bluuebunny/crossref_metadata_2025 huggingface dataset:
Hubungan Dukungan Keluarga Dengan Kepatuhan Minum Obat Pasien DM di Poli Penyakit Dalam RSUD. R. Syamsudin, S.H. Kota Sukabumi
Authors: Anggraeni Reni
Published: December 2024

Diabetes mellitus (DM) merupakan penyakit kronis yang memerlukan pengobatan dalam jangka panjang. Dukungan keluarga adalah bantuan yang diberikan anggota keluarga yang meliputi dukungan penilaian, instrumental, informasional dan emosional. Kepatuhan minum adalah perilaku pasien sesuai dengan ketentuan yang diberikan oleh profesional kesehatan. Tujuan penelitian untuk mengetahui hubungan dukungan keluarga dengan kepatuhan minum obat pasien DM. Jenis penelitian ini adalah korelasional dengan pendekatan Cross sectional. Populasi berjumlah 134 orang dan sampel sebanyak 100 orang dengan menggunakan Accidental sampling. Analisis data menggunakan uji Chi-square. Hasil penelitian ini menunjukkan terdapat hubungan antara dukungan keluarga dengan kepatuhan minum obat pasien DM di Poli Penyakit Dalam RSUD R Syamsuddin, SH. Kota Sukabumi dengan p-value = 0,000. Diharapkan pihak Rumah Sakit dapat meningkatkan promosi kesehatan dan konseling, serta pendidikan kesehatan khususnya mengenai dukungan keluarga serta kepatuhan minum obat bagi penderita penyakit diabetes melitus.
Institucionalizar la asistencia. Notas para pensar la necesidad de su disputa.
Authors: Arias Ana Josefina , Hiquis Florencia , Sierra Noelia
Published: December 2024

Resumen:El artículo describe un conjunto de fundamentos a través de los cuales proponer al debate público una ley nacional de asistencia social. En primer lugar, aborda la importancia de lo asistencial como campo de construcción de formas de protección dentro de la sociedad en el actual contexto; a continuación, identifica la necesidad de institucionalización que da sentido a la idea de construir un sistema. Finalmente, se presenta un esbozo de sistema, de distribución de tareas y competencias federales con relación a las distintas instituciones. Parte de estas ideas han sido discutidas con la Red Argentina de investigadores de Asistencia Social (RAIAS) y con las distintas unidades académicas y consejos y colegios profesionales que organizaron los dos foros para la discusión de la ley que se llevaron adelante en distintos puntos del país durante el año 2024.Palabras clave: Asistencia, Sistemas públicos, Derechos Summary:The article describes a set of foundations through which to propose a national social assistance law to public debate. Firstly, it addresses the importance of assistance as a field for constructing forms of protection within society in the current context; Next, he identifies the need for institutionalization that gives meaning to the idea of building a system. Finally, an outline of the system is presented, of distribution of federal tasks and powers in relation to the different institutions. Part of these ideas have been discussed with the Argentine Network of Social Assistance Researchers (RAIAS) and with the different academic units and professional councils and associations that organized the two forums for the discussion of the law that were carried out in different parts of the country. during the year 2024.Key words: Assistance, public systems, Rights.
Promotoras de salud: aportes en la atención primaria de la salud en el Barrio 31.
Authors: Elizalde Inés , Luna María Gabriela , Rostkier Salomé
Published: December 2024

Resumen:El presente artículo es resultado de la investigación transversal anual que realiza la Residencia de Trabajo Social del Hospital General de Agudos “Dr. J.A. Fernández” (CABA) durante el período 2023- 2024. La misma tiene por finalidad aportar conocimientos y reflexiones en torno al rol de las promotoras de salud que integran los Centros de Salud y Acción Comunitaria del Barrio 31 o Barrio Padre Mugica. Es una propuesta que pone de manifiesto la importancia de las promotoras de salud en el Barrio 31 como trabajadoras clave en la prevención y promoción de la salud, así como en la construcción de accesibilidad de los vecinos al sistema de salud.Palabras clave: Accesibilidad - Territorialidad - Cuidados comunitarios. SummaryThis article is the result of the annual cross-sectional research carried out by the Social Work Residency of the General Acute Hospital “Dr. J.A. Fernández” (CABA) during the period 2023-2024. Its purpose is to provide knowledge and reflections on the role of the health promoters who make up the Health and Community Action Centers of Barrio 31 or Barrio Padre Mugica. It is a proposal that highlights the importance of health promoters in Barrio 31 as key workers in the prevention and promotion of health, as well as in the construction of accessibility in the neighborhood.Key words: Accessibility, Territoriality, Community care.
Políticas públicas, comunicación y alianzas populares. Homenaje a Glenn Postolski
Authors: Pajoni Hernán
Published: December 2024

