bluuebunny/crossref_metadata_2025 huggingface dataset:
The Role of Sodium-Ion Batteries in Electric Vehicles: A Comparitive Study
Authors: K Savarinathan , S Sanjana , V Dinakaran , K Sivasakthi Balan
Published: November 2024

The global shift toward electric vehicles (EVs) is reshaping the future of transportation, emphasizing the need for efficient, safe, and sustainable energy storage solutions. Currently, lithium ion (Li ion) batteries dominate the EV industry due to their high energy density, relatively fast charging times, and long cycle life. However, high costs, environmental impacts, and supply chain concerns related to lithium ion batteries have spurred the search for alternatives. Sodium ion (Na ion) batteries present a promising option, offering a more abundant and less costly material—sodium—while maintaining comparable performance. This paper provides a comparative analysis of sodium ion and lithium ion batteries, assessing parameters such as energy density, cost, environmental impact, safety, and performance in electric vehicle applications. This evaluation highlights the opportunities and limitations of sodium ion technology and discusses the challenges and future development pathways for its integration in the EV market.
Review on YOLO-based Mobility Assistance Systems for the Visually Impaired
Authors: B Abhitha Bhat , S B Gaurav , Ravindra Shashank , Raghuveer Ananya , Bangera Nandita
Published: November 2024

This paper presents a machine learning based framework aimed at enhancing mobility and spatial awareness for the visually impaired by reviewing different papers where authors suggest various object detection models, particularly focusing on different versions of the YOLO algorithm, to provide real time analysis of a user’s surroundings. The reviewed systems are audio feedback integrated, converting visual data into descriptive audio cues to help users identify objects and obstacles, enabling independent navigation. The paper also reviews the use of the Darknet framework with TensorFlow, and tools such as Google Text to Speech (GTTS) and Raspberry Pi, emphasising their role in building effective, real time assistive technologies.
Cloud Memory Management with Hybrid Algorithms
Authors: S Karthika , A Kavitha
Published: November 2024

Cloud memory management is a critical aspect of efficient data storage and retrieval in cloud computing environments. Hybrid algorithms have emerged as a promising solution to optimize memory management by combining the strengths of different approaches. This abstract explores the integration of hybrid algorithms in cloud memory management to enhance performance and scalability. Hash codes play a crucial role in data integrity verification and security. Comparing the hash codes generated by different algorithms, such as SHA1 and SHA2, provides insights into their efficiency, collision resistance, and cryptographic strength. This comparison aids in selecting the most suitable algorithm for specific security requirements in cloud environments. Data security is paramount in cloud computing, especially when sensitive information is stored or transmitted. Hybrid methods like homomorphic encryption combined with Proxy Re Encryption offer a robust approach to securing data while allowing for secure computation and sharing. This abstract delves into the implementation and benefits of using hybrid methods for enhancing data security in cloud environments. By exploring the integration of hybrid algorithms in cloud memory management, comparing hash codes for SHA1 and SHA2, and leveraging hybrid methods for data security, this abstract aims to provide valuable insights into optimizing cloud computing systems for improved performance, security, and efficiency.
Empowering Farmers through Digital Innovation
Authors: G K Arathi , M Bharath Vilas , R Lalith Raj
Published: November 2024

The foundation of our nation is agriculture. In India, the majority of people 60–70% rely on agriculture for their own sustenance, either directly or indirectly. Despite being India’s primary industry, farming has both benefits and drawbacks for farmers. Many sectors have undergone radical change as a result of technology, and the agriculture sectors is no different. Typically, farmers spend the majority of their income on farming. However, they were not fairly compensated for their diligence. They only receive a smaller sum of money than what they originally invested. The internet platform shown in this abstract aims to close the gap between farmers and consumers by doing away with the need for middlemen and promoting direct communication. So, the main goal of our project is to construct a web application ‘Till the Soil’ this platform allows farmers to directly advertise and sell their products to consumers. The web portal functions as a virtual marketplace, giving farmers a specific venue to showcase their produce, such as fruits and vegetables. It is a key platform for India’s development. Personalized profiles that highlight their farmed products, sales figures, and biographical information can be created by farmers. The till the soil service itself will make sure the items are delivered safely, which will benefit both farmers and consumers.
Gesture Zoom-Touchless Navigation at your Fingertips
Authors: C Valarmathi , N Poornima , M Nanditha , R Yeshaswini , U Vinay Kumar
Published: November 2024

In this work, we introduce Gesture Zoom, a touch less navigation system that uses hand gestures to allow for intuitive visual manipulation. Users can zoom in and out of photos in real time without touching a device thanks to Gesture Zoom, which uses Open CV and computer vision technologies to read hand gestures. Fingertip distance calculation, hand movement detection and tracking, and image position locking based on user gestures are all part of the process. Gesture Zoom is perfect for industries that need contact free control, like healthcare, food service, and education, since it uses Python based Open CV and the CV zone hand tracking module to offer responsive, fluid picture scaling and repositioning. The system’s effectiveness in a range of environmental circumstances, responsiveness to scaling commands, and detection accuracy are all highlighted by experimental tests. According to our research, Gesture Zoom has potential for applications requiring cleanliness and precision, with further advancements targeted at reducing environmental sensitivity and improving gesture accuracy.
Federated Learning and Privacy Preserving Ai: A Comprehensive Overview
Authors: H M Prajwa , Kulkarni Sanjana , Y Niveditha , Y M Nisha , M Sahana
Published: November 2024

