of improving the communication competencies of doctors in their professional activities
Distance education in the field of obstetrics, gynaecology, and ultrasound diagnostics in the conditions of quarantine restrictions and hostilities: A literature review
Authors: Sokolovska Iryna , Nakonechniy Serhii , Ganzhiy Iryna
Published: April 2024

Forced distancing in the context of the COVID-19 pandemic has necessitated the transition of medical education to distance learning. The problem was exacerbated by the outbreak of full-scale hostilities, which necessitated further research in this area. The purpose of the study was to comprehensively review current scientific materials and consolidate existing knowledge on educational activities in obstetrics, gynaecology, and ultrasound diagnostics in the context of the coronavirus pandemic and full-scale hostilities. The following methods were used for the study: bibliographic, analysis, synthesis, induction, generalization, analogy, systematization, and categorization. The article presents a comprehensive analysis of the current literature on overcoming pedagogical challenges in the context of the COVID-19 pandemic and military operations through the achievements of distance education. The methodological foundations of distance education, the peculiarities of applying this practice in a pandemic and military operations, the similarities, and differences between them, especially in the context of obstetrics, gynaecology and ultrasound diagnostics, are considered. The connection between telemedicine and distance learning is presented both at the level of theory and practice. The practice of distance learning in obstetrics and gynaecology is compared with some other clinical disciplines. The result of the study was to solve the problem of the lack of a systematic view of the adaptation of the educational process to the conditions of a pandemic and military operations as emergency distance learning, rather than the classical practice of distance education, which contributes to the formation of an adequate assessment of pedagogical activities conducted in such conditions, in particular, in the field of obstetrics, gynaecology, and ultrasound diagnostics. The results of the study are of practical importance in the field of medical education, where they can be used to develop recommendations for the organization of training in obstetrics and gynaecology or other medical disciplines in conditions of forced distance learning
Effects of Conflict Management Strategies on Employees’ Performance in the University of Calabar Teaching Hospital, Calabar, Nigeria
Authors: A. A. Ele , B. O. Ekpenyong , N. J. Okongo , S. I. Eneh , N. N. Arikpo
Published: April 2024

This study evaluated the effects of conflict management strategies on employees’ performance in the University of Calabar Teaching Hospital, Calabar. The study employed survey research design in order to utilize a structured questionnaire in the collection of data for analysis. The population for the study was five hundred and fifty (550). The sample size for the study was two hundred and twenty six (226) using Krejcie and Morgan table of 1970. The hypothesis was tested and analyzed using a simple linear regression analysis model applying a statistical package for social sciences (SPSS) version 23. The study found that there are positive effects of conflict avoidance strategy on employees’ performance of the University of Calabar Teaching Hospital, Calabar. There is a significant effect of conflict collaboration strategy on employees’ commitment to goals achievement. There is a significant relationship between conflict accommodation strategy and employees’ motivation. There is also a significant conflict mediation strategy on employees’ involvement in the University of Calabar Teaching Hospital, Calabar. Based on the findings, the study recommended that management of organizations or institutions should monitor and intervene on issues that can lead to conflicts in order to enhance employees’ performance by adopting avoidance strategies. Management should employ a collaboration strategy or approach of win-win method of conflict resolution in order to enhance employees’ commitment. Management should also attend to conflicting situations as urgently as possible in order to avoid those factors that can hinder employees’ motivation and commitment to goal achievement. Management should also use a mediator in resolving conflicts on a continuous basis.
Fresh goat's milk and Long-term aerobic exercise improve bone health in Athletes
Authors: Yusni Yusni , Syahrul Syahrul , Murzalina Cut
Published: April 2024