Este número en homenaje a Glenn Postolski es a la vez un homenaje a sus preocupaciones político-académicas. Las políticas públicas de comunicación son las herramientas con las que pensó modalidades de intervención efectivas para hacer más equitativa la participación en un sistema de comunicación de masas desigual, dependiente y concentrado. ¿Qué hay de la palabra no dicha, que es mucho más vasta y diversa que la que reina? ¿Qué hay de las voces mayoritarias nunca representadas en un sistema que las desplaza con deliberación? Deberá ser el Estado el garante de esa igualdad; dependerá de la fuerza de sus intervenciones en representación de lo popular, de lo diverso, de lo plural. Así lo presento, así lo recordamos, en el universo de esas preocupaciones.
Effect of Powdered Lemon Grass (Cymbopogon citratus) as Feed Supplement on the Growth Performance of Native Chicken
Authors: R Javier Maria Juliet , P Oraye Venus Mae
Published: December 2024

This study was conducted to determine the growth performance of the native chicken using different levels of powdered lemon grass as feed supplement in the diet. The study utilized 128 unsexed native chickens were randomly distributed into 4 experimental treatments with 4 replications and 8 birds per treatment using Randomized Complete Block Design (RCBD). The different levels of powdered lemon grass in treatments were T1 (Control) – 0%, T2 –1%, T3 –1.5%, and T4 - 2.5% powdered lemon grass, respectively. The results revealed that there is no significant difference in the body weight gain and feed conversion ratio of the native chicken among the experimental animals. In the feed consumption, there is a highly significant difference among the treatment mean during week 2 and 8, and significant difference on week 4, 11 and 12. These findings suggest that the inclusion of powdered lemongrass influenced the feed consumption of the chickens, particularly in certain periods of the study. This implies that while powdered lemongrass, particularly at 1%, appeared to enhance feed consumption during specific weeks. In terms of the average daily gain, results revealed a significant difference only in week 14. The result suggests that while the inclusion of powdered lemongrass in the diet did not significantly affect ADG during the early and middle stages of growth, it exhibited a notable positive effect in the later stages. This indicate a cumulative or delayed impact of lemongrass supplementation on growth performance, which becomes evident as the birds approach maturity. Based on the findings stated above, it can be concluded that the body weight, feed consumption, FCR and ADG of the experimental birds were affected by the different inclusion levels of powdered lemon grass. Inclusion of 1% lemon grass powder in feedstuff is recommended as it enhanced the overall growth performance of native chickens.
Implementasi Penerapan Budaya Kaizen dalam Meningkatkan Mutu Pendidikan di Sekolah
Authors: Rani Irmayanti
Published: December 2024

When we want change in our lives, we often expect immediate and drastic changes. We hope that this change will soon have a significant impact on our lives. But usually, we focus too much on results, don't commit to long-term changes, and the results are not optimal. Apart from building motivation and strong intentions, you must also develop habits and continuous improvement. Things like this are known based on the Japanese cultural principle of "Kaizen". Even the smallest changes in education can significantly impact improving the quality of education in schools. To maintain the quality of education in schools, there needs to be a significant increase in the level of education, through utilizing all instructional strategies and procedures used by teachers, educators, and students. The 5S concept (Seiri, Seiso, Seiton, Seiketsu, and Shitsuke), the PDCA concept, the 5W1H concept, and the 3M concept (Muda, Mura, and Muri) are some of the concepts that the Kaizen concept can justify to improve the quality of education. The process of collecting data through library research involves reading books, papers, journals, and other references to understand and analyze the theories presented in many literary works relevant to the topic. According to research findings, implementing kaizen culture in schools can have a positive effect on the field of education. In terms of efficiency or HR involvement as well as costs and time in implementation, this makes it possible to prevent waste and minimize failures in these activities because the implementation of educational activities through kaizen culture will be fully controlled and protected from sudden educational changes. Both in terms of curriculum, class management, extracurriculars, skills development, routine school activities, and providing learning.
‘Bag of Words’ to ‘Bag of Concepts’: Improving Text Categorization using SVM
Authors: Rahul V. Mohare  , Satyajit S. Uparkar  , Pravin Y. Karmore  , Vishnu Vardhan Budati
Published: December 2024

This research paper tries to brief about existing research trends in improving text categorization, few research work of vital importance are implemented in recent times, and discovering some major open issues about text categorization using the famous Support Vector Machine (SVM) Algorithm. The design approach renders to an experimental study consisting an exploration of concepts, definitions and scoping of previous literature along with analyzing comparing the text categorization using Bag of Words (BoW) and Bag of Concepts (BoC) approaches. The major findings include, the effectiveness borne by the applied research techniques for categorizing text using SVM and comparing the results with respect to BoW and BoC approach. This will further direct the researchers to identify and proceed their research work in right direction bridging the research gap. In addition, this research work will also guide researchers to go beyond ‘Bag of Words’ and start using/Developing the tools that emphasize on ‘Bag of Concepts’.
Enhancing the Failure Mode and Effects Analysis Risk Prioritization Model Using Conservative Method Based on Enterprise Resource Planning Implementation
Authors: Sunita N. Dhote  , Chandan R. Vichoray  , Pravin Y. Karmore  , Satyajit S. Uparkar
Published: December 2024

