bluuebunny/crossref_metadata_2025 huggingface dataset:
Indicadores de bienestar subjetivo y sus determinantes socioeconómicos: un estudio para la sociedad española
Authors: Pena-López Atilano , De Juan-Díaz Rebeca
Published: November 2024

En el presente trabajo analizamos los indicadores de bienestar subjetivo revelado (BSR) en la sociedad española. Nuestro objetivo es comprender las diferencias entre los tres tipos de indicadores (evaluativos, eudaimónicos y hedónicos) y su relación con los condicionantes socioeconómicos, en particular el ingreso. Tras una revisión crítica de la literatura existente, examinamos las variables socioeconómicas que se han relacionado con los diferentes tipos de BSR y realizamos un análisis empírico sobre una encuesta de Bienestar Subjetivo en España. Los resultados muestran que existen diferencias significativas entre los tres sistemas de evaluación. Los indicadores evaluativos y eudaimónicos se relacionan con el estatus y el ingreso, en tanto que en los indicadores hedónicos esta relación no es significativa. Los resultados apoyan la afirmación de que el estatus y el ingreso tienen influencia significativa en el BSR en la medida en que se acentúa el peso de la evaluación cognitiva.
In this work, we analyze the indicators of subjective well-being (SWB) in Spanish society. Our goal is to understand the differences between the three types of SWB (evaluative, eudaimonic, and hedonic) and their relationship with socioeconomic determinants, particularly income. After a critical review of the existing literature, we examine the socioeconomic variables associated with the different types of SWB and conduct an empirical analysis for the Spanish society. The results show that there are significant differences between the three evaluation systems. Evaluative and eudaimonic indicators are associated with status and income, while this relationship is not significant for hedonic indicators. The results support the claim that status and income have a significant influence on SWB to the extent that the weight of cognitive evaluation is accentuated.
La compra de libros como consumo cultural: Un análisis prospectivo de los motivos que generan la demanda
Authors: Alonso-Hernán Daniel
Published: November 2024

En el presente artículo se analizan los diferentes motivos que conducen a la compra de libros, prestando especial atención a las relaciones sociales de los lectores, a la obtención de estatus mediante la lectura, al desarrollo del proceso de compra de libros o a la relevancia de la editorial para el consumidor. Asimismo, a lo largo de todo el estudio se destaca la dimensión simbólica que rodea al consumo de libros, en tanto que consumo cultural. Tras realizar una revisión de la literatura sobre la temática, se realizan una serie de entrevistas en profundidad a las que se les aplica el análisis sociológico del discurso. De este procese se extrae que, además de buscar entretenerse o adquirir conocimientos, al comprar libros los lectores también tratan de proyectar un determinado estilo de vida o adquirir distinción social. Por otra parte, cabe señalar que la posición que ocupa cada lector en el espacio social repercute en su forma de consumir y relacionarse, en general, con los libros. Además, hay un amplio segmento de lectores a los que, por un lado, les gusta ser propietarios de los libros y crear su biblioteca personal y, por otra parte, disfrutan del proceso de compra presencial. Por ello, se puede afirmar que el libro se defiende a sí mismo como objeto, por lo que el desarrollo del libro en formato digital no entra necesariamente en conflicto con el formato en papel.
This paper analyzes the different reasons that lead to buy books, emphasizing readers’ social relationships, the status achievement through reading, the process of purchasing books or the importance of the publisher for the customer. Furthermore, throughout all the study, the symbolic dimension that surrounds the purchase of books is highlighted, as cultural consumption that it is. The first part of the paper includes a literature review of the topic, and the second part is empirical and contains in-depth interviews that are analyzed through sociological discourse analysis. Outcomes obtained reveal that people buy books not only to enjoy or acquire knowledge, but also to socialize and get social distinction, too. Moreover, each reader’s social position has an effect on the way s/he buys and relates with books. In addition, there is a large segment of readers who, on the one hand, like to own books and have their own personal library, and on the other hand, they enjoy the process of face-to-face book purchase. Therefore, book defends itself as an object and, so the development of e-books does not necessarily conflict with paper books.
Stress analysis of splice joint of the aircraft bottom wing skin by Finite Element Method
Authors: N Rajesh Kumar , S Gokul , V Uday , M Vishwa
Published: November 2024

This paper investigates the maximum stress concentrated part of the splice joint of an aircraft bottom wing skin due to tensile loading. Wings are the aerofoils attached to each side of the fuselage to produce lift force. Joints are inevitable in any large structure like an aircraft wing. Splicing is normally used to retain a clean aerodynamic surface of the skin for most of the aircraft structure. This analysis considers the wing box with a bottom skin splice joint. The wing box comprises of two spar beams, three ribs, stiffeners covered with skin plate. In this paper the chord wise splicing of wing skin is considered for a detailed analysis. The splicing is multi row riveted joint under the action of tensile in plane load due to wing bending. The stress analysis of the joint is carried out to compute the stresses at rivet holes due to By pass load, bearing load and secondary bending. The splice is optimized to minimize the rivet hole local stress. A finite element analysis is carried out to evaluate the stresses. Analyses were performed by MSC PATRAN and NASTRAN software.
Sentiment Analysis of Incoming Voice Calls
Authors: K R Sushmitha , G R Rangalakshmi , A Suguna
Published: November 2024