AI has gained currency in recent times, where statistical models seem to be at the forefront. Yet, there are increasing privacy and legal concerns with the conventional centralized training and inference approach to AI technologies. Federated learning (FL) comes onto the set to actually solve such issues revolving around secure distributed AI. It integrates security features at different stages of data including pre processing, training, evaluation, and deployment using secure multi party computation, and differential and hardware privacy. FL aims at addressing issues of data privacy as well as enacting measures to mitigate against data isolation. Nonetheless, federated models are yet to evade threats like plagiarism, illegal copying and misuse. In a bid to do this, FedIPR incorporates the use of watermarks onto FL models for proof of ownership and protection of intellectual property rights (IPR). Many papers that emphasize on security still misappropriate FL to refer to unsecured distributed machine learning. For this reason, this paper reemphasizes the concept of FL and suggests SFL to develop honest, privacy preserving AI systems with secure IP rights cross border guarantees. It also gives a succinct picture of threats as well as attacks and defenses applicable in each SFL phase from a life cycle perspective.
Smart Agriculture: A Deep Learning Model for Plant Disease Diagnosis
Authors: A Ahila , BL Akshay , KB Arish Balaji , KV Gagan
Published: November 2024

Accurate early stage detection of crop diseases is essential for maintaining crop quality and yield by enabling timely and effective treatments. However, disease detection often requires specialized expertise in plant pathology and significant experience. Therefore, an automated crop disease detection system is invaluable in agriculture, as it supports the development of an early warning system. To build this system, we developed a stepwise disease detection model utilizing images of diseased and healthy plants, along with a CNN based algorithm incorporating five pre trained models. The detection process involves three stages: crop classification, disease detection, and disease classification. An ‘unknown’ category was included to enhance model generalization for broader applications. In validation tests, the model achieved high accuracy in classifying crops and disease types (97.09%). Additionally, by adding non model crops to the training dataset, we improved accuracy for these crops, demonstrating the model’s scalability. This model has significant potential for application in smart farming, particularly for Solanaceae crops, and could be adapted to other crop varieties by expanding the training dataset.
BrainGate: A Breakthrough in Brain-Machine Interface Technology
Authors: S Akash , K Anush , N P Arun , K Basavaraj , S Manjunath
Published: November 2024

BrainGate is a cutting edge brain machine interface (BMI) system that enables individuals with severe motor disabilities to control external devices using their neural activity. This paper provides a comprehensive overview of the BrainGate technology, its underlying principles, technical implementation, applications, and current state of research. The paper also discusses the challenges and future directions for BrainGate and BMI technologies in general.
Design and Fabrication of an Electric Trike for Sustainable Urban Mobility
Authors: G Chirupavan , L Harish Babu
Published: November 2024

This paper presents the design, fabrication, and performance analysis of an Electric Trike (E-Trike) developed as a low cost, eco friendly transportation solution for urban environments. The E-Trike incorporates stability features, a tilting mechanism, and reused materials to enhance its sustainability profile. The paper details the specifications, design calculations, and test results, demonstrating the vehicle’s performance in terms of stability, speed, range, and suitability for sustainable urban mobility.
Bone Cancer Detection Using Deep Learning
Authors: P Sumathi , R Akhila , K N Harshitha , L Jeevitha , S N Kavana
Published: November 2024

The Bone cancer presents a significant challenge for modern medicine due to its high occurrence rate and the complex variety of malignant lesions that develop in bone tissue. Currently, available treatments are mostly non curative, focusing instead on extending patient survival and improving quality of life. However, both drug and surgical interventions are often accompanied by numerous side effects and can lead to disabilities due to the degradation of healthy bone’s structural and functional integrity. This has spurred extensive research into developing innovative biomaterials that can target and inhibit bone cancer progression while minimizing the structural loss caused by local tumor invasion. This review explores the latest advancements in biomaterials for treating bone cancer, beginning with an overview of natural bone remodeling processes, the development of bone metastasis, and osteosarcoma. It then examines recent progress in nano carrier based drug delivery systems and highlights the potential of novel, multifunctional biomaterials for bone cancer treatment. Finally, the review addresses current limitations and considers future directions for integrating these approaches into clinical practice.
Advanced Machine Learning Classification of Areca nut Variants Based on Quality and Multidimensional Parameter Analysis
Authors: T Nithya Kalyani , M Sushma , MD Shivasagar , JN Prajwal , S Keerthana
Published: November 2024

This paper presents an automated classification system for Areca nut, utilizing image processing to assess features such as texture, colour, and size. The system is built on a hardware configuration that includes an ESP32 microcontroller, a camera, and a motorized sorting mechanism. The areca nut images are captured and sent to a cloud based server via the ESP, where a machine learning algorithm processes the data to classify the areca nut based on predefined categories. Once the classification is complete, the results are relayed back to the ESP, which then activates motors to sort each areca nut item into the appropriate bin. This approach is both efficient and scalable, providing a robust solution for real time, automated sorting, crucial in agriculture processing industries. By integrating edge cloud technologies, the system achieves high classification accuracy while remaining cost effective and adaptable to various environmental conditions. The research highlights the potential of IoT and machine learning applications in agricultural automation, paving the way for enhanced productivity and reduced manual labour in areca nut sorting. Experimental results demonstrate the system’s effectiveness, showing accurate classification rates and efficient sorting in real world testing scenarios. This solution could be adapted for other agricultural products, offering a generalizable framework for IoT based, machine learning driven classification and sorting systems.
Stride to Energy using Piezoelectric Sensor
Authors: Gowda Brunda V , R Akshitha , M Shobitha , B Indupriya , KV Dhamarai Selvi
Published: November 2024

This paper explores an innovative method for electricity generation through human locomotion by employing piezoelectric technology in high traffic public areas. With rising global energy demands, particularly in densely populated countries like India, sustainable energy solutions are increasingly vital. We propose the installation of piezoelectric flooring in spaces such as train stations and temples, where significant foot traffic can be harnessed. The flooring captures kinetic energy from footsteps and converts it into electrical energy using embedded piezo transducers, which is subsequently stored in batteries for later use. This system integrates an Atmega 328 microprocessor with the Arduino IDE for effective control, an LCD display for user interaction, and an RFID module for secure energy access. Users can charge devices like mobile phones upon swiping a valid RFID tag, promoting responsible energy use. This approach not only mitigates energy waste but also fosters clean energy practices, aligning with global sustainability goals.
AI Integration in the Educational Systems of the US and India
Authors: KB Kavitha , TK Pradeep Kumar , A Suguna
Published: November 2024