Enterprise resource planning (ERP) implementation has been one of the problems of organizations recently, and there have been several obstacles to establish ERP effectively. Companies can decrease the effect of failure by recognizing their strength and weakness. Failure Mode and Effects Analysis (FMEA) is an organized way to determine and address possible issues or failures and their effect on the process or system before an adversarial event occurs. The risk priority number (RPN) index is utilized for prioritizing failures, particularly for its ease and subjective assessments of severity, occurrence, and the detectability of every failure. Hence, this paper suggests Fuzzy assisted weighted risk priority number (F-WRPN) analysis for FMEA. This model can efficiently realize dynamic, real-time, and long-term assessment of RPN under the situation of continuous knowledge accumulations. The significant contribution of the suggested method is to utilize the random ambiguity and fuzzy ambiguity in an incorporated method to resolve the complex combined model. Compared with the weight factor based on fuzzy set theories, the ambiguity measure-based weight factors for vagueness measure of the particular evaluation guarantees the inner management of the suggested technique. The findings illustrate that the suggested approach can overcome many inadequacies of conventional FMEA and efficiently help decision-makers and research and development (R&D) departments improve product reliability. The experimental results show that the suggested F-WRPN model enhances the failure classification ratio of 95.6%, an efficiency ratio of 96.3%, detection ratio of 96.2%, risk prediction ratio of 97.9%, accuracy ratio of 98.2%, and prioritizing critical failure ratio of 98.7% compared to other popular methods.
Exploring Sustainable Synthesis Methods for Nanomaterials and their Integration into Green Supply Chain Practices within Small and Medium-Sized Enterprises (SMES)
Authors: Vishakha Ashish Mankar  , Athar Javed Ali  , Chandan Vichoray  , Anant Deogaonkar  , Abhijit Rameshwar Gajghate  , Deepa Choudhari
Published: December 2024

In the context of growing environmental concerns and the need for sustainable development, small and medium-sized enterprises (SMEs) face unique challenges in adopting green supply chain practices. This research explores sustainable synthesis methods for nanomaterials and their integration into the supply chain of SMEs. The study aims to develop a mathematical model that optimizes the adoption of these sustainable practices, considering economic, environmental, and operational factors. By employing eco-friendly synthesis techniques, such as green chemistry principles and renewable energy sources, the research investigates the lifecycle impacts of nanomaterials. The proposed mathematical model will incorporate multi-criteria decision-making to evaluate the trade-offs between cost, environmental impact, and technological feasibility. The model will also consider the specific constraints and opportunities faced by SMEs, such as limited resources and scalability. The outcome is expected to provide a framework for SMEs to make informed decisions regarding the sustainable production and integration of nanomaterials, thereby contributing to a greener supply chain and promoting overall sustainability in the industry. This research has the potential to guide policymakers and industry stakeholders in developing guidelines and strategies for sustainable nanomaterial production and utilization in SMEs.
Approximation of function in the Generalized Hölder Class using (TC^δ) means for Fourier Series and Conjugate Fourier Series
Authors: Pankaj Tiwari  , Dr. Aradhana Dutt Jauhari
Published: December 2024

In the current work, we explore function of error estimation in the generalized Hölder class  and  classes using  means of F. S. as well as C. F. S. A result regarding the generalized Hölder class degree of approximation has been established.
Developmental Sequence in the Comprehension Method of Deep Learning for Classifications of Human Emotions
Authors: Barkha Sain  , Dr. Kalpana  , Dr Roushan Kumar  , Ashutosh Jaiswal
Published: December 2024

Human emotion recognition is an important step that is used in augmented and virtual reality, advanced driver assistance systems, human-computer interaction, and security systems, among other things. People's speech, language, and facial expressions are all ways that humans can figure out how other people are feeling. Emotion recognition is the process of using technologies based on artificial intelligence to figure out how people feel about products, services, or goods without saying anything. While deep learning research is one of the fields that is rapidly growing and evolving today. Humans comprehend emotions differently than machines do. The capacity to remember one's feelings can open up a universe of potential outcomes and applications, going from more amicable human-PC connections to better designated publicizing efforts, lastly, worked on human correspondence, by raising every one of our ability to understand people on a deeper level. This makes understanding human emotions an important area of research. The objective is to go over every specific aspect of the dataset, the features that were created, data processing, face detection, CNN architecture, and transfer learning techniques. To determine the user's thoughts, preprocessed Reddit comments are trained using a Deep Neural Network (DNN). The inference engine module, a hybrid convolutional-recurrent neural network, is also coupled. The model delivers a precise response.
Assessing the Influence of Shading Patterns on Solar PV Panel Efficiency
Authors: Lokesh Varshney  , Raju Hajare  , Dinesh Singh
Published: December 2024