This project aims to meet the increasing need for real time sentiment analysis within voice call interactions, acknowledging the rising significance of voice based engagements in today’s telecommunications realm. For instance, pre trained word embeddings, such as Word2Vec, Glove, and bidirectional encoder representations from transformers (BERT), generate vectors by considering word distances, similarities, and occurrences ignoring other aspects such as word sentiment orientation. Aiming at such limitations, this paper presents a sentiment classification model (named LeBERT) combining sentiment lexicon, N grams, BERT, and CNN. In the model, sentiment lexicon, N grams, and BERT are used to vectorize words selected from a section of the input text. CNN is used as the deep neural network classifier for feature mapping and giving the output sentiment class. The proposed model is evaluated on three public datasets, namely, Amazon products’ reviews, Imbd movies’ reviews, and Yelp restaurants’ reviews datasets
AI in Health
Authors: D Mohith , K Rajeev
Published: November 2024

Artificial Intelligence (AI) is transforming healthcare, especially in personalized medicine, where AI driven tools enable tailored patient care. This paper explores AI applications across predictive analytics, genomics, and real time patient monitoring. Emphasizing AI’s role in precision diagnostics and treatment customization, we highlight challenges and potential pathways for implementation.
A Machine Learning approach for crop and fertilizer using Ensemble Model with XGBoost and Random Forest Algorithms
Authors: K Surya Narayanan , E B Varun Kumar , J Srinath , H Yashvanth , JK Jayakumari
Published: November 2024

The machine learning method for crop and fertilizer recommendations is presented in this study in an effort to improve agricultural productivity. By combining the strengths of XGBoost and Random Forest algorithms through an ensemble model, this system provides customized recommendations based on important soil and environmental parameters, such as temperature, humidity, rainfall, pH, nitrogen, phosphorus, potassium, and other indicators for the optimal crop growth and yield. Using a soft voting technique, the model outperforms traditional single model systems by averaging predictions from both algorithms to attain a high level of accuracy beyond 98%. This ensemble design captures the high accuracy of XGBoost with its data handling capabilities while balancing it with Random Forest’s strength and reduced overfitting and also making the model in many different kinds of agricultural environments. Precision and computational efficiency were demonstrated by the model, which was trained on a dataset of 3,610 samples with an additional 903 samples set aside for testing. Although it takes longer to train, XGBoost helps by producing extremely accurate predictions. Random Forest helps by training more quickly and maintaining performance even when dealing with many different kinds of data. When combined, these models produce an efficient system that can be adjusted to various soil types and climates, empowering farmers to make data driven, well informed decisions for sustainable farming. This study demonstrates how ensemble learning can revolutionize precision agriculture by assisting farmers in making the best decisions possible to enhance crop yield and resource management while encouraging sustainable practices. Real time data integration may be incorporated into future research to increase the model’s adaptability to shifting agricultural conditions. All things considered, this system improves the use of machine learning in the agricultural sector by offering farmers a strong tool to help them in making profitable and sustainable agricultural decisions.
Crow Search Optimized Pi Control Approach for Photovoltaic Connected Grid System
Authors: E Immanuvelbright
Published: November 2024

This paper presents an advanced Photovoltaic (PV) grid connected system utilizing a Superlift Luo Converter (SLC) to enhance energy conversion efficiency and system performance. The proposed system integrates a Crow Search Algorithm (CSA) optimized Proportional Integral (PI) controller to regulate the output of SLC. The SLC is employed to boost the voltage from the PV panels, enhancing energy extraction. The VSI, controlled by the optimized PI controller, ensures stable and efficient power transfer to the grid. An LC filter is incorporated to mitigate harmonics and ensure compliance with grid standards. The use of CSA for PI controller optimization improves dynamic response and system robustness. This results accomplished using MATLAB demonstrate that the proposed system achieves high efficiency, improved stability, and superior grid compliance compared to conventional approaches. The comparative analysis presented in this work demonstrates that the proposed approach significantly outperforms traditional methods, achieving an impressive efficiency of 95.2%. This highlights the effectiveness of the new system in optimizing performance, making it a valuable alternative to established techniques. This approach highlights the effectiveness of combining advanced converter topologies and optimization techniques to enhance PV grid connected systems.
Women Safety Analytics- Protecting Women from Safety Threats
Authors: J Midhula Sree , G S Amitha Shri , Jha Ashutosh Kumar , K Kishor
Published: November 2024

In today’s world, incidents of harassment and threats against women are increasingly frequent across various settings. Social media platforms, in particular, play a significant Role in facilitating these global issues. This paper aims to develop a novel methodology that provides a robust safety mechanism for women, helping them avoid dangerous situations and individuals. The proposed solution, referred to as the Machine Learning based Social Threat Filter (MLSTF), introduces an innovative approach to threat detection. By focusing on threats originating from social media platforms primarily Facebook, Twitter, and similar sites—this paper demonstrates how to identify and process social media posts, analysing tweets and other posts to detect harmful content. This is achieved by calculating word intensities to assess the nature of the content. Additionally, this paper describes a method that captures real time data about women’s safety through a wearable device (SWD) designed to monitor individual users continuously. This SWD incorporates intelligent sensors, including a body position identifier, GPS, an accident detection sensor, a small microphone to capture distress signals like a black box, and a pen hole camera for location based imaging. Together, these sensors gather real time data on a user’s safety, transmitting it to a server for processing and analysis, ensuring responsive action where needed.
Urban Glide Two-Seater Smart E-Car: An Innovative Approach to Urban Mobility
Authors: G Keerthi Chowdary , R Harini , B Puneeth , G Anura Chiru Pavan , S Sujatha
Published: November 2024