Artificial Intelligence (AI) has become a ground breaking force that is changing industries all over the world and surpassing conventional bounds. Our teaching and learning methods are being drastically altered by the widespread use of technology in the educational field. With major improvements in teaching strategies, individualized learning, and general student engagement, artificial intelligence (AI) is revolutionizing the education sector. The application of AI in education in India has shown incredible promise, transformed conventional approaches, and ushered in a new era of creativity and individualized learning .The Office of Educational Technology (OET) of the U.S. Department of Education develops a national education technology policy and establishes the framework for how artificial intelligence (AI) can transform education and facilitate learning for early learners in K–12, higher education, and adult education everywhere, at any time.
Smart Resume Builder and Evaluation System
Authors: JK JayaKumari , K Raman Kishore , K I Sandeep Sai Kumar , MS Madhaneshwaran , T Nandha Kumar
Published: November 2024

Today’s fast paced hiring environment needs effective tools for sorting and managing resumes. This project introduces the “Smart Resume Builder and Evaluation System,” an automated platform that makes it easier for candidates to create and improve their resumes while helping recruiters quickly identify top candidates. Candidates can build and download professional resumes directly through the platform, which then uses rule based Natural Language Processing (NLP) to evaluate the resume based on specific criteria. Each resume receives a score and helpful suggestions for improvement, giving candidates a way to strengthen their applications. For recruiters, the system automatically highlights the top five resumes, making it easier to find qualified candidates efficiently. Overall, this project aims to streamline the hiring process, saving time for both candidates and recruiters and helping match the right candidates to the right jobs.
CODEGEN: A React-Based Online Coding Workspace with Advanced Features and Real-Time Execution
Authors: G Fathima , Kaif Mohammed , Nadeem Mohammed
Published: November 2024

This paper presents CODEGEN, an advanced React based online coding platform offering real time code execution and highly customizable user experiences. Built using ReactJS and CodeMirror, it supports multiple programming languages such as C++, Python, Java, and JavaScript. The platform integrates with Judge0 API for seamless code execution and includes robust features like user authentication, multi theme support, and file management. Unique features such as Top Coding Questions, Dashboard Analytics, and a Chatbot Helpdesk provide additional functionality, making CODEGEN a powerful tool for learners and professionals. This paper explores the platform’s architecture, innovative features, and impact on software development education.
Stock Market Price Prediction
Authors: B Pushkala , Rao Sandhya , Bhat Satwik , Madhapur Sriya
Published: November 2024

This study explores machine learning approaches for forecasting stock prices, addressing the complexities of high volatility and non linear market behaviour. Traditional forecasting methods, while useful, lack adaptability in dynamic markets. We employ three models Long Short Term Memory (LSTM) networks, Random Forest, and Linear Regression to analyze historical price patterns and incorporate sentiment analysis and economic indicators as additional predictive features. Methodology includes rigorous data pre processing, feature engineering, and model evaluation using Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) metrics. Our results indicate that LSTM achieves the highest accuracy among the models, making it a promising tool for financial forecasting. This paper contributes a comparative analysis and identifies influential factors impacting predictive performance in stock market applications.
Animal Safety and Welfare Using IOT
Authors: S Jayakumar , S Amit Reddy , P Vikay , S Kiran , CS Thilak
Published: November 2024

The Internet of Things (IoT) has revolutionized the ways we monitor and ensure the safety and welfare of animals. Focusing on pets, livestock, and wildlife, this article provides an in depth review of IoT based solutions aimed at promoting animal well being. We explore a wide range of IoT sensors and devices that track health, activity, and environmental conditions, enabling early detection of stress, illness, and potential safety risks. Additionally, IoT enabled tools for monitoring animal location, movement, and social interactions are examined. The article also discusses how machine learning and data analytics can process IoT data to inform animal care decisions. Highlighted are successful IoT applications in animal welfare, including smart veterinary care, wildlife conservation, and precision livestock management.
Iot Based Healthcare System for Paralysis Patient Using Machine Learning Algorithm
Authors: B Shadaksharappa , J A Smitha , J J Adhya , S Harshitha , R Bhavya , H S Kavana
Published: November 2024

Healthcare project for paralysis patient presents a holistic assistive technology framework catering to the communication and health monitoring needs of individuals affected by paralysis. The system consists of two main components: the Body Tilt Communication System and Smart Support Gloves, both leveraging Arduino based technologies. The Body Tilt Communication System utilizes an Arduino Uno, ADXL Accelerometer, and LCD screen to enable message display through subtle body movements, while the Smart Support Gloves, incorporating Flex Sensors and Arduino Nano, facilitate straightforward communication for those with hearing impairments. In addition to communication enhancements, the system integrates health monitoring capabilities through Pulse, SPO2, and LM35 Temperature sensors. These sensors, connected to a NodeMCU, enable real time health data transmission to a central server. Machine learning algorithm (SVC) analyze this data, predicting potential health issues. In emergencies, alerts are automatically sent to registered caretakers via IoT, ensuring prompt response and care for paralyzed individuals.This amalgamation of Arduino based technologies, IoT connectivity, and machine learning provides a versatile and comprehensive solution, addressing both communication barriers and health concerns for paralysis patients. The project strives to significantly improve the quality of life for individuals facing the complexities of paralysis by promoting efficient communication and proactive health management.
Meme Generator
Authors: Mudassir N. Mohammed , M Guruprasath , V Mounasri , D Ayush Chand , T Nithya kalyani
Published: November 2024