Solar energy is increasingly recognized as a key source of renewable energy, driving ongoing research into optimizing its efficiency and minimizing power generation losses. This study investigates the performance of multiple solar PV panel configurations, examining various strategies that prioritize efficiency, power output, and loss reduction. Among the configurations evaluated are Total Cross Tied (T-C-T), Series Parallel (S-P), Physical Relocation of Modules with Fixed Electrical Connections (PRM-FEC), and a novel Sudoku-inspired arrangement. The analysis is conducted using MATLAB/SIMULINK to provide a detailed examination of each configuration's impact on parameters such as output power, mismatch losses (ML), fill factor (FF), and overall efficiency. Simulation techniques are employed to explore functionality and effectiveness of these configurations. The findings of this research aim to advance the efficiency of solar power systems, aligning with the global shift toward sustainable energy sources. Additionally, the paper introduces an innovative approach to enhancing photovoltaic (PV) system efficiency through a unique Sudoku-based shading configuration. By arranging the PV panels in a pattern inspired by Sudoku, this method seeks to reduce shading effects and boost system performance.
An Automated Approach to Finding the Best Prices on Gem and Private E-Commerce Sites
Authors: Kuldeep Vayadande  , Yogesh Bodhe  , Amol Bhosle  , Preeti Desai  , Gayatri Dere  , Snehal Elkiwar
Published: December 2024

In the current scenario, it is difficult to determine whether the price for any product displayed on a website is the best possible price, and surfing different sites for the same product to get a reasonable price is a tedious job. To overcome this problem, a web-based system is proposed. This paper delves into a fully responsive website where the prices of various products on GeM, an Indian Government e-commerce website, are compared with prices of the same products on private e-commerce websites. This lets customers to get products at the most affordable prices and gives new dealers a fair chance at trade. The website also features an alert system where users can set a threshold price or desired price for the product, and if the product's price drops below this threshold, users are immediately notified. For fetching the price-related data from these websites, the proposed system implements various tools, APIs, and web scraping techniques. After successful fetching of the data, it is stored in database systems such as MySQL, and price comparison is done using collaborative filtering techniques (user-based, content-based) based on matching search results using cosine similarities and weighting methods. Data protection protocols are followed throughout the data collection and storage process, resulting in 95% accuracy for the proposed system.
Detecting Malware on Android Devices Using CNN-LSTM with FDSWM based Feature Selection
Authors: S.V. Vasantha  , B. Kiranmai  , B. Jyoshna  , S. Ramakrishna  , B. Suvarnamukhi  , S. Hariharan
Published: December 2024

The widespread use of Android devices has made them attractive targets for cyberattacks. Traditional methods of detecting malware often struggle to keep up with evolving threats. In this research, a thorough analysis is undertaken to explore the potential contributions of deep learning and machine learning towards improving the detection of Android malware. Deep learning approaches based on GRU, LSTM, and CNN-LSTM networks are evaluated and compared with three machine learning algorithms: KNN, DT, and SVC. Using the CICAndMal2023 dataset, the effectiveness of these methods in distinguishing between malicious and benign Android apps is analyzed. The findings suggest that both deep learning and machine learning hold promise for improving Android malware detection. Specifically, the CNN-LSTM deep learning method demonstrated the highest accuracy of 99.7%. This indicates that deep learning techniques can provide a more reliable means of shielding Android users from evolving malware threats.
Intelligent Disease Prediction and Personalized Health Management System
Authors: Shanthi Makka  , Ramesh Babu Popuri  , Rajendar Sandiri  , Muni Sekhar Velpuru  , Gagandeep Arora
Published: December 2024

An increasing number of effective and precise illness prediction techniques are required due to the volume of healthcare data that is being collected. The application of machine learning techniques to the creation of an intelligent disease prediction system is examined in this research. The suggested model makes use of sophisticated algorithms to examine various patient data sets, such as those pertaining to lifestyle, medical history, and demographics. In order to create a prediction model that can identify possible health hazards, the paper focuses on integrating ensemble learning with machine learning techniques such as decision trees, random forest and etc. The model is trained and evaluated on a large dataset that is representative of a range of diseases and the factors that influence them. The findings show how machine learning may be used for risk assessment and early disease identification, giving healthcare professionals a useful tool for wise decision-making and efficient resource allocation. By integrating intelligent illness prediction systems, the research findings support ongoing efforts to improve healthcare outcomes, lower treatment costs, and increase overall patient well-being.
Exploring the Interplay of Leadership, Work Environment, and Teacher Performance: Insights from Madrasah Aliyah in Soppeng Regency
Authors: Nurahmi  , Abdul Rahman MUS, Baharuddin Semmaila  , Ahmad Hakim  , Reza Aril Ahri
Published: December 2024