The Two Seater Smart E Car is a compact, electric vehicle designed to address urban transportation challenges such as traffic congestion and environmental pollution. This project integrates cutting edge smart technologies and sustainability principles to create an efficient, eco friendly, and user friendly vehicle optimized for urban environments. This paper presents the design, development, testing, and potential impact of the Two Seater Smart E Car, outlining its role in reducing emissions and promoting shared mobility.
OMR Detection Using Image Processing Technique
Authors: Rooge Sujal , Kadli Veeresh Kotresh , Manjare Shivanand , M B Varun Kumar , A Suguna
Published: November 2024

Optical Mark Recognition (OMR) is a widely used method for automatically capturing data from human marked documents such as tests, surveys, and assessments. This paper presents a technique for OMR detection leveraging image processing methods. Traditional OMR systems rely on specialized hardware; however, with advancements in computer vision, we propose a solution that utilizes common digital imaging devices like scanners or cameras, making the process more accessible and cost effective. The technique involves pre processing scanned or captured images to enhance clarity, followed by feature extraction methods to detect and interpret marked areas accurately. Key image processing steps, such as grayscale conversion, noise reduction, edge detection, and contour identification, are employed to locate and analyze marked fields. Furthermore, our approach incorporates algorithms to handle skew, uneven lighting, and other imperfections in image quality. This method demonstrates high accuracy in detecting marks even in suboptimal conditions and offers flexibility across different form designs. The proposed system can be integrated into existing digital frameworks, reducing dependency on traditional OMR scanners while maintaining precision and efficiency.
Revolutionizing Mental Health: The Role of Artificial Intelligence in Current and Future Solutions
Authors: P Anu , Chhetri Anil , S Aravind Narayanan , SA Kushal , R Tharani , S Anitha
Published: November 2024

Artificial Intelligence (AI) is making transformative strides in the mental health sector. This study explores the integration of AI into mental health services, focusing on current applications, ethical challenges, and future directions. By reviewing literature from databases such as PubMed, IEEE Xplore, PsycINFO, and Google Scholar, we examine the role of AI in diagnosis, treatment, and ongoing monitoring within mental healthcare. The findings reveal significant potential in AI driven early diagnosis, personalized treatment plans, and AIpowered virtual support systems, all of which contribute to more accessible and effective mental health care. However, ethical considerations related to privacy, bias, and the need for human presence highlight the necessity for balanced AI integration. The study concludes that while AI has transformative potential, responsible use is critical to fully leverage its benefits in mental health services.
Rainfall Prediction for Agriculture Optimization
Authors: A Priyanka , Patil Balaji , Yadev Purige Bhavyesh , Kumar Suresh
Published: November 2024

This paper presents a machine learning based approach to predict rainfall and crop yields, leveraging the MERN stack to develop a user friendly web based application for farmers. The proposed system uses time series algorithms for rainfall prediction and regression models for estimating crop yields. These predictions help farmers optimize their resource management and make data driven decisions. The system integrates MongoDB for data storage, Express and Node.js for backend processing, and React for a dynamic front end interface. This innovative solution empowers farmers to adapt to climate changes and improve agricultural productivity.
Artificial Neural Networks for Stock Market Prediction
Authors: C Sathish , M Vijayalakshmi , Kumar R. Tharun , Kumar S. Sandeep , R Pavan , N Sathish
Published: November 2024

Artificial Neural Networks (ANNs) have gained significant attention in recent for their potential to model complex, non linear relationships in various domains, including financial markets. In stock market prediction, ANNs are used to forecast stock prices, identify trends, and support investment decision making. It explores the application of ANNs in stock market prediction, focusing on the underlying principles of neural networks, common architectures such as feed forward networks, recurrent neural networks (RNNs), and long short term memory (LSTM) networks, which are particularly effective for time series forecasting. The ability of ANNs to capture intricate patterns and relationships in historical stock data, such as price movements, trading volume, and technical indicators, offers a promising avenue for predictive analysis. Despite challenges such as over fitting, the need for large datasets, and market noise, ANNs have demonstrated competitive performance when compared to traditional statistical methods. This study discusses various techniques to improve ANN model accuracy, including data pre processing, feature selection, and model optimization. Ultimately, it highlights the growing roles of ANNs in financial forecasting and their potential to enhance market prediction strategies in the rapidly evolving field of algorithmic trading.
“Powering the Future: Smart Home Energy Management Unleashed”
Authors: M Diva Darshini , G Basavaraja , C Valarmathi , G Lakshna priya , L Bhargavi
Published: November 2024

The zone of a smart house is constantly evolving in terms of technology, leading to the improvement of Home Energy Management System (HEMS), which focuses on efficient energy consumption, sustainability, and ease for the user. “Powering the Future: Smart Home Energy Management Unleashed” states that HEMS can be a complete and smart home energy management solution. Such intelligent HEMS can control and assess the energy consumption of different devices, analyze the consumption patterns, and promote energy efficient behaviors through cloud computing and IoT devices like smart plugs and energy monitors. This report focuses on the key elements of HEMS, which include energy waste elimination strategies, energy use management through remote control, and time series forecasting of the electric utilization rate.
A Machine Learning approach for Smart Community Tracking System Using Yolov3 Algorithm
Authors: V Meena , S Mega Nathan , C Mithulashwaran , K Pavamana
Published: November 2024