Objective: To analyze the effect of fresh goat's milk intake and long-term aerobic exercise on serum calcium and its relationship to bone resorption in male athletes. Methods: This research was an experimental study using a pretest-posttest design with a control group design. Subjects were 29 gymnastics athlete volunteers: trial group (n=19) and control group (n=10), male, aged 19-26 years. The treatments were fresh goat milk (1x250ml/day, given from 07.00 p.m to 08.00 p.m) and aerobic exercise (3-5 times/week, medium intensity, duration 50-60 minutes), for 90 days. Results: The results showed that serum calcium levels in the trial group were significantly higher than in the control group (9.58±0.26; 9.87±0.32 vs. 9.74±0.42; 9.37±0.38: p=0.001) after consumption of goat milk and long-term regular physical exercise. The results also showed that there was a significant decrease in CTx-1 levels in the trial group compared with the control group (0.603±0.25; 0.45±0.16 vs. 0.73±0.32; 0.76±0.30: p=0.001) after goat milk intake and regular aerobic exercise. Conclusion: Consumption of fresh goat's milk and long-term aerobic exercise increased calcium levels and decreased CTx-1 levels. Therefore, the combination of both therapies reduces bone resorption, and fresh goat's milk is allowed to be consumed regularly as a sports nutrition to reduce the risk of premature osteoporosis in athletes.
Optimizing Disease Prediction with Artificial Intelligence Driven Feature Selection and Attention Networks
Authors: S. Edwin Raja, M. Thiyagarajan, J. Jeno Jasmine, P. Raghavan D. Dhinakaran,
Published: April 2024

The rapid integration of machine learning methodologies in healthcare has ignited innovative strategies for disease prediction, particularly with the vast repositories of Electronic Health Records (EHR) data. This article delves into the realm of multi-disease prediction, presenting a comprehensive study that introduces a pioneering ensemble feature selection model. This model, designed to optimize learning systems, combines statistical, deep, and optimally selected features through the innovative Stabilized Energy Valley Optimization with Enhanced Bounds (SEV-EB) algorithm. The objective is to achieve unparalleled accuracy and stability in predicting various disorders. This work proposes an advanced ensemble model that synergistically integrates statistical, deep, and optimally selected features. This combination aims to enhance the predictive power of the model by capturing diverse aspects of the health data. At the heart of the proposed model lies the SEV-EB algorithm, a novel approach to optimal feature selection. The algorithm introduces enhanced bounds and stabilization techniques, contributing to the robustness and accuracy of the overall prediction model. To further elevate the predictive capabilities, an HSC-AttentionNet is introduced. This network architecture combines deep temporal convolution capabilities with LSTM, allowing the model to capture both short-term patterns and long-term dependencies in health data. Rigorous evaluations showcase the remarkable performance of the proposed model. Achieving a 95% accuracy and 94% F1-score in predicting various disorders, the model surpasses traditional methods, signifying a significant advancement in disease prediction accuracy. The implications of this research extend beyond the confines of academia. By harnessing the wealth of information embedded in EHR data, the proposed model presents a paradigm shift in healthcare interventions. The optimized diagnosis and treatment pathways facilitated by this approach hold promise for more accurate and personalized healthcare, potentially revolutionizing patient outcomes
Optimizing Solar Panel Systems using Machine Learning and Ant Colony Optimization
Authors: M. Senthil Kumaran, V. Vaithianathan, Pavithra Guru K. Murugesan,
Published: April 2024

In this study, the optimization of solar panel systems is investigated using machine learning algorithms and Ant Colony optimization. The performance of ML models, such as Artificial Neural Network, Support Vector Machine, Decision Tree, and Random Forest, is assessed relying on precision, recall, F1 score, and accuracy. Furthermore, the ANN model is combined with ACO for additional optimization. The results of the experimentation indicate that the ANN model demonstrates the best performance with the following scores: 97.86% precision, 96.50% recall, 97.20% F1 score, and 98.00% accuracy. In addition, when ACO is applied, the ANN model focuses its capabilities to predict the pressure hitting on the panel as follows: 98.50% precision, 97.80% recall, 98.15% F1 score, and 98.80%. The given outcomes also reflect the application of confusion matrices to show the classification of each model, and the results indicate that the most effective way to predict the parameters of the solar panel and optimize the system is presented by the combination of ANN and ACO. In such a way, the importance of using machine learning approaches and optimization techniques to optimize the energy generation efficiency in renewable energy systems is indicated. In the future, extensive research should be performed to assess experiments on large-scale solar panel systems using the proposed approaches and algorithms to scale-up sustainability efforts and minimize the environmental impact.
CNN based Smart Activity Recognition for Elderly and Physically Disabled
Authors: P.Hemavathy
Published: April 2024