The current era of globalization is characterized by very sharp and tight competition, where every organization tries and competes in competition to achieve the best, by organizing all existing systems so that they can play their proper role. Research Objectives: 1. to analyze the influence of leadership and Work Environment on the job satisfaction of Madrasah Aliyah teachers in Soppeng district 2. To analyze the influence of leadership and environment on the work performance of Madrasah Aliyah teachers in Soppeng district 3. To analyze the influence of job satisfaction on the work performance of Madrasah Aliyah teachers in Soppeng district. Quantitative research approach. The population was 211 madrasa teachers and the sample was 138 madrasa teachers. The data collection method uses a questionnaire. The sample collection method is by percentage sampling. The analysis method uses Multiple Linear Regression. The results of the research show that: (1) leadership and the work environment have a joint effect on the job satisfaction of Madrasah Aliyah teachers in Soppeng district 2. Leadership and the work environment have a joint effect on the performance of Madrasah Aliyah teachers in Soppeng district 3. Partially, leadership has no significant effect on job satisfaction of Madrasah Aliyah teachers in Soppeng district 4. Partially leadership has no significant effect on the work performance of Madrasah Aliyah teachers in Soppeng district 5. Job satisfaction has an effect on the performance of Madrasah Aliyah teachers in Soppeng district.
16s rRNA Sequence Analysis and Identification of nif Genes in a New Strain of Rhodopseudomonas Sps, As A Potential Candidate for Increasing Efficiency of a Biogas Production
Authors: Aysha Sherieff  , M.Mohibbe Azam  , K.Sesha Maheswaramma
Published: December 2024

A new strain of Rhodopseudomonas sps with partial 16s rRNA sequence was performed previously using sequencing methods and further analysed using Bioinformatics tools which is being reported in the present paper. GC profile content of the sequence, phylogenetic tree analysis and functional analysis of 16s rRNA sequence was performed using various tools like RDP classifier, BLAST, FASTA, Expasy-Translate, ORF Finder, EMBOSS Seqret, CLUSTALW, Addgene, InterPro scan and CLUSTAL Omega. The insilico studies have revealed its close relation to Rhodopseudomonas faecalis and also supporting evidences that the strain has genes for photosynthetic activity and consists of nif genes for Nitrogenase activity. Several researchers reported the presence of nitrogen rich biomass as raw material and have yielded high methane production. This shows the presence of nif genes in Rhodopseudomonas faecalis increases the efficiency of biogas production and the insilico research work proposes to utilise the bacterium as a potential candidate.
Six Bottom-up Visual Modulating Areas are Valued by Observers after Having Viewed the Dalinian Image ‘The Invisible Man’: A Study Based on Ocular Fixations Analyses
Authors: Guillermo Rodríguez-Martínez
Published: December 2024

Salvador Dalí painted several paintings in which there were ambiguous images. Also known as bistable images, paintings like ‘The invisible man’ have the characteristic of having two or more possible perceptions. That is to say that different visual percepts can be perceived while observing this image. In order to observe if six different constituent parts of the ‘invisible man’ have the same level of salience, 69 participants took part in an experiment in which they observed the mentioned dalinian bistable image in front of an eye-tracker device (60Hz.). Eye-movements recordings were analyzed to stablish the durations of ocular fixations made on the six areas that were chosen following the criterium of having the capability of operate as bottom-up perceptual modulating factors. The results show that ocular fixations have a different duration when comparing gazes made on each area. Eyes and hair parts were the more observed ones. It was concluded that there are areas that are more salient when looking at the bistable image in question. This fact can vindicate that constituent aspects of visual stimulus have an influence on the perception of the image itself.
A Empirical Study of Oil Palm Productivity in India using Time Series
Authors: B R Sreedhar  , Narsimlu. G  , Swatmaram  , M. Ganeshwar Rao  , M. Ramesh
Published: December 2024

This is an overview of Oil Palm production and manufacturing in India. The facts from 1966-67 to 2022-23 have been analyzed the usage of time sequence method. Versions from 1966-67 to 2021-22 are used for generation and forecasting. Figures from 2019-20 to 2020-21 are used for version verification. Autocorrelation function (ACF) and partial autocorrelation characteristic (PACF) had been calculated for statistics. Fixed Patty Jenkins' Correct Autoregressive Integrated Moving Average. Version verification the usage of known statistical methods. The universal performance of the version is validated by an assessment criterion that includes the percentage deviation of the fee and the mean percentage absolute error (MAPE) of the prediction. To forecast the location, we forecast the key years the usage of autoregressive joint shifting common (0,1,1) and autoregressive joint transferring common (0,1,1), respectively. The effects also exhibit that the 12-month area forecast for 2022 is 8.3219 million hectares in the decrease bound state, 10.3718 million hectares in the top sure state, and the manufacturing forecast is about 6.4445 million hectares in the decrease certain state. Hectares, if the upper restriction is 8.6487 million hectares. Slope fashions are examined exponentially or linearly. The ultimate outcomes proven that integrated linear rebar loading is a fantastic and volumetric method for field and manufacturing applications.
A Three-Level Agent-Based Framework For Resource Allocation Optimization In Flexible Manufacturing Systems
Authors: Atef Gharbi  , Sami Aziz Alshammari  , Mohamed Ayari  , Yamen El Touati  , Akil Elkamel  , Mahmoud Salaheldin Elsayed  , Zeineb Klai
Published: December 2024