The machine learning method for Effective management of public spaces, especially in scenarios involving large crowds, is crucial for ensuring public safety and efficient use of resources. This project focuses on the development of a real time person counting and tracking system, specifically designed to assist with crowd control and safety in public areas. The system leverages advanced deep learning techniques, combining the Convolutional Autoencoder for crowd analysis with YOLOv3 (You Only Look Once version 3), a powerful object detection algorithm, to accurately identify, track, and monitor individuals in real time. The YOLOv3 algorithm serves as the core component for object detection, enabling the system to recognize and follow individuals across consecutive frames of video footage. Its ability to detect multiple objects in a single pass ensures efficient tracking of people in dynamic environments, even in cases of partial occlusion or varying lighting conditions. YOLOv3’s high speed processing and accuracy make it ideal for real time applications, where quick and reliable detection of people is essential. In parallel, the Convolutional Autoencoder is used to enhance the tracking capabilities by analysing crowd density and patterns within a given area. By learning spatial features from video frames, the autoencoder supports the system in estimating the number of people in crowded environments, where individual detection may be challenging due to overlaps or dense packing of people. This combination of techniques ensures both precise detection of individual people and the ability to monitor overall crowd movement and density. The system further enhances its utility by recording entry and exit times, thus offering a comprehensive log of people flow in and out of the monitored area. This feature is especially valuable for authorities or organizations responsible for managing large public spaces, such as airports, train stations, shopping malls, and event venues, where the real time data on crowd size and movement can inform better decision making. The recorded data is stored for further analysis, providing insights that can be used to optimize the use of space, improve crowd management strategies, and enhance overall safety protocols. By integrating these cutting edge deep learning techniques, this project provides a reliable, scalable solution for crowd monitoring, offering real time insights into people flow, occupancy levels, and movement patterns. The system’s ability to operate autonomously, without the need for human intervention, makes it a valuable tool for improving the safety and efficiency of public space management.
Advancing Cybersecurity with Deep Learning: Innovative Approaches and Real-Time Applications
Authors: Kosuri Sandeep , Eeday Saranya
Published: November 2024

Sophisticated security measures are required to safeguard vital infrastructure and sensitive data due to the ever changing nature of cyber threats. Integrating deep learning techniques into cybersecurity is the subject of this study, which aims to improve threat detection, response, and prevention using novel methodologies. We compare the performance of different deep learning architectures, such as RNNs and CNNs, in detecting patterns that can indicate cyberattacks, using tools like malware detectors and intrusion detection systems (IDS). By utilizing massive datasets and real time analytics, these models outperform conventional methods in terms of speed and accuracy. Moreover, we offer real world examples of deep learning in action, including automated threat intelligence analysis and anomaly detection in network data. Strong training processes and model optimization are necessary, as the results show that cybersecurity frameworks must constantly learn and adapt. Also covered are issues with data privacy and the need for computational resources, two of the many obstacles to using deep learning solutions in cybersecurity. By expanding our knowledge of how deep learning can strengthen cybersecurity measures, this research ultimately leads to more resilient systems that can withstand emerging cyber threats.
Resume Building Web Application
Authors: B Jeevan , A Suguna
Published: November 2024

These days, landing a job is tough, and creating the perfect resume is often the biggest challenge for job seekers. A resume is usually the first thing employers look at when considering an applicant, and they use it to narrow down candidates before moving to interviews. This paper aims to present an easy and user friendly way to build resumes. We are introducing an app that helps users create resumes simply by providing their information. The resume builder app lets users log in or sign up using OTP verification and allow them to create, update, delete, view, and save their resumes in PDF format.[3] This project introduces a web based resume building application designed to simplify the resume creation process. This tool is aimed at making resume building more accessible, efficient, and user friendly for individuals at any career stage.[1] With the rise of online recruitment, traditional hiring methods are becoming less effective. In India, especially in the IT sector, there are plenty of jobs, but many candidates don’t meet the requirements. Resumes are often poorly organized, and the skills listed aren’t verified, making it hard for recruiters to choose the right person for the job. Big tech companies use an Application Tracking System (ATS) to filter through many resumes, but this still leaves challenges.[4] To solve this problem, we propose building a machine learning web app that creates well structured, verified resumes and portfolios. The platform, called “Real Resume,” will verify the candidate’s skills through mock tests and make sure the resume meets ATS guidelines. Verified resumes will be shown to recruiters and categorized according to their needs, making the hiring process easier by ensuring that only qualified candidates are presented for each job role.[2] A resume is a document job seekers use to highlight their qualifications for a position, summarizing their experience and education to show why they’re a good fit for a job. For new graduates, creating a resume can be challenging because they have limited practical experience. Research shows that around 75% of resumes don’t even make it past the first stage at companies, and the numbers are worse for fresh graduates. [5] Other common problems with resumes include poor organization Incorrect formatting, and not knowing which sections to include or leave out.[5]
Design and Development of a Modern Mechanized Pepper Cleaning Machine
Authors: L Harish Babu , H C Rakshith , R Keerthana , N Darshan , R Sivaram Kumar
Published: November 2024