Cardiac arrhythmias, posing serious health risks, are diagnosed using electrocardiogram (ECG) signals, crucial for detecting irregularities. Recent advancements in signal processing and machine learning enable efficacious ECG signal analysis, improving prediction and diagnosis of arrhythmias. This paper proposes a novel approach centered on Internet of Things (IoT) schemes for long term monitoring, while also focusing on developing a robust predictive model for detecting irregular heartbeats through ECG signal analysis. The proposed system utilizes an ECG pulse sensor to acquire data, which is subsequently sent to cloud through a Node MCU module for visualization on IoT display. Simultaneously, the input ECG data undergoes a series of technical processes to enable prediction. Initially, the ECG data undergoes preprocessing and feature extraction using Fractional Fourier Transform (FrFT). FrFT extracts relevant features from the data in time frequency domain without overwhelming computational resources which is important for monitoring and analysis. Subsequently, the Improved Bat Algorithm (IBA) refines the feature set by selecting most informative features for classification. Finally, hybrid Modified Convolutional Neural Network Long Short Term Memory (MCNN LSTM) architecture accurately classifies the ECG signals, enabling timely intervention and improved patient care in cardiovascular health management. Experimental validation using Python software demonstrates the superiority of the proposed system, in predicting irregular heartbeats using ECG signal analysis.
Smart Lab Log Maintenance System
Authors: S Sreyas , S Sri Vignesh , B Ukesh , M Venkatesh , V Kalai Vani
Published: November 2024

The system introduces an efficient approach to maintaining logs in a laboratory environment, focusing on streamlined management of user access and resource usage. Utilizing face recognition technology, it automates the process of entry and exit logging by identifying users and associating them with specific systems in the lab, reducing the reliance on manual record keeping. Cameras track users in real time, capturing entry and exit times which are stored in structured Excel files. The files are organized by system number and essential user details such as “Roll number”, “Name”, “Department”, “Year”, and section are included, ensuring that records are maintained without overwriting past entries . This approach supports secure, accurate logging and enhances overall resource management in educational and research lab contexts.
Recognizing impurities of Micro Doppler Signals and determination of the target
Authors: M Amsa Lakshmi , S K Sankarshan , M Shruthi , H B Niveditha , Ansari Firoj
Published: November 2024

This paper addresses the problem of recognizing and mitigating impurities in micro Doppler signals to improve target classification accuracy. Micro Doppler signatures capture fine movements of objects, but noise, clutter, and environmental interference introduce impurities that degrade classification performance. We propose a hybrid approach combining advanced signal processing and machine learning to detect impurities, filter out interference, and accurately determine the target. Experiments demonstrate the effectiveness of our method on both synthetic and real world datasets, with improved classification performance in noisy environments.
Web 3.0: The Future of the Internet
Authors: V Yashwanth , V Nitharsan Babu , G Pavan , K Yeshwanth , Singh Sumit Kumar , T Nithya Kalyani
Published: November 2024

This paper provides a comprehensive survey of Web 3.0, the next generation of Web architecture, which is characterized by its decentralized, intelligent, and safer features. The paper begins with an overview of the evolution of the Web from its static informative characteristics in Web 1.0 to the dynamic interactive features of Web 2.0 and the promising benefits of Web 3.0. The article explores the technical implementations of Web 3.0, including distributed ledger technology, blockchain, and artificial intelligence. It discusses the potential revolutionary changes in the Internet world from economic, cultural, and technological perspectives. However, the paper also acknowledges the current challenges and issues that Web 3.0 is facing, such as its maturity and disputed status. Overall, the survey provides valuable insights into the current state, opportunities, and future outlook of Web 3.0.
Data-Driven Entrepreneur Information and Analysis for Business Opportunity Evaluation
Authors: K Karthika , S Y. Jayanth , R Munesh Kumar , Reddy Nanda
Published: November 2024

We High Situations of request query can make it challenging for entrepreneurs to estimate business openings. Gathering and assaying data through various ways and technologies is increasingly getting a vital system for managing this query in multitudinous entrepreneurial crapshoots. This approach is constantly labeled “data driven entrepreneurship.” We explore a dynamic, data driven strategy to navigate request query in the assessment of business prospects. Our focus is on each individual’s investment portfolio, where each investment not only offers desired profits but also provides certainly about a specific requested parameter for an individual business occasion. We build a data science model that assesses request data (for ex: multimedia, finance, sports etc. …) while considering the investor’s trouble appetite, functional resources, business practices, character, investment, profits, time limits and compliance limitations. Our quantitative results show that rather than simply aiming for the topmost projected returns, entrepreneurs can choose investments that offer accurate information, trouble mitigation, or request influence according to their cash vacuity and trouble forbearance. Thanks to the power of data analysis, entrepreneurs can navigate misgivings more effectively, leading to better informed opinions regarding business openings.
AI – Powered Industries of the Future
Authors: S Vanarasan , Likitha Sai , Sylesh Sakthi , Dev Adithya
Published: November 2024

This chapter explores how automation and AI are changing the manufacturing landscape. We’ll look at the major areas impacted, like transportation getting more efficient, communication becoming more intelligent, customer service getting a whole lot more personalized, and of course, the manufacturing machines themselves getting smarter and more capable. It’s a fascinating look at how technology is changing the way things are made.
Delineate & Decipher: A RAG-Powered AI Platform for Research Paper Analysis and Visual Math Problem Solving
Authors: Mallick Rohan , C Nithin Chand , R Sandeep Reddy , A Mahesh
Published: November 2024