Knowledge and expertise are essential components of a fulfilling life, contributing significantly to well-being. However, there is a marginalized group of individuals who face challenges in achieving a peaceful and inspired existence. These individuals, particularly those with physical disabilities such as blindness and deafness, encounter significant difficulties when navigating the world on their own. Each problem they encounter requires careful consideration and tailored solutions.The concept of the clever assistive expedient aims to alleviate the struggles faced by physically impaired individuals, providing them with a sense of confidence and independence. The primary goal is to empower them to recognize and navigate their surroundings. The planned expedient, SARD (Sensory Augmented Reality Device), achieves this by capturing images and translating them into auditory information. Additionally, it identifies complications and provides comprehensive counsel for individuals facing challenges. In essence, the SARD is designed to be a support system for the physically impaired, utilizing technology to enhance their understanding of the world and addressing obstacles they may encounter. Through these advancements, the aim is to make their lives more manageable and foster a sense of self-assurance and independence.
Optimizing Data Centre Energy Efficiency with Dynamic Resource Allocation and Intelligent Cooling Management through Machine Learning
Authors: Niranchana Radhakrishnan
Published: April 2024

Our research aims to improve energy efficiency in data centres by combining cloud computing infrastructure with machine learning techniques. We propose that dynamic resource assignment, combined with intelligent optimisation of cooling systems, can reduce power waste and operational costs. Real-time sensor data from various data centre components such as servers, cooling systems, and power distribution units is collected and fed into machine learning models for analysis. In this way, we can create power arrangements that are tailored to the resources and needs of various applications. The experimental results show that energy consumption has been reduced by an average of 30% compared to traditional methods. Furthermore, our machine learning models are quite accurate in predicting cooling system performance. For example, an Artificial Neural Network (ANN) has an accuracy rate of 98.78%. This result demonstrates the efficacy of our approach in promoting energy efficiency and operational performance in data centres: it not only provides a scalable, cost-effective solution to industry energy efficiency challenges, but it also improves day-to-day data centre operations by reducing electrical consumption. Our approach, which is based on dynamic allocation of computational resources and real-time data analysis for optimising cooling systems, not only saves energy but also improves operational efficiency. With energy in mind, we must all work towards a more sustainable and green approach to data centre management. Future research can look into other potential optimisations, as well as issues with scalability and application in real-world data centre environments.
Forecasting Model of Sustainable Financial Market Trend Based on Neural Network Algorithm
Authors: Guili Huo, Ying Lou Li Han,
Published: April 2024

Recent advances in computer technology have resulted in the ongoing gathering of massive amounts of data and information. Because the financial market creates so much real-time data, including transaction records, we have a great potential to get important insights from analysing that data, especially in the banking industry. As a result, the goal of this work is to use the financial data that is now accessible to create a unique stock market prediction model. We employ the deep learning approach because of its exceptional capacity to learn from large datasets. This study proposes a hybrid approach that integrates the Archimedes optimisation algorithm (AOA) with a long short-term memory (LSTM) network. So far, heuristic-based trial and error has been extensively used to estimate the temporal window size and architectural components of long short-term memory networks. This paper investigates the temporal properties of financial market data by providing a systematic way to selecting the topology and time window size for the LSTM network. The experimental results demonstrate that the hybrid LSTM network and AOA model outperform the benchmark model.
Construction and Optimization of Macroeconomic Data Forecasting Model Based on Machine Learning
Authors: Chenfan Duan Bin Li,
Published: April 2024

Human capital was first viewed as a production component in macroeconomic development, but endogenous growth theories eventually replaced this perspective. The majority of earlier research used econometric models to investigate the GDP forecasting. Since machine learning models can efficiently resolve nonlinear interactions, this study offers a new perspective by examining the linkages using machine learning approaches. The prediction model for economic development was created for this reason using the best machine learning techniques, specifically the Support Vector Machine. In order to improve SVM prediction, the hyper parameters were optimised using the Bayes approach and several kernel functions. Three statistical metrics—the coefficient of determination, mean absolute error, and root mean square error are used to assess the models' effectiveness.
Computer-Aided Technology in Product Communication and Promotion Strategy
Authors: Hongyu Wei, Xiaotong Qin, Liangyu Chen Huijiao Liang,
Published: April 2024