The Modern Mechanized Pepper Cleaning Machine project was designed to develop a cost effective, small scale solution for efficiently cleaning black pepper. Traditional manual methods of cleaning pepper involve labour intensive sorting and cleaning techniques, which are time consuming and inefficient. The proposed machine aims to automate the cleaning process by incorporating mechanical sieving and airflow based cleaning, thereby minimizing human labour, energy consumption, and time. This paper outlines the design, fabrication, testing, and results of the mechanized pepper cleaning machine, emphasizing its potential to enhance productivity for small scale farmers and processors.
Emergency Alert System
Authors: M Srinivasan , S Pradeep , R Kishore , K Gajendran , J Prakash , S DineshKumar
Published: November 2024

This paper proposes a novel system to enhance the efficiency of medical emergency services during road accidents. The increasing number of accidents due to reckless driving, drunk driving, and driver negligence necessitates a more effective way to respond to these incidents promptly. The current system often faces delays in informing the ambulance and police authorities, which may lead to severe consequences for the victims. To address this issue, the paper suggests integrating sensors in vehicles to monitor critical data like displacement, location, and chassis number using a Global Positioning System (GPS) device. This information would be shared through the Internet of Things (IoT) to a control center, allowing them to dispatch the necessary emergency response units to the accident site swiftly. The proposed system utilizes a gyro sensor (MEMS sensor) to detect angle changes between 45° and 315° and a vibration sensor that activates at a collision impact frequency of 90 Hz or more. This system could potentially serve as a reliable addition to the safety features in vehicles manufactured in India, ensuring better accuracy in accident detection and reporting. In conclusion, this paper presents a valuable concept to improve medical emergency services during road accidents by implementing a smart vehicle based system. The integration of sensors and IoT technology can significantly reduce response time and save lives by providing immediate assistance to victims in need.
Wireless Charging System Coupled for Electric Cars
Authors: M Murali , K Gangabhavani , T Satish Kumar , G Govind Srinivas , Y Vinay , K Bhagyalakshmi , N Suresh
Published: November 2024

Electric vehicles (EVs) have steadily developed over the last decade as more energy-efficient alternatives to internal combustion engine cars. With increased range and performance, EVs are growing more reliable. Expanding the availability of electric vehicles can be facilitated via wireless charging. Wireless charging (WC) is an interesting remodelling makes electric cars to charge more convenient. Wireless Power Transfer (WPT) enables power to be transmitted from a power source without a trouble of connections, and this charging option increases the use of electric cars. This paper examines the most effective wireless power transmission techniques Magnetic Resonance Coupling (MRC), and its real-world application in EVs. It also examined how wireless power transfer efficiency declines with distance.
Determination of Artificially Ripened Fruits Using IOT
Authors: M Chethan , V Meghana , G Pruthvi Raj , M Arjun , K V Malini , K Ramya
Published: November 2024

Food adulteration refers to the practice of deliberately adding substances to food to increase its volume, weight, or to improve its appearance, texture, or flavor; it is a significant issue that affects the health and safety of consumers. With the increasing demand for food, the risk of contamination and the intentional addition of harmful substances has increased. There are several existing methods for detecting food adulteration, including chemical analysis, microscopy, sensory analysis, etc. While these methods are helpful, they can be time consuming, labor intensive, and may not provide Real-time results. Using the Internet of Things (IoT), Machine Learning (ML) can significantly enhance the ability to identify food adulteration. Within this Framework, we are propose a solution to detect food adulteration using IoT and machine learning. The system comprises IoT sensors and devices to gather data on various parameters such as color, pH, gas content, etc. The collected data is fed into machine learning algorithms for pre processing, analysis, and testing. Any anomalies or deviations from the standard patterns are flagged for further investigation. ML algorithms can continuously learn from the collected data, enabling them to enhance their accuracy and effectiveness over time. By implementing this system, we aim to create a Real time, data driven approach to detecting food adulteration, ensuring food safety and quality for consumers by creating a warning system.
Speech Recognition Based On Machine Learning
Authors: A C Namruthah , V Amutha , M Jyothi , E S Tamil Varshini , L S Thaniska , K Karthika
Published: November 2024

Speech is the fundamental form of communication between the speaker and the listener. Machines are unable to understand the speaker’s words, while humans can easily understand their meaning. Today, machines take up the majority of our time, but we are unable to communicate with them. Speech recognition requires both the human brain and machine learning technology to interface with humans and robots. This paper uses English as the speech recognition language because it is a universal language.
Dynamic Bidding Platform for Enhanced E-commerce Products
Authors: Ealias Shiji , Shariff Arfathulla , BM Bhavana , SK Deekshak , S Deepika
Published: November 2024

Real time bidding (RTB) is one of the most striking advances in online advertising, where the websites can sell each ad impression through a public auction, and the advertisers can participate in bidding the impression based on its estimated value. In RTB, the bidding strategy is an essential component for advertisers to maximize their revenues (e.g., clicks and conversions). However, most existing bidding strategies may not work well when the RTB environment changes dramatically between the historical and the new ad delivery periods since they regard the bidding decision as a static optimization problem and derive the bidding function only based on historical data. Thus, the latest research suggests using the reinforcement learning (RL) framework to learn the optimal bidding strategy suitable for the highly dynamic RTB environment. In this paper, we focus on using model free reinforcement learning to optimize the bidding strategy. Specifically, we divide an ad delivery period into several time slots. The bidding agent decides each impression’s bidding price depending on its estimated value and the bidding factor of its arriving time slot. Therefore, the bidding strategy is simplified to solve each time slot’s optimal bidding factor, which can adapt dynamically to the RTB environment. We exploit the Twin Delayed Deep Deterministic policy gradient (TD3) algorithm to learn each time slot’s optimal bidding factor. Finally, the empirical study on a public dataset demonstrates the superior performance and high efficiency of the proposed bidding strategy compared with other state of the art baselines.
Self-balancing E-bike
Authors: Saravanan Om , J Mamatha , C Chandana , R Sukanthraj , K Lakshmi
Published: November 2024