Agent-based approach has become a prominent paradigm for analysis, design and implementation of modern industrial systems. The paper focuses on the development of practical applications to optimize resource allocation in flexible manufacturing systems using auto-guided vehicles (AGVs). The system uses Agent Unified Model Language (AUML) to design models for various agents such as machines, vehicles, and products. In the first level, we define the role, capacity, and function of the agent in the system and describe its functions in detail. The second level deals with the behavior of these agents and explains in detail their decision-making processes and tasks. The third level examines how agents interact and communicate to achieve system-wide objectives. Using Contract Net Protocol (CNP), agents can demonstrate and manage interactions, ensuring efficient negotiations and resource allocation. The multi-level approach offers effective solutions for optimizing operations of dynamic and distributed manufacturing systems.
Latent Dirichlet Allocation, Vocabulary Handler, and Domain Knowledge-Based Framework for Text classification
Authors: B. Lavanya  , U. Vageeswari
Published: December 2024

Due to the remarkable advancement of information technology, the amount of unordered text information in the computer database is constantly expanding, making it difficult to organise, analyse, summarise, and classify text. The process of retrieving important data from unstructured text is called text mining. The latent Dirichlet Allocation (LDA) technique which is an unsupervised machine learning technique, is frequently employed for topic modelling. The result from an LDA makes perfect sense for categorization. Domain-specific and Out of Vocabulary (OOV) terms abound in the LDA model. This research proposes an unsupervised framework for text categorization using LDA with enhanced vocabulary handling and domain knowledge. Domain-specific terms are eliminated, and the most comparable LDA Dictionary words are used in place of OOV words. Two datasets with various data categories were used in the experiment. On both datasets, the proposed model performs better than alternative models. By using the suggested framework, Accuracy, Purity, Precision, Recall, and F1- scores were all improved.
Environmental Impact Assessment for Renewable Energy Projects: Risks and Solutions in Solar, Wind, and Hydropower
Authors: Dr. Faisal A. Osra
Published: December 2024

Reducing global warming and promoting natural benefits are critical objectives for the global community, spanning various sectors and initiatives. The commitment to reducing greenhouse gas emissions by 80-95 percent by 2050, along with promoting renewable energy sources, is central to this effort. Solar power development in regions with high solar radiation requires open, agricultural, or marginal areas, distinguishing it from projects that misuse valuable land. Social concerns, such as noise from wind turbines and changing landscapes, raise questions about visual impact and landscape rehabilitation. Impacts on flora and fauna, shadowing effects, and wind changes caused by turbines are important considerations. Hydropower projects also affect river dynamics through reservoirs and physical barriers, which must be addressed before implementation. The Environmental Impact Assessment (EIA) is a valuable tool for identifying these issues, as it provides a baseline for analyzing project impacts and includes predictive forecasts. Managing these environmental and social factors, including potential legal challenges, requires adopting a management system early in the project. This paper will analyze the environmental and social impacts of renewable energy projects, focusing on case studies of solar, wind, and hydropower. It will also explore how EIAs can predict and mitigate negative impacts and present strategies for effective environmental management at the early stages to balance environmental and social objectives.
Monitoring and Analysing the Performance of Runners using the Internet of Things
Authors: Nur Farhana Zainuddin  , Azrul Amri Jamal  , M.D. Zakaria  , Elsayed Makki Elbishr Ali Hassan  , Azwa Abdul Aziz  , Hani Mohammed Al-Duais
Published: December 2024

Running is a common and easily accessible physical activity that greatly enhances mental and cardiovascular health. The development of technology has completely changed the way we think about fitness. However, the absence of a sophisticated and intelligent running tracking system poses several problems. Among the problems running enthusiasts face is recording the data and analysing the runner for coaches to monitor their health in field paces, heart rate, and distances using wearable devices. In the absence of monitoring, the runner is incautious about their condition while running to avoid injury. The objectives are: to design the implementation of wearable devices equipped with sensors to record comprehensive data during a runner's activity; to develop a system for coaches to monitor and track the health and performance of runners in real-time; and to test the effectiveness of the sensor monitoring system in promoting runner awareness and preventing injuries during running. The methodology is to create a wearable device sensor using IoT components that can easily attach and detect the runner’s health accurately. In this project we can expect that the system should seamlessly function once installed on the runners, the data will be forwarded to the server attached to the laptop where the coaches and admin can view the runner's health status. Also, the device can trace and record the data without causing any problems. Hence, the objective of the result is to study a better-running tracking system by analysing its limitations, especially concerning accurate data storage, real-time performance analysis, and personalized insights. The embedded device can record the health status and able to be monitored by the coach. The combination of embedded technology and monitoring systems will effectively prevent injuries among runners. The technologies/tools used are MKR1010, Arduino IDE, Laravel, Firebase, PHP, and Processing IDE.
Bibliometric Analysis on Educational Games in Preschool Children's Learning
Authors: Herrera Rojas Mirela Zuceti  , Herrera-Rojas Jessica Juliana  , Ana Cecilia Sotelo-Núñez  , Oscar López-Regalado  , Barrueto-Chunga Luis Enrique  , Mogollón-Torres Daniel Alberto
Published: December 2024