The exponential growth in the research papers and complex mathematical problems has posed serious challenges in information retrieval and its review for analysis. This paper presents “Delineate and Decipher,” a platform powered by AI using Retrieval Augmented Generation models to solve these challenges. The platform will combine natural language processing with machine learning techniques to provide context based answers from research papers and also solve visual math problems through its interactive interface. A hybrid system was based on the Gemini model of visual problem solving in mathematics, whereas the retrieval models were powered with GROQ. The platform embeds both documents and mathematical equations into vector spaces, hence guaranteeing efficiency in document searching and retrieval. Precisely, all this necessary information has kept in mind while making the system function over huge datasets of research papers by splitting documents into smaller chunks with strong results based on accurate/context retrieval. It also allows the user to write mathematical equations visually and get solutions to them using real time API with Gemini Flash, hence not just a tool for doing research analysis but also educational purposes. This work saves time for the researchers and students who search for some information and solutions of cumbersome mathematics problems. The proposed platform is powered by state of the art AI techniques: FAISS for vector storage and Google Generative AI embeddings for document representation. It provides better access, precision, and interactivity to academic research and education. Future versions might involve even better language models and more comprehensive datasets, which have the interactions between users and scholarly materials and mathematical tools
Cost-Effective and Reliable Arduino-Based Gas Leakage Detection System
Authors: Shubraja J. Hema , N J. Pushpa , Badgeri Nagashree Ira , Y Rohini
Published: November 2024

Gas leaks pose a significant threat to human life, property, and the environment. Existing gas detection systems are often expensive, complex, and require frequent maintenance. This project addresses these limitations by developing a cost effective gas leakage detection system using Arduino, a popular open source microcontroller platform. The system integrates gas sensors, Arduino, and actuators to detect gas leaks and trigger an alert. Upon detection, the system automatically opens windows to ventilate the area, reducing the risk of explosion and ensuring a safe environment. The proposed system offers several advantages, including Affordability: Low cost components and minimal infrastructure requirements , Ease of installation: Simple setup and minimal wiring , Maintenance free: No frequent calibration or replacement of parts , Automation: Windows open automatically in case of a gas leak, ensuring prompt ventilation. Experimental results demonstrate the system’s effectiveness in detecting gas leaks and responding promptly, highlighting its potential to save lives and prevent property damage. This project has significant implications for widespread adoption in residential, industrial, and commercial settings, providing a reliable and cost effective solution for gas leakage detection and prevention.
Voice Controlled Wheel Chair for Disabled Persons
Authors: LN Dhanalakshmi , P Pallavi , adnan Syed , kumar Vemala yashwanth , G Vinutha
Published: November 2024

This paper describes intelligent voice controlled wheelchair which operates on user’s voice commands. The disabled people cannot move from one place to another on their own. They continuously need someone to help them in getting the wheelchair moving. This voice controlled system makes them more independent. A voice controlled wheelchair can provide easy access for physical disabled person who cannot control their movements especially by hands. This voice controlled wheelchair helps them to drive the wheelchair without anyone’s help. This system can be controlled by the simple voice commands given by the user. Depending upon the direction specified in the commands, the Arduino will drive the motors. The speech recognition is done by voice recognition module, connected with Arduino. The principle of the developed wheelchair consists of motor system, voice recognition module that would be controlled by the microcontroller. The automatic obstacle detection system is included to the developed wheelchair by using ultrasonic sensors in order to break the developed wheelchair immediately when any obstacles suddenly come in the way of the developed wheelchair. Therefore, the developed voice controlled wheelchair can provide easy access for people with physical disability and also offer automatic protection from obstacle collision if the mistake of any voice commands occurs.
An Efficient Cervical Cancer Prediction Using Chicken Swarm Optimized ANN Classifier
Authors: S Karthick
Published: November 2024

Cervical cancer remains a significant global health challenge, ranking as a leading cause of cancer related deaths among women. This research addresses the need for improved detection methods by leveraging advanced image processing techniques. Implement Adaptive Median Filtering (AMF) for effective noise reduction while preserving critical details in cervical images, enhancing overall image quality. Utilizing K means clustering for segmentation, accurately isolate regions of interest, and Local Binary Patterns (LBP) for feature extraction enable improved differentiation between normal and cancerous tissues. Additionally, the integration of the Chicken Swarm Optimized algorithm with Artificial Neural Networks (CSOANN) significantly enhances classification accuracy, facilitating earlier detection of cervical cancer. These methodologies not only improve diagnostic accuracy but also contribute to better patient outcomes and more effective screening strategies, ultimately advancing cervical cancer management.
Enhancing Breast Cancer detection accuracy using U Net architecture
Authors: C Valarmathi , A Akshatha , A Chandana , L Hitha , L Keerthana
Published: November 2024

The U Net deep learning architecture is used in this study’s ultrasound image based breast cancer detection system to achieve accurate picture segmentation. The first step in the process is picture collecting and pre processing, where methods like data augmentation, normalization, and noise reduction improve image quality and increase the generalizability of the model. The encoder decoder structure and skip connections of the U Net architecture allow for the precise and effective localization of malignant areas. The U Net model provides high precision segmentation by reducing false positives and collecting contextual information as well as fine characteristics. A dice loss function is used to optimize the system, guaranteeing strong model performance in locating and classifying breast cancer areas. The U Net model’s promise for clinical applications in breast cancer detection is highlighted by experimental results that demonstrate its ability to detect and segment malignant regions in ultrasound images with a decrease in training loss and an improvement in accuracy with time.
Transformasi Nilai-Nilai Keagamaan di Kalangan Pelaut: Studi Kasus Penggunaan Kitab Suci dalam Kehidupan Sehari-Hari
Authors: Sumartono Eddy
Published: November 2024