Today, automation is essential rather than optional, especially as two wheeler accidents continue to occur despite various safety precautions. Many of these incidents result from loss of balance or control. Customers are increasingly demanding self balancing systems for two wheelers to address this issue. This paper explores the use of the gyroscopic method to create a self balancing bike. The gyroscopic effect, long employed in stabilizing systems across fields like military rocket guidance, aerospace, aeronautics, and marine navigation, has proven effective over the years. We adapted this principle to our two wheeler model, considering factors critical to balance, including the system’s center of mass, weight distribution, acceleration, steering dynamics, and, most importantly, the gyroscopic effect. Automation has become indispensable in enhancing safety standards across various fields, especially in the transportation sector. Two wheeler accidents, often caused by loss of balance or control, continue to occur despite numerous safety precautions, highlighting a critical area for improvement. In response, there is increasing demand from consumers for self balancing systems to improve stability and reduce accident risks for two wheeled vehicles. This paper investigates the application of the gyroscopic method to develop a self balancing mechanism for two wheelers. The gyroscopic effect has been extensively utilized in stabilizing systems in domains like military rocket guidance, aerospace engineering, aeronautics, and marine navigation, where precision and stability are paramount. This effect arises from the angular momentum of a spinning wheel, which resists changes to its orientation and thereby helps maintain balance. By harnessing this principle, the gyroscopic method offers a reliable approach to stabilize two wheelers, mitigating the risk of tipping or falling, especially during low speed maneuvers or while stationary. This adaptive approach allows for improved control and potentially prevents accidents, offering a substantial advancement in two wheeler safety. By adapting proven stabilization techniques, this system demonstrates a promising pathway to meeting modern demands for safer, self balancing two wheeled vehicles.
PKM Pemberdayaan Pemuda Melalui Keterampilan Servis Komputer di Kota Makassar
Authors: Mantasia Mantasia , Mustamin Mustamin , Ponta Tasri , Saharuddin Saharuddin , Ma’ruf  Idris Muh.
Published: November 2024

PKM Pemberdayaan Pemuda di Kota Makassar bertujuan untuk memberikan bekal pengetahuan dan keterampilan serta kecakapan hidup (life skills) yang dapat mereka gunakan untuk berwirausaha ataupun bekerja pada orang lain. Kegiatan ini sangat membantu mitra dan menjadi solusi alternatif dalam mengurangi angka pengangguran di Kota Makassar. Selain dari pada itu, mereka dapat membantu perekonomian keluarga, minimal mereka tidak lagi menjadi beban keluarga dan beban masyarakat. Metode yang digunakan dalam kegiatan ini adalah: 1) Pemberian keterampilan servis komputer serta pemeliharaannya, dan 2) Peningkatan pemahaman dan wawasan kewirausahaan. Pemberian keterampilan dilakukan dengan cara simulasi dan praktek langsung dengan objek yang sesungguhnya. Sedangkan pemahaman kewirausahaan dan pengembangan karakter dilakukan melalui metode ceramah dan tanya jawab,, peserta diarahkan untuk membuka lapangan kerja sendiri ataupun bila bekerja dengan orang lain dapat diterima dengan baik. Hasil yang diperoleh dari kegiatan ini adalah meningkatnya keterampilan peserta pelatihan servis komputer dan munculnya minat pemuda untuk berwirausaha.
SISTEM MONITORING KEBISINGAN BERBASIS INTERNET OF THINGS (IoT)
Authors: Azis Asmawaty , Amaliah Asma , Harun Rasyid Kurniawan
Published: November 2024

Kebisingan merupakan salah satu faktor yang dapat mengganggu ketenangan dalam belajar terutama ketika siswa sedang belajar mandiri. Beberapa siswa ingin belajar secara serius tetapi ada pula beberapa oknum yang tidak ingin belajar dan melakukan aktivitas yang dapat mengganggu konsentrasi siswa lainnya. Dari kasus ini maka dibutuhkan sebuah sistem yang dapat mengurangi kebisingan pada ruang kelas. Tujuan penelitian ini adalah untuk merancang suatu alat yang dapat digunakan sebagai pendeteksi suara kebisingan suara di kelas dengan memberi notifikasi berupa bunyi pada buzzer jika terjadi kebisingan yang berlebihan. Adapun metode yang digunakan adalah metode tahapan pengumpulan data kemudian melakukan tahap analisis terhadap kinerja alat yang telah dibuat dengan metode perancangan prototyping. Hasil pengujian menghasilkan alat deteksi kebisingan berbasis Internet of Things. Dari hasil pengujian yang telah dilakukan, peneliti menyimpulkan bahwa alat ini berfungsi sesuai yang diinginkan dan diharapkan dapat mengatasi kebisingan yang terjadi di kelas.
PENGGUNAAN MOTOR WIPER UNTUK MESIN PENGANGKUT SAMPAH SELOKAN
Authors: T Hadi Gunawan , Saputra Aji
Published: November 2024