The study examines the crucial importance of the early years in the cognitive and socio-emotional development of children, focusing on the essential role of educational games in early learning. It analyzes the variety and impact of these games on the acquisition of cognitive, social, and emotional skills in early childhood, aiming to conduct a critical analysis of recent research, identify trends, evaluate effective methodologies, and highlight areas that require further investigation in the field of educational games for preschoolers. To carry out this research, a systematic literature review was conducted, using a comprehensive search on the Web of Science and Scopus platforms, complemented by bibliometric analysis. The search period was limited to studies published between 2018 and 2023, selecting 39 articles that met predefined criteria. The results emphasize the effectiveness of educational games in developing key skills such as problem-solving, creativity, collaboration, and conceptual understanding. The diversity of games suggests the need for adaptable and personalized educational strategies in preschool education. The main conclusion is that educational games are valuable and effective tools for the holistic development of preschool children. However, their use and application require careful, evidence-based approaches tailored to the individual needs of each child. This study highlights the value of incorporating educational games into the preschool curriculum, demonstrating that they are not just entertainment but fundamental pedagogical tools that can significantly enhance learning and development processes in the early years of life.
Predicting Security Breaches in AI-Powered Mobile Cloud Applications Using Deep Random Forest Algorithm
Authors: S. Hassan Abdul Cader  , Dr. K. Nirmala
Published: December 2024

This research addresses the need for a predictive approach to detect security breaches in AI-powered mobile cloud applications. We propose a novel approach combining Radial ResNet for advanced feature extraction with Random Forest (RF) for classification. This hybrid model is designed to analyze complex and dynamic datasets in real-time, enhancing the predictive capabilities for identifying potential security threats. Results indicate significant efficacy, with the algorithm achieving high accuracy and sensitivity in predicting security breaches. The Radial ResNet–RF combination achieved a training accuracy of 98.5%, with precision, recall, and F1-score values of 97.8%, 98.2%, and 98.0%, respectively. On testing and validation datasets, the model demonstrated accuracies of 96.2% and 95.5%, respectively.
Echoes Through Time: A Cultural And Historical Examination Of Communication
Authors: Nguyen Thi Lan Anh
Published: December 2024

Communication has been central to human progress, shaping societies, cultures, and historical narratives throughout history. This study explores the evolution of communication from oral traditions and early writing systems to the emergence of the digital age, emphasizing key developments and their impact on human interaction and social structures. By examining critical junctures—such as the advent of the printing press, the industrial revolution, and the rise of digital technology—the research illustrates how communication has influenced cultural identities, facilitated the spread of knowledge, and supported societal advancements. The study also considers the challenges posed by modern communication, including misinformation, privacy issues, and the effects of digital fatigue. The findings underscore the dual role of communication as both a unifier and a source of complexity in the globalized world. This comprehensive analysis provides valuable insights into the historical significance of communication and its future implications in fostering connectivity and cultural cohesion.
Cybersecurity In Smart Grids: Deep Learning Approaches To Intrusion Detection In Iot-Enabled Power Systems
Authors: Mr. Panthangi Venkateswara Rao  , Dr. Swati Saxena  , Dr. M. Tamilarasi  , Dr. U. Priya  , Ramakrishna Vadrevu  , Ziauddin Syed
Published: December 2024

The increasing complexity and interconnectedness of IoT-enabled smart grids expose these systems to diverse cybersecurity threats, making effective intrusion detection essential. This research presents a comparative analysis of deep learning models—Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, and Transformer models—for intrusion detection within smart grids. Each model was evaluated on performance metrics such as accuracy, precision, recall, F1-score, and computational efficiency. Results show that the Transformer model achieved the highest accuracy and F1-score, excelling in capturing complex temporal patterns critical for identifying sophisticated intrusions. The LSTM model demonstrated strong recall but required higher computational resources, while the CNN model, though computationally efficient, displayed limitations in capturing temporal dependencies.The study also investigated the effect of hyperparameter tuning, with surface plot analyses revealing optimal learning rate and batch size combinations for each model to maximize detection accuracy. Radar and area graph analyses illustrated the models' performance across different metrics, highlighting CNNs for rapid detection, LSTMs for recall-intensive applications, and Transformers for high-accuracy scenarios. Compared to traditional machine learning methods, the deep learning approaches demonstrated superior accuracy, establishing them as viable solutions for enhanced cybersecurity in smart grids.This work provides a foundation for deploying robust and scalable deep learning-based intrusion detection systems in IoT-driven power systems. Future research could further optimize these systems through hybrid or ensemble approaches to adapt to evolving cyber threats in smart grid environments.
Computational Fluid Dynamics And Machine Learning For Predictive Analysis In Turbomachinery
Authors: Dr. R Jegadeesan  , Ms. Sri Lavanya Sajja  , Mr. P. V. Ramanaiah  , Dr. K. Venkata Naganjaneyulu  , Baljeet Yadav  , Saket Rusia
Published: December 2024