In today's competitive marketplace, effective in-product communication and promotion strategies are essential for capturing consumer attention and driving product adoption. These strategies involve utilizing various channels within the product itself to convey key messages, showcase features, and encourage user engagement. Computer-aided promotion strategies leverage digital tools and technologies to enhance marketing efforts and reach target audiences more effectively. By utilizing data analytics, automation, and digital advertising platforms, businesses can optimize promotional campaigns, personalize messaging, and target specific demographic segments with precision. This paper proposes an innovative approach to product communication and promotion strategy utilizing computer-aided technology, specifically tailored for the Weibo platform, named the Weibo Stacked Communication Classification Strategy (WeSCCS). The WeSCCS framework integrates advanced computational techniques with Weibo's communication dynamics to classify and categorize promotional content effectively. With machine learning algorithms and natural language processing, the system analyzes user engagement patterns, content preferences, and social interactions to identify optimal communication strategies. WeSCCS classifies user interactions with promotional content such as "positive engagement" (70% of interactions), "neutral engagement" (20%), and "negative engagement" (10%). Additionally, automated content generation and sentiment analysis enable marketers to generate relevant promotional content with a sentiment score of +0.8, indicating high positivity. Real-time monitoring facilitates dynamic adjustments to promotional strategies based on user feedback, ensuring responsiveness to evolving consumer preferences. By integrating computer-aided technology, WeSCCS empowers marketers to maximize promotional impact, increase audience engagement, and drive conversions effectively on Weibo and similar social media platforms.
An Innovative Model of English Teaching Based on Particle Swarm Algorithm Construction
Authors: Yang Yingying
Published: April 2024

English teaching in construction plays a vital role in facilitating effective communication and collaboration within the industry. By equipping construction professionals with proficiency in English, they can navigate global markets, engage with multinational clients, and access a wealth of resources and knowledge available in the English language. English instruction tailored to the construction sector emphasizes industry-specific terminology, communication skills for project management, and comprehension of technical documents and specifications. Additionally, English language proficiency enables construction workers to comply with safety protocols, understand complex engineering plans, and participate in international conferences and training programs. This paper introduces an innovative model of English teaching tailored for construction professionals, utilizing a Particle Swarm Algorithm Construction with Weighted Particle Swarm Classification (W-PSO). Acknowledging the significance of effective communication in the construction industry, especially in multinational contexts, this research endeavors to optimize English language instruction through advanced computational techniques. The proposed model combines traditional teaching methodologies with the W-PSO algorithm, a metaheuristic optimization technique inspired by the behavior of particles in a swarm. W-PSO dynamically adjusts the weightings assigned to various instructional components, such as vocabulary acquisition, grammar instruction, and communication skills development, to optimize learning outcomes for construction professionals. Simulation results a cohort of 100 construction professionals participating in the English teaching program, the average improvement in English language proficiency is measured at 25% after completing the W-PSO-enhanced curriculum. Furthermore, specific language skills exhibit notable enhancements, with vocabulary acquisition increasing by 30%, grammar comprehension by 20%, and communication skills by 35%. The W-PSO algorithm dynamically adjusts the weightings assigned to various instructional components, optimizing the learning process. For instance, vocabulary acquisition receives a weighting of 0.4, grammar comprehension 0.3, and communication skills 0.3, resulting in a balanced and comprehensive approach to English language instruction.
E-Balikas: English to Ilocano Language Mobile Translator Application
Authors: Thelma D. Palaoag Clarence S. Ordonia,
Published: April 2024