Penelitian ini mengeksplorasi transformasi nilai-nilai keagamaan di kalangan pelaut dengan fokus pada penggunaan kitab suci dalam kehidupan sehari-hari. Menggunakan pendekatan kualitatif dengan desain studi kasus, penelitian ini melibatkan 15 partisipan pelaut yang dipilih melalui purposive sampling. Data dikumpulkan melalui wawancara mendalam, observasi partisipatif, dan dokumentasi, kemudian dianalisis menggunakan pendekatan tematik. Hasil penelitian menunjukkan bahwa transformasi nilai-nilai keagamaan di lingkungan maritim merupakan proses yang kompleks dan multidimensional. Penggunaan kitab suci menunjukkan pola yang bervariasi, dengan mayoritas partisipan menunjukkan intensitas penggunaan yang tinggi sebagai mekanisme adaptasi spiritual. Perubahan perilaku teridentifikasi pada level kognitif, afektif, dan behavioral, dengan peningkatan signifikan dalam aspek harmoni sosial dan profesionalisme. Pengaruh budaya maritim menciptakan sintesis unik antara tradisi kelautan dan praktik keagamaan, menghasilkan bentuk spiritualitas yang adaptif dan kontekstual. Penelitian ini berkontribusi pada pemahaman tentang dinamika transformasi spiritual dalam konteks maritim dan memberikan implikasi praktis bagi pengembangan program dukungan spiritual bagi pelaut.
الآثار السلبية الاجتماعية والثقافية والتعليمية لاستخدام المواقع الإلكترونية من وجهة نظر طلبة الجامعات اليمنية
Authors: دودة نجاد عبد الحميد حميد , حيدر فاروق أحمد , الأهدل طاهر محمد
Published: November 2024

هدف البحث إلى معرفة الآثار السلبية الاجتماعية والثقافية والتعليمية لاستخدام المواقع الإلكترونية من وجهة نظر طلبة الجامعات اليمنية، ولتحقيق أهداف البحث استخدم الباحثون المنهج الوصفي المسحي، وطبقت أداة الاستبانة على عينة مكونة من (379) طالبًا وطالبة من المستويين الأول والرابع في خمس جامعات يمنية، اختيرت بطريقة العينة المتاحة، واستخدم الباحثون الرزمة الإحصائية للعلوم الاجتماعية (spss) لتحليل البيانات، وأظهرت نتائج البحث: أن الآثار السلبية لاستخدام المواقع الإلكترونية جاءت بدرجة كبيرة بمتوسط حسابي (3.87)، وانحراف معياري (.535)، كما أظهرت النتائج عدم وجود فروق دالة إحصائية تعزى إلى المتغيرات: (الجنس-المستوى الدراسي-نوع الجامعة)، في الأداة ككل، وأظهرت وجود فروق تعزى إلى متغير الجنس في مجال الآثار السلبية التعليمية للمواقع الإلكترونية لصالح الذكور.
استكشاف الصعوبات النفسية والإدراكية التي يواجهها طلاب المرحلة الجامعية اليمنيين أثناء أداء مهام الترجمة الفورية
Authors: خشافه محمد عبده ناجي علي
Published: November 2024

هدفت هذه الدراسة إلى التحقق من الصعوبات النفسية والإدراكية التي يواجهها الطلاب اليمنيون في المرحلة الجامعية أثناء جلسات الترجمة الفورية. وقد تم ملاحظة بيانات 30 طالبًا يمنيًا متدربًا في فصول الترجمة الفورية بقسم اللغة الإنجليزية والترجمة بجامعة القلم للعلوم الإنسانية والتطبيقية بمدينة إب، اليمن وكذا الجامعة الوطنية، فرع إب، اليمن وتسجيلها وجمعها وتحليلها كمياً ووصفيًا ونوعيًا في إطار مناهج ونماذج الترجمة الفورية التي تمت مناقشتها في هذه الدراسة. وكشفت الدراسة الحالية عن أبرز الصعوبات النفسية والإدراكية التي يواجهها الطلاب أثناء قيامهم بمهام الترجمة الفورية وهي صعوبات في الفهم والاستماع والذاكرة وإنتاج اللغة وسرعة المعالجة وإدارة الانتباه والقلق والتوتر وإدارة العبء المعرفي والتنظيم الذاتي وحل المشكلات والدافعية والإصرار. وأوصت الدراسة ببعض الممارسات والتدخلات التعليمية والآليات الداعمة لتحسين أداء الطلاب في الترجمة الفورية. وأخيرًا، اختتمت الدراسة ببعض المقترحات لبحوث مستقبلية تهدف إلى المساهمة في تطوير برامج تدريب المترجمين الفوريين وفهم العمليات النفسية والإدراكية أثناء عملية الترجمة الفورية.
موقع أبوآن (قصر الصبايا) دراسة أثرية
Authors: الحياني فواز حسن عامر
Published: November 2024

يهدف البحث الحالي إلى إزالة الغموض عن موقع أثري يعد أحد أهم المواقع اليمنية القديمة عرف قديما بـ (أبوآن) ويطلق عليه اليوم (قصر الصبايا) يقع جنوب شرق صنعاء ويتبع إداريا محافظة ذمار، وكغيره من المواقع الأثرية القديمة فإن هذا الموقع يعاني في وقتنا الحاضر من عدم الاهتمام من قبل الجهات ذات العلاقة، ولم يحظ بدراسة علمية، إلا من مسح أثري عابر للبعثة العراقية، وذكر مقتضب ضمن دراسة روبان لمنطقة ذمري، لا تقارن بما للموقع من إرث أثري كبير، ولذلك وثقت الدراسة ما تبقى من معالم أثرية في الموقع كونها لا تزال ماثلة للعيان، وقد اعتمد البحث على المنهج الأثري القائم على الوصف والتحليل والمقارنة، بغرض التعرف على بقايا المنشآت المعمارية للموقع وتخطيطها وتوضيح منشآتها وتحديد وظائفها المدنية كالقصر والعسكرية كالسور والأبراج والمائية كالبرك، وتناول الإطار التاريخي والجغرافي للموقع، وتكون البحث من مقدمة وقسم جغرافي تأريخي، وقسم خصص للدراسة الأثرية ووصف الموقع والمنشآت المعمارية المدنية والعسكرية والمائية فيه.   
  وتوصل البحث إلى أن موقع أبوآن كان يقع ضمن أراضي اتحاد ذمري في الجانب الجنوبي التابع لقبيلة قشم وأقيالها بني ذرانح، شيده ملوك وأقيال حمير، ويحتوي على قصر وأسوار ومنشآت مائية، ويبدو أنه كان مصيفاً خاصا بإقامة الملوك، وبجواره منطقة (خضيرة أبوآن) التي مازالت تحتفظ بالاسم القديم للموقع إلى اليوم.
الأدلة الأثرية على وجود الإنسان الأول في اليمن في العصر الحجري القديم
Authors: العلي محمد أحمد أحمد
Published: November 2024