Kejadian banjir yang melanda daerah daerah yang seharusnya menjadi daerah yang tidak mungkin terjadi banjir seperti di kota kota besar seperti Kota Batam, kerap kali diakibatkan oleh sampah yang tersangkut dalam selokan. Adanya sebab sampah dalam selokan ini maka kebutuhan mesin pengangkut sampah botol dan sampah lainya didalam selokan menjadi perlu diadakan untuk mengangkat sampah dan botol yang berada di selokan. Dalam penelitian ini lebih ditujukan untuk mengetahui kebutunan daya penggerak mesin tersebut dimana pemakaian mesin DC yang dipergunakan dalam mobil sebagai penggerak wiper menjadi pilihan untuk dipergunakan dan dikendalikan sebagai penggerak mesin ini. Metode yang dipakai dalam penelitian ini dengan melakukan Perhitungan kebutuhan akan perangkat keras dan lunak dan juga perhitungaan Putaran motor DC diteruskan ke roda pengerak mesin menggunakan rantai ditinjau dan dianalisis apakah sistem pengangkat sampah dari selokan dengan mempergunakan motor wiper ini bisa diaplikasikan, dan juga analisa data terhadap kecepatan putaran mesin dan arus terpakai dalam mesin menjadi acuan dalam perhitungan pemakaian motor wiper ini. Hasil Penelitian yang diaplikasikan dalam mesin yang menggunaan slot konveyor yang an dapat di sesuaikan dari lebar 30cm hingga 60 cm dengan menggeser base roda secara manual diperoleh hasil bahwa Motor DC yang diharapkan dan di realisasikan menyerap arus kurang lebih 8,34Ampere dan mesin mampu bergerak maju atau mundur sehingga mesin dapat maju atau mundur diatas selokan yang berkecepatan 0.0704 – 0.0759 m/s dengan tingkat keberhasilan mengangkat sampah 75.94 % serta mampu mengangkut maksimal 3 sampah botol perputaran.
PENGEMBANGAN SISTEM INFORMASI PENGELOLAHAN PRAKERIN DI SMKN 5 MAKASSAR
Authors: NFH Alifyah , Tri Wardani Ayu , Suhardi Rahman Edi
Published: November 2024

Tujuan penelitian ini adalah mengembangan sistem informasi PRAKERIN di SMKN 5 Makassar yang dapat membantu staf sekolah dalam pengeloaan PRAKERIN. Tahapan dari pengembangan sistem informasi pengelolaan PRAKERIN dilakukan dengan metode prototype dengan empat tahapan yaitu komunikasi (Communication), rancangan pemodelan (Modelling Quick Design), konstruksi (Prototype Consturction), dan penyerahan sistem dan umpan balik (Delivery and Feedback). Pengembangan sistem informasi PRAKERIN menggunakan bahasa pemrograman HTLM, PHP, JavaScript dan Bootstrap. Penelitian ini menghasilkan sistem informasi pengelolaan PRAKERIN yang memiliki dampak yang signifikan dalam mempermudah pengelolahan kegiatan PRAKERIN di SMKN 5 Makassar.
PENGEMBANGAN E-MODUL PEMBELAJARAN KESELAMATAN KERJA DAN KESEHATAN LINGKUNGAN (K3L) KOMPETENSI KEAHLIAN TEKNIK INSTALASI TENAGA LISTRIK SMK NEGERI 1 SEDAYU YOGYAKARTA
Authors: Wahrini Retyana , Makmur Elfira
Published: November 2024

Penelitian ini bertujuan untuk mengembangkan e-modul dan mengetahui kelayakan e-modul pembelajaran keselamatan kerja dan kesehatan lingkungan. Penelitian ini merupakan jenis  penelitian dengan metode pengembangan R&D (Research and Development) dengan menggunakan Langkah-langkah model pengembangan 4D yang terdiri dari define (pendefenisian), design (perencanaan), develop (pengembangan) dan disseminate (penyebaran). Hasil uji kelayakan pengembangan e-modul pembelajaran keselamatan kerja dan kesehatan lingkungan (K3L) Kompetensi Keahlian Teknik Instalasi Tenaga Listrik Di SMK N 1 Sedayu Yogyakarta yang dikembangkan dengan menggunkan teknik pengumpulan data berupa anket dan validasi dilakukan oleh ahli media dan ahli materi. Hasil validator ahli media dengan persentase 100% dan validator ahli materi dengan persentase 99% dan hasil responden oleh siswa dengan persentase 88%, Sehingga emodul K3L interaktif dengan menggunakan flip pdf builder ini dinyatakan “Sangat Layak” dan siap digunakan sebagai media pembelajaran.
PENGEMBANGAN APLIKASI PEMILIHAN PROGRAM STUDI BAGI CALON MAHASISWA PADA SMA SWASTA MUHAMMADIYAH BENTENG
Authors: Ihsan Maro Muhammad , Ardiansyah Mursyid , Ma’arief Al Imran Abdul , Astri Surya Ramadani A. , Suhardi Rahman Edi
Published: November 2024