The tourism sector relies primarily on communication. Both industry practitioners and tourists must be able to communicate using a medium that both parties can understand. The purpose of this study is to create a mobile application that provides an English to Ilocano language guide for tourists visiting Ilocos Norte. The e-Balikas mobile application delivers interactive Ilocano translations for popular English words such as essential expressions, directions and places, and emergency and safety needs. The descriptive-developmental approach was used in the study, which also embraced the concepts and practices of SCRUM as a framework for building the application. Usability testing was also carried out utilizing the USE tool to assess the application's usability and acceptability among identified tourists to Ilocos Norte via random sampling. The application enables responders to search and translate frequently used terms and phrases related to essential expressions, directions and places, and emergency and safety needs. Additional features include a visual of the word or phrase, along with the pronunciation, and audio playing to allow the user to hear the actual native speaker pronunciation. The respondents agreed that the objectives are met in terms of user friendliness, functionality, and portability. With an average mean of 4.53, it is assumed that the majority of respondents were "Very Satisfied" with the application. The e-Balikas application meets the respondents' needs for English to Ilocano translations and also improves the English to Ilocano language guides, and they were grateful for the benefit of information technology. Furthermore, the application performs as intended, with a high level of dependability and security. It is also adaptable enough to be readily installed and set on a wide range of devices and it adheres to established portability criteria.
Transforming Women Safety with Information Technology: A Mobile Real-Time Intelligence Framework
Authors: Brahmaleen K. Sidhu Priyanka Gupta, Kawaljeet Singh,
Published: April 2024

Despite significant technological advancements, women's safety remains a pressing issue in modern society. Women face threats to their safety in various environments, including while traveling alone, on secluded roads, and in the workplace. Education plays a crucial role in empowering women and raising awareness about safety measures. Despite government and NGO efforts, violence against women continues to rise, with instances of harassment, abuse, eve-teasing, kidnapping, domestic violence, and rape persisting. While women have made strides in various fields, including sports, business, politics, and entertainment, safety concerns remain a significant challenge, especially for those working in the IT and corporate sectors, including night shifts. Sexual harassment in the workplace, including by supervisors or seniors, is a growing concern. Existing safety systems often overlook crucial features such as audio or video recordings, which could aid in identifying perpetrators and ensuring justice for women. To address these limitations, a Real-Time Intelligent System for Women's Safety is proposed, leveraging technologies such as machine learning, artificial intelligence, and data science. This system aims to provide sophisticated yet user-friendly safety features, including live location sharing with authorities and contacts, predicting safe routes, recording evidence, and offering legal rights guidance for women in distress.
Development of a Lean and Green Indicators for Sustainability Assessment in Processing Manufacturing Industries: A Delphi approach
Authors: Nazaruddin Matondang, Udisubakti Ciptomulyono Surya Indrawan, Sukaria Sinulingga,
Published: April 2024

Sustainability has become a concern of academics and practitioners, where implementing sustainability can help reduce environmental impact, and for that, it is necessary to take steps to assess the level of sustainability. So to assess sustainability, it is necessary to discuss the indicators used to measure sustainability, where these indicators really need to be discussed in measuring sustainability manufacture. This paper aims to determine the selection of indicators used for sustainability assessment in the manufacturing industry by discussing lean and green indicators to measure sustainability. Given that lean focuses on operational performance while green focuses on environmental performance, these two dimensions will be examined for appropriate indicators to measure sustainability. The Delphi method is used in this study to select manufacturing sustainability indicators that are appropriate or relevant to the manufacturing industry. The findings of this study found 29 indicators suitable for measuring sustainability in the processing manufacturing industry, consisting of 13 indicators for the economic dimension, 9 for the environmental dimension, and 7 for the social dimension. The indicators obtained can be applied to measure sustainability in the cooking oil processing manufacturing industry.
Machine learning and Sensor-Cloud Based Precision Agriculture for Intelligent Water Management for Enhanced Crop Productivity
Authors: Abhishek Sharma
Published: April 2024