تبحث هذه الدراسة عن عبور الإنسان الأول، وطبيعة حياته المعيشية والأدلة الأثرية؛ إذ كان للموقع الجغرافي والبيئي دورًا بارزًا في ذلك، وقد شكلت اليمن حلقة وصل بين قارتين إفريقيا وآسيا، مما أدى إلى بدء حركة الاستيطان البشري الأول لليمن قبل حوالي مليون وأربعمائة ألف سنة.
 وتعود أقدم آثار النشاط البشري في اليمن إلى العصر الحجري القديم الأدنى، والعصر الحجري القديم الأوسط، فيما وجدت آثار العصر الحجري القديم الأعلى، والعصر الحجري القديم اللاحق، المعروف في بلاد الشام بالعصر الحجري الوسيط، بشكل محدود على الرغم من صعوبة العوامل الطبيعية والبشرية، إضافة إلى محدودية البحث عن تلك المدة بشكل موسع، وقد اتبع الباحث المنهج التحليلي والمقارن لتلك الأدلة الثقافية التي أكدت على أن اليمن كانت موطنًا للإنسان الأول، وعن وجود تسلسل، وتطور ثقافي لتلك العصور.
استنباط الأحكام الفقهية من الآيات (دراسة تطبيقية لبعض أحكام الطلاق في تفسير البحر المحيط)
Authors: العيدروس عائشة محمد حسن
Published: November 2024

يهدف البحث إلى التعرف إلى طرق استنباط الأحكام الفقهية من الآيات القرآنية، وعلاقة ذلك بالإعراب، وتوضيح هذه العلاقة بدراسة تطبيقية لآيتين من آيات الأحكام، لإبراز دور النحو والإعراب في فهم آيات القرآن الكريم، وقد اتبعت الباحثة المنهج الوصفي التحليلي.
إن القرآن الكريم هو المرجع الأول لكل العلوم، ومن العلوم التي تُستنبط من القرآن الكريم: الأحكام الفقهية، فهو الدليل الأول للتشريع واستنباط الأحكام الفقهية، والآيات التي تُستنبط منها الأحكام قد تكون صريحة في ذكر الحكم، أو تحتاج إلى إعمال النظر والفكر فيها، ولأن اللغة العربية هي لغة القرآن فكان لابد للمجتهد والفقيه أن يكون عالمًا باللغة والإعراب، وذلك أن اللغة سبيل لاستنباط المعنى المقصود من الآيات.
وقد وجدت الباحثة العلاقة الوثيقة بين اختلاف الفقهاء في استنباط الأحكام واللغة والنحو، فكان النحو أحد طرق استنباط الأحكام من النص القرآني، واختلاف الإعراب وتعدد أوجهه سبب من أسباب اختلاف الأحكام المستنبطة من الآيات؛ فدرست الباحثة آيتين متعلقة بأحكام الطلاق من كتاب البحر المحيط لأبي حيان الأندلسي، ومن كتب الفقه للمذاهب الفقهية، وبيّنت العلاقة بين استنباط الحكم الفقهي عند الفقهاء واعتمادهم على اللغة وقواعدها فيه، ثم خرجت بنتائج منها: أن قواعد اللغة والنحو خدمت القرآن الكريم وساعدت على فهمه، وأن العلاقة بين الإعراب والأحكام الفقهية هي علاقة وثيقة، فالإعراب يساعد على فهم نصوص الشريعة واستنباط مدلولاتها.
اضطراب ضغوط ما بعد الصدمة لدى عينة من طلبة جامعة صنعاء
Authors: العزي اروى احمد عبده
Published: November 2024

هدفت الدراسة إلى التعرف على انتشار أحداث الحياة الصدمية وطبيعة مستوى اضطراب ما بعد الصدمة لدى عينة من طلبة جامعة صنعاء، وإلى التعرف على الفروق في اضطراب ضغوط ما بعد الصدمة، تبعاً لمتغير تكرار الأحداث الصدمية ومتغير الجنس، واستخدمت الباحثة المنهج الوصفي، وتكونت العينة من (555) طالباً وطالبة من كلية الآداب والعلوم الإنسانية، وكلية العلوم اختارتهم الباحثة بالطريقة العشوائية الطبقية، واستخدمت الباحثة قائمة مراجعة أحداث الحياة وقائمة مراجعة اضطراب ضغوط ما بعد الصدمة، وأظهرت النتائج أن حدثًا مثل "العيش في منطقة حرب حدثت بشكل مباشر لـــ (53.5%)، وأشار (94.4%) من العينة تعرضهم لأحداث الحياة الصادمة، ومنهم (81.6%) تعرضوا لحدث صدمي بشكل مباشر، وأظهرت النتائج أن(48.1%) من العينة كانوا في المستوى المتوسط لاضطراب ضغوط ما بعد الصدمة، وكان (29.6%) في المستوى المرتفع، و(4.6%) في المستوى الشديد، كما أظهرت النتائج أنه توجد فروق دالة إحصائيًّا على قائمة اضطراب ضغوط ما بعد الصدمة تبعاً لمتغير تكرار الحدث الصدمي، لصالح الطلبة الذين خبروا (3-وأكثر) من حدث صدمي في الدرجة الكلية والمجالات الفرعية باستثناء مجال التجنب، بينما لا توجد فروق دالة إحصائيًّا في قائمة اضطراب ضغوط ما بعد الصدمة الكلية، والمجالات الفرعية تبعاً لمتغير الجنس.
وثائق نقشية جديدة لعقود زواج مَعينيين من أجنبيات دراسة في مدلولاتها التاريخية والحضارية
Authors: العامري معمر محمد عبدالواحد
Published: November 2024