Penelitian ini bertujuan mengembangkan aplikasi untuk pemilihan program studi melalui serangkaian tahapan, mulai dari identifikasi masalah hingga desain dan pengembangan sistem. Aplikasi tersebut memfasilitasi pengguna dalam memilih program studi dengan mempertimbangkan faktor seperti jurusan, rumpun ilmu, sub rumpun ilmu, dan bidang ilmu. Pengembangan front-end dilakukan dengan memanfaatkan teknologi HTML5, CSS, dan JavaScript, sedangkan back-end dikerjakan menggunakan PHP dan MySQLi dengan menerapkan metode Forward Chaining. Uji coba sistem dilakukan melalui pengujian Black Box dengan menguji 5 skenario yang telah ditetapkan, dan hasilnya menunjukkan kelancaran dalam fungsionalitas sistem. Aplikasi diuji oleh 20 siswa dari SMA Swasta Muhammadiyah Selayar, dengan rincian 6 berasal dari jurusan IPA dan 14 dari IPS. Dalam implementasinya, siswa memilih program studi, dan hasilnya mengungkapkan bahwa Ilmu Manajemen dan Ilmu Ekonomi menjadi pilihan terbanyak. Penelitian ini memiliki potensi sebagai referensi bagi pembaca yang ingin mengembangkan aplikasi serupa. Selain itu, konsep aplikasi ini juga dapat diterapkan dalam pengembangan berbasis Android, serta dapat diadaptasi untuk berbagai jurusan, termasuk di lingkungan SMK.
A PHENOMENOLOGICAL INVESTIGATION OF EARTHQUAKE EXPERIENCES: THE CASE OF THE FEBRUARY 6, 2023 MARAŞ EARTHQUAKE
Authors: Babahan Ali , Güler Zuhal
Published: November 2024

This study investigates the experiences of individuals who survived the Maraş earthquake on February 6, 2023, employing a phenomenological approach to examine the multifaceted effects of the disaster. Structured interviews were conducted with 16 participants (eight women and eight men) who relocated to Bolu temporarily or permanently following the earthquake. As one of Turkey's most catastrophic seismic events, it has resulted in significant physical destruction and profound psychological and social ramifications. The majority of survivors perceived the earthquake as a form of "punishment" or "warning," interpreting it through scientific, religious, and conspiratorial frameworks. They experienced intense fear, helplessness, guilt, and a sense of victimization, recounting the event as a combination of intense auditory stimuli, darkness, cold, and mortality. The earthquake significantly altered their perspective on life, leading to increased maturity, religiosity, and heightened awareness regarding the transience of existence. Social relationships were also affected by the earthquake, with some survivors withdrawing from relatives and close acquaintances, while others formed stronger interpersonal bonds. This study underscores the complex nature of the earthquake experience, emphasizing that survivors' needs extend beyond physical reconstruction to encompass psychological, social, and economic support. Limitations include the small sample size and the geographic focus on the province of Bolu. Future research should expand to larger, more diverse populations, and consider the long-term effects of such traumatic events. These findings are critical for developing effective post-earthquake interventions and support strategies that address the comprehensive needs of survivors.
DRAMATIZATION OF MORALITY AND LEGALITY IN TURKISH MEDIA: THE WORLD OF TV SERIES AND CRIME REALITY SHOWS
Authors: Erol Işık Nuran , Yaman Akyar Burcu
Published: November 2024

Understanding the representations of justice and law in different media texts requires evaluating a web of multidimensional themes and issues, and their intersections, in a relational manner. Media portrayal of the world of law-making, as well as forms of ‘doing the right thing’, vary across media genres. The representations of law are revelatory products of political and popular cultural practices which intersect in unexpected ways. Codes of morality and legality in media are two ‘worlds of evaluation’ (Dromi and Illouz, 2010) where such a complicated intersection can be observed. Due to the ways in which narratives on formal legal criteria of judgements and moral argumentation styles are intertwined, one can clearly derive a series of validity claims (Habermas) in different genres in popular culture. This paper tackles the question of moral reasoning embedded in two genres in an evaluation of the representations of legality as part of moral universe.
العلاج بالأحجار الكريمة بين النظرية والتطبيق (من منظور إسلامي)
Authors: المخلافي كوثر حمود
Published: November 2024

يهدف هذا البحث إلى التحقق من وجود العلاج بالأحجار الكريمة بأنواعها المختلفة في اليمن، وإبراز أهمية العلاج بها، وبيان أماكن تواجدها، وفوائدها العلاجية للكثير من الأمراض سواءٌ أكانت العضوية أم الروحية لمختلف المراحل العمرية، وبيان اقتصار استخدام اليمنيين لها على مجالات الزينة دون العلم بفوائدها العلاجية، وتبين وجود معالجين بهذه الأحجار الكريمة في اليمن، ولكن بصورة محدودة، وأوضح البحث أن العلاج بالأحجار الكريمة له أساس في ديننا الإسلامي، وكان له السبق في الإشارة إلى العلاج بها، كما وردت بعض الدراسات الأجنبية في هذا الموضوع.
ووضح الحكم الشرعي للعلاج بالأحجار الكريمة، وأن ما ثبت شرعاً أو علماً وبالتجربة نفعه في علاج المرض وحقق المقصد الشرعي في حفظ النفس يجوز التداوي به، بل يجب إن تعين للعلاج.     
وتوصل البحث لمجموعة من النتائج، منها أن العقيق اليمني -بكل ألوانه- من أجود أنواع العقيق في العالم، للأحجار الكريمة، وللعقيق فوائد علاجية؛ حيث يعالج المرض من أساسه وليس أعراض المرض، كما أورد البحث مجموعة من التوصيات والمقترحات التي تبرز أهمية العلاج بالأحجار الكريمة، وفتح باب الدراسة في هذا الموضوع.