The combination of Machine Learning algorithms with Internet of Things devices is emerging as an effective solution to redefining precision agriculture for better water management and crop cultivation. The purpose of this study is to use different learning models, such as Artificial Neural Networks , Support Vector Machines , Decision Trees , and Random Forest , to predict the irrigation need based on real-world sensor data. To retrieve the output variable, which is the irrigation requirement, data from temperature, soil moisture, water level, and humidity sensors that are available in a tomato cultivation facility are used. The dataset consists of 3422 readings, which are split into training and testing sets. A designated percentage of 70% is used for training the models, while the remaining 30% are used to test the outcomes. As the results of the study show, the ANN model is the most accurate predictor of irrigation need with the classification rate of 97.6%, followed by SVM , DT , and RF with 95.4%, 91.3%, and 88.9% correspondingly. The differences in the outcomes are demonstrated in confusion matrices, which identify the classification of the cases and indicate the percentage of correct predictions. Evidence of the predictive power of ML models implies that farmers can independently determine when to activate the pump when real-world data serve as the input. Additionally, the ability to collect real-world data using IoT sensors is beneficial, as it empowers farmers with up-to-date information to make a timely decision about pump activation. The limitations are associated with the type of crop and agricultural facility and, for this reason, future studies may investigate the generality of the conclusion with regard to other crop types and facilities.
An IoT Intelligent Approach for Safety and Efficiency of Robotic Medicine Delivery in Hospitals
Authors: Senthilkumar Selvaraj
Published: April 2024

In this paper, we introduce an IoT-based robotic medicine delivery system designed to boost safety and efficiency in hospital settings. This IoT-embedded robotic system integrates advanced sensors such as proximity, temperature, pressure, pulse rate, etc., sensors for ensuring real-time health and environmental monitoring behind this system. The robot is connected to a centralised controller, enabling healthcare workers to monitor patients using a cloud-based system. . Our robotic system moves about with the support of both line follower and LiDAR scanning technology for environmental safety and proximity detection. Experimental results show that the robotic system was able to navigate static obstacles and dynamically move away from the mobile entities within its reach without touching them. Besides, the proposed robotic system was able to reach the bed at the right time under normal operating conditions, which shows the efficiency of this approach in real-time settings. Patient feedback suggests that patients are very happy about the system, in terms of its usefulness, trustworthiness, and to their suprise to be taken care of by a robot. Overall, our study shows the potential of the proposed robotic system in streamlining the existing medicine delivery process in hospitals. It can potentially improve various aspects including safety, efficiency, patient compliance, and quality of care delivery. The promising results suggest further studies for incorporating advanced features and for ensuring compliance with regulatory requirements. Additional studies are to be conducted to further improve the proposed sensor-based robotic medicine delivery system, to facilitate its adoption in hospitals.
Evaluating the machine learning based Efficacy of Decision Tree and Support Vector Machines in Smart Irrigation Systems for Precise Irrigation Status Classification for Optimizing Water Management in Agriculture
Authors: Shaik Mabasha
Published: April 2024

The research paper deals with the improvement of water management practices in agriculture through the use of machine learning in smart irrigation systems. The efficiency of the Decision Tree and the Support Vector Machine models with real-time sensor data was analyzed. The Internet of Things system was developed to analyze the data collected at the field with the help of various sensors, including temperature, humidity, and soil moisture sensors. The layout and choice of sensors were based on the official parameters required for most average plants and corps. After data collection and development of classifiers, the Decision Tree and Support Vector Machine models were used and analyzed regarding their efficiency for making irrigation-related decisions. As the result, it was found that both models showed a good performance, but the SVM model was slightly better due to a smaller number of false positives and false negatives, which allow it to divide the data into the corresponding categories more accurately. At the same time, both models use history data, and the more recent data are better for decision as it was also noted during the development when the use of models based on history data for irrigation decision result in regular irrigation, which in long term would cause the loss of productivity of plans, due to the issue that ancient and no crops are watered too. As the final results, it can be stated that the models show good performance, and the algorithms that were tested can be stated viable for the use at practice. At the same time, the SVM model shows slightly better performance than DT.
Machine learning and IoT Based Robotic Solution for Home Wrist Rehabilitation through Motion Capture Enabled Architecture
Authors: Ch. Gangadhar
Published: April 2024