This study explores the integration of Computational Fluid Dynamics (CFD) and Machine Learning (ML) for predictive analysis in turbomachinery, focusing on the optimization of efficiency, power output, and structural integrity. CFD simulations are employed to model the fluid flow and thermal gradients within turbomachinery components, while ML techniques are used to analyze these simulations and develop predictive models for performance optimization. The results demonstrate that coupling CFD with ML allows for the identification of design modifications that optimize efficiency and power output under varying operational conditions. Surface plots generated from CFD simulations provide detailed insights into temperature and pressure gradients, revealing their impact on system performance. Scatter plots further highlight key correlations, such as the relationship between efficiency and temperature and power output and pressure, which are essential for fine-tuning operational parameters. Machine learning algorithms, such as regression models and neural networks, are trained on CFD data to predict performance trends, enabling faster, more accurate decision-making. The integration of these techniques not only enhances the operational efficiency of turbomachinery systems but also contributes to the development of real-time monitoring systems for predictive maintenance. This research lays the groundwork for future optimization algorithms and broader applications of CFD and ML in the design and operation of energy systems, offering significant potential to improve system reliability, reduce maintenance costs, and extend equipment lifespan.
Activity Of Different Metals In Disinfection Of Activated Sludge By A Fenton-Like Process
Authors: Māra Blumfelde  , Vadims Bartkevics  , Olga Muter
Published: December 2024

Treatment technologies of the activated sludge (AS) for its use as a fertilizer should include a hygienization process to meet legislative criteria. Numerous approaches have a strong effect on sludge stabilization and dewatering, while disinfection efficiency is unsatisfactory. In this respect, a combination of different physical (e.g., thermal) and chemical processes could develop hybrid methods for converting the sludge into a qualitative fertilizer with safe microbiological characteristics. Our study was focused on the comparison of the efficiency of Fe(II), Cu(II), Co(II), Mn(II), and Zn(II), which mediated the Fenton-like reaction in combination with H2O2, - for disinfection of AS from the municipal wastewater treatment plant. The enzymatic activity, i.e., fluorescein diacetate (FDA) hydrolysis, served as an integral criterion of cell inhibition. After 72h treatment of AS with 7 mM Me and 200 mM H2O2, an inhibition efficiency was ranged as follows: [Fe+Co]>>[Co]≥[Fe+Cu+Co+Mn+Zn]>[Mn]>[Fe+Cu+Co]>[Cu]>[Zn]>[Fe+Zn]>[Fe].The effect of Fe(II) and Co(II) as single metals and binary mixtures on AS ecotoxicity was evaluated. No toxic effect was detected in a 24h immobility test with Artemia sp. and 72h root elongation of Raphanus sativus seeds. The AS treatment with Fe(II)+Co(II) in the presence of H2O2 leads to additional antimicrobial resistance of AS microbial community towards amikacin, piperacillin / tazobactam constant 4, doripenem, impenem, meropenem, and cefepime, as compared to untreated AS.
Ai Application Framework For Systematic Onboarding In Organizations
Authors: Simarjit Singh Lamba  , Upinder Kaur
Published: December 2024

The integration of AI within an organizational framework often encounters formidable challenges, primarily rooted in the initial identification of suitable use cases. Inadequate identification can result in misaligned technology solutions, leading to the squandering of resources and a lack of discernible benefits. This challenge necessitates a meticulous approach to ascertain the appropriate use cases that align with the organization's strategic goals and operational requirements.To tackle this challenge effectively, organizations must embark on a methodical process that commences with a comprehensive comprehension of the overarching objectives. This involves an in-depth analysis of the organizational landscape, encompassing current pain points, operational inefficiencies, and potential areas for technological innovation. Subsequently, conducting comprehensive needs assessments, evaluating the quality and accessibility of available data, and determining the feasibility and suitability of AI solutions become pivotal steps. Engaging diverse stakeholders across various departments to prioritize challenges and opportunities, validating concepts through proof-of-concept projects or pilots, and securing buy-in from key decision-makers are integral component of this process.Furthermore, a structured approach involves iterative refinement, continuous evaluation, and a relentless commitment to a data-driven strategy. Emphasizing ongoing reassessment ensures that the selected use cases remain aligned with the evolving business goals and organizational dynamics. By diligently navigating these steps, organizations can establish a foundation for the successful integration of AI, harnessing its potential to streamline operations, optimize processes, and drive measurable outcomes that align with overarching strategic imperatives.In this paper we will define an AI application framework which works on affinity factors to help organizations select the right use cases for AI application.
Optimizing Customer Support for Small Businesses Using Machine Learning Algorithms
Authors: G. Nagaraj  , Divya Nimma  , Prasad H K  , Nageswara Rao Eluri  , Janardhana C  , Sudheer S Marar
Published: December 2024

In today's highly competitive business landscape, customer support plays a critical role in building customer loyalty and driving business success. For small businesses with limited resources, delivering prompt and effective customer support can be challenging. Machine learning (ML) algorithms offer a powerful solution to enhance customer support by automating repetitive tasks,analyzing customer sentiment, and providing insights into customer preferences. This paper explores various machine learning techniques suitable for small businesses, such as natural language processing (NLP) for sentiment analysis, chatbots, and predictive analytics to optimize support operations. We also discuss challenges and implementation strategies, highlighting how small businesses can leverage machine learning to improve customer satisfaction and operational efficiency. By using real-world case studies and recent advancements in ML technology, this paper demonstrates the transformative impact of machine learning on customer support, empowering small businesses to compete with larger enterprises.