The present study investigates the effectiveness of an integrated approach that combines machine learning, robotic rehabilitation, and traditional methods for wrist rehabilitation among the hemiparesis population. Subjects with robotic rehabilitation at the four-week intervention period showed the ball’s significant difference in the flexion and extension angles and demonstrated the better overall outcomes from the robotic intervention . The machine learning models, in particular, the artificial neural networks, provided the prediction of rehabilitation outcomes and demonstrated the high accuracy, high precision, high recall, and high F1 score. This study supports the idea of the beneficial effects of combining robotic rehabilitation with machine learning algorithms and supports the notion that the two benefits could bring high synergistic effects above their individual potentials. The report also contributes to the development of the rehabilitation science of wrists, given that few studies have explored the applications of artificial intelligence methods or robotic rehabilitation beyond the context of feasibility reports. The findings of the current research could contribute useful insights for the decision-making practices of therapists and healthcare providers, also given the reliance on large data contributed by the previous interventions. Future study could investigate the long-term results, the cost-effectiveness of the approaches, and assist in developing the potential results of customization for individual patients.
Machine learning Approach to Battery Management and Balancing Techniques for Enhancing Electric Vehicle Battery Performance
Authors: Basavaraju Bennehalli
Published: April 2024

The objective of this research is to apply machine learning techniques to optimize electric vehicle battery management and balance to attain maximum battery performance. Here, we will assess and compare the efficiency and accuracy of decision tree classifier, ANN, and Naive Bayes classifiers in: predicting two optimal charging and discharging battery management strategies, prediction of the battery’s performance and detecting any abnormalities in it. . In this context, one machine learning model will be proposed to have the most advantageous performance. The experimental results details that such findings are attained, and the precision rates, recall rates, and F1-scores attained by net models exceed 98%. Additionally, the decision tree, as well as the Naive Bayes classifier, have impressive performance, and their accuracy rates exceed 90%. Decision trees along with Naive Bayes classifiers have also important impacts on the identification of classification of battery’s responses through probabilistic classification in the former. Our findings have important implications for ensuring electric vehicle batteries are managed more properly for optimal performance. Additionally, based on the results obtained, they can be utilized to help relevant stakeholders in the EV industry and other industries with battery usage implement practical measures that optimize battery performance, increase battery life, and reduce the incurred operational expenses of electric vehicle fleets.
Machine learning and IoT based Predictive Maintenance for the industrial motors for sustained Automation in the power plant Industry
Authors: Amar Bharatrao Deshmukh
Published: April 2024

The purpose of this research is to develop novel framework based on advanced tools, including machine learning and the Internet of Things and self-attention mechanisms. Traditional and advanced tools were used in the data-driven predictive maintenance mechanism and served as a basis for comparing the tools. At the power plant, thirty-four datasets were collected to monitor three industrial motors continuously. The tools’ predictive ability was analysed using conceptualized features from the sensory data , and the management strategy remained dependent on the network. The study results show that each tool performs at high-performing levels because each exceeded 75% of the performance metric. The study results indicated that the proposed framework gave a consistent high-performance metric, 86.4%, in all the ten experimental scenarios used . The rate determination was attributed to the choice of the long short-term memory architecture, the self-attention mechanisms, and the optimization techniques. The choice of using mean squared logarithmic error also contributed to the outcomes because these tools yield high-performance scores. In addition to these, the test of the forecasting models chosen also influenced this performance. This study’s findings show that this framework is reliable in predicting pending failure equipment and developing relevant management strategies in industrial motors failure. It was found that the use of advanced tools in the development of the framework was a crucial aspect. Scaled tools and optimization techniques are of crucial importance in predictive management in equipment failure as they assist the frameworks in identifying ideal features from the sensory data. It should also be noted that the choice of a loss-function model is also important in the predictability of framework..
Modeling Nonlinear Physical Systems in a Real-Time Operating System Environment for Control and Cyber Threat Mitigation
Authors: Prarthana A. Deshkar,
Published: April 2024

Nonlinearity control and interconnection between physical systems are essential for many contemporary systems. Cyber Physical systems came about because of the growing number of security concerns with network control systems. Nonlinear physical systems are the primary subject of this study's investigation into control and cyber threat mitigation. Nonlinear physical systems are the focus of this study, which also delves into their control, modelling, and real-time implementation. Here, physical systems are defined as two kinds of benchmark nonlinear robotic systems: the Segway transporter and the cruise control system. Many see the Segway as an unstable and nonlinear physical system. By factoring in kinetic and potential energy, Lagrangian dynamics has allowed for the derivation of equations of motion. Next, a state-space model of the system is obtained by linearizing the nonlinear differential equations using Taylor series expansion of functions. This model is then transformed into two transfer functions, one for the tilt angle and one for the yaw angle.  The Model Predictive Controller (MPC), GA Tuned PID, and PID are the building blocks of MATLAB's effective simulation study of nonlinear control.
Empathetic Algorithms: The Role of AI in Understanding and Enhancing Human Emotional Intelligence
Authors: Sesha Bhargavi Velagaleti
Published: April 2024

In an era where artificial intelligence (AI) seamlessly integrates into the fabric of daily life, understanding and enhancing human emotional intelligence (EI) through empathetic algorithms emerges as a frontier in technological advancement. This research paper explores the development and application of AI systems capable of recognizing, interpreting, and responding to human emotions in a manner that fosters emotional growth and understanding. Through a comprehensive literature review, this study identifies the theoretical underpinnings of emotional intelligence and examines the current landscape of empathetic algorithms. Employing a mixed-methods approach, including case studies and empirical analysis, the paper presents novel insights into how AI can be engineered to support emotional intelligence across various domains, such as healthcare, education, and customer service. Ethical considerations, including privacy, consent, and data security, are thoroughly evaluated to address potential societal implications. The findings suggest that empathetic algorithms hold significant promise in enhancing human emotional intelligence, albeit with challenges that necessitate careful ethical and technical scrutiny. The research culminates in proposing a set of guidelines for future developments in this field, emphasizing the need for interdisciplinary collaboration. This study not only contributes to the theoretical framework of empathetic algorithms but also paves the way for future innovations that prioritize emotional intelligence in the design and implementation of AI systems.
Brain Tumour Detection and Multi‐Classification Using Advanced Deep Learning Techniques
Authors: Shrikant Telang, Mandakini Ingle, Jayesh Surana,  Padma S. Gajendra Singh Rajput, Kailash Kumar Baraskar,
Published: April 2024

The early detection and accurate classification of brain tumors are pivotal in enhancing treatment efficacy and patient survival rates. Traditional diagnostic methods, while effective to a degree, are often invasive and reliant on subjective interpretations. This paper introduces a novel approach using advanced deep learning techniques to automate the detection and multi-classification of brain tumors from medical imaging data. Leveraging a comprehensive dataset, we employed state-of-the-art convolutional neural networks (CNNs), incorporating innovative mechanisms such as transfer learning and attention models to refine the accuracy and interpretability of tumor identification and classification. Our methodology encompasses rigorous preprocessing, data augmentation, and a multi-faceted evaluation framework to assess model performance comprehensively. The results indicate a significant improvement over conventional methods and existing machine learning models, showcasing high precision, recall, and F1 scores across multiple tumor types. This research not only contributes to the body of knowledge in medical image analysis but also presents practical implications for integrating advanced AI technologies into clinical diagnostics, thereby potentially transforming patient outcomes through earlier and more accurate diagnoses. The discussion extends to the challenges faced, including dataset imbalances and model deployment in healthcare settings, and proposes directions for future research to further enhance model effectiveness and applicability.
FedCGS: Leverage Feature Compression and Conditional GAN for Privacy Protection and Efficient Communication in Federated Learning
Authors: Sun Haoxuan
Published: April 2024

Federated learning is a distributed machine learning technique that can train models from decentralized data, collaboratively building central models and protecting data privacy by passing partial model updates rather than raw data in each iteration of model learning. However, recent work has shown that the information during the exchange between the server and the client is still vulnerable to gradient-based privacy attacks, and traditional privacy protection methods can incur significant costs (such as communication consumption and performance evaluation). Therefore, we need an algorithm that can protect data privacy while reducing communication costs and performance losses. Here, we propose a federated learning framework FedCGS that leverages conditional generation adversarial networks and feature compression in order to achieve privacy protection while maintaining competitive model performance and effectively improving communication efficiency. We divided the local network of each client into feature extractor and public classifier, and kept the extractor local to protect privacy, and sent  classifier and client generator to the server for aggregation and update through singular value decomposition, so as to improve the performance of the local network and efficient communication. A lot of experimental data prove that FedCGS can improve the performance of local model better than traditional federation learning, and improve the communication efficiency better than common conditional generation adversarial network. Therefore, we believe that FedCGS has better federal learning potential