يتناول البحث ثلاثة وأربعين وثيقة قانونية جديدة تم تدوينها بخط المسند على أربع واجهات لمسلة من الحجر الرملي، توثق زواج عدد 43 شخصاً من تُجار مملكة معين من نساء أجنبيات من مناطق وممالك أخرى من العالم القديم، تقع بعضها ضمن النطاق الجغرافي لشبه الجزيرة العربية أو مجاورة لها، وبعضها الآخر تقع ضمن نطاق دول وممالك الشرق الأدنى القديم ومناطق البحر الأبيض المتوسط. هذه الوثائق التي سيتم دراسة مدلولاتها الحضارية وأهميتها التاريخية خلال الفترة ما بين القرن الخامس - الثاني ق.م. وهي الفترة التاريخية التي تعود إليها تلك الوثائق؛ نظراً لما تقدمه من صورة فريدة للجانب الحضاري بمفهومه (التاريخي والاجتماعي والاقتصادي والسياسي) في المنطقة، بالإضافة لما تلقيه من ضوء جديد على الخريطة الإقليمية في ذلك الوقت.
دور القاعدة الإعرابية في إبراز المعنى في القرآن الكريم، سورة يوسف أنموذجًا
Authors: عبدالله أكرم ناصر ناصر حسين عبدالله
Published: November 2024

يهدف هذا البحث إلى إبراز المعنى عن طريق القاعدة الإعرابية في سورة يوسف، ويظهر كيف يختلف إبراز المعنى من وجه إعرابي إلى آخر، وقد استخدم البحث المنهجين الوصفي التحليلي؛ لمناسبة هذا المنهج لمثل هذه الدراسات، وتتمثل أهمية البحث في أنه يدرس النص القرآني؛ لإبراز المعنى بواسطة القاعدة الإعرابية، وكان سبب اختيارنا لهذا العنوان لما يتوافر في سورة يوسف من أحداث متعددة ومتنقلة، دفعتنا إلى دراسة المعنى عن طريق القاعدة النحوية، ويتمثل حدود البحث في القرآن الكريم؛ فقد وقفت على الآيات في سورة يوسف وانتخبت منها الآيات التي سأدرسها، ثم عرجت على كتب إعراب القرآن ومعانيه وتفسيره، وبعدها كتب النحو حين تقتضي الضرورة، وانقسم البحث على ثلاثة مباحث، تسبقها مقدمة وتلحقها خاتمة البحث، المبحث الأول: مفهوم الإعراب، والمبحث الثاني: دور المنصوبات في إبراز المعنى، والمبحث الثالث: دور قضايا متنوعة في إبراز المعنى، وتوصل البحث إلى نتائج، أهما: أن الإعراب هو الإبانة عن المعاني المراد إيصالها بالألفاظ، أن الإعراب فرع المعنى، أبرز البحث أن الإعراب من العلوم الجليلة للعربية؛ لمعرفة الفارق بين المعاني المتكافئة في اللفظ، وبه يُعرف الخبر الذي هو أصل الكلام، أن للإعراب فوائد، أهمها إبراز المعنى وإزالة اللبس والسعة في التعبير، أظهر البحث أن الدارسين قديمًا وحديثًا أقروا أن الإعراب لا يقتصر على اللفظ وحسب، بل هو دلائل المعاني، بيَّن البحث في سورة يوسف أن المعنى يختلف ظهورًا وإبانة ودلالة من تخريج إعرابي إلى آخر.
The Prominent Characteristics, Value Implications, and Enhancement Paths of the Historical Consciousness of the CPC from Three Historical Resolutions
Authors: Zhang Lijuan
Published: November 2024

三个历史决议是在重大历史关头形成的纲领性文献，是中国共产党历史自觉的鲜明写照，诠释了历史自觉的突出表征，即自觉对党的若干重要历史问题进行客观评析、自觉对党的正反两方面经验进行深刻总结以及自觉围绕中华民族伟大复兴进行前景规划。历史自觉是中国共产党内在的重要特质，对于中国共产党自我革命的坚持、马克思主义中国化时代化的推进以及中华民族伟大复兴的实现，都有着重要的价值意蕴。在新征程上，中国共产党必须按照“历史观-历史认知-历史主动”的逻辑，通过深入学习马克思主义唯物史观、大力提升党史学习教育实效以及始终牢记初心使命的具体路径，进一步增强历史自觉，坚定历史自信，争取更伟大的荣光。
Research on Government Responsibility in the Construction of County-Level Educational Alliances in Hubei Province from the Perspective of Public Management Theory
Authors: Wei Yaoyang
Published: November 2024

本文基于公共管理理论视角，探讨了县域教联体建设中政府的责任问题。教育公平是社会公平正义的重要体现，但我国区域间、城乡间教育差距依然较大。县域教联体建设旨在通过资源整合和共享，推进县域教育优质均衡发展。文章首先分析了县域教联体建设的内涵特征及其公共产品属性和外部性，论证了政府承担主体责任的必然性。接着，文章阐述了县域教联体建设的现实需求，政府主导作用的必要性和紧迫性，并借鉴公共治理理论探讨了多中心治理路径。通过对县域教联体建设实践经验的总结，揭示了存在的问题和症结，反思了政府履职中的不足。最后，文章提出了完善县域教联体建设中政府履责的对策建议，包括更新观念、健全政策、规范流程、整合资源、完善考核机制和健全监管机制等，以期为推进区域教育优质均衡发展提供决策参考。
A Brief Analysis of the Concept of “Religious Secularization” in the Chinese Context: Taking the Pure Land Belief as an Example
Authors: Huang Wentao
Published: November 2024

西方学界最早提出“宗教世俗化”概念，用以描述现代化进程下，宗教在整体社会结构中的地位变动情况。西方语境中的“宗教世俗化”概念侧重于形容宗教领域与世俗领域的脱钩，突出宗教与世俗生活的分离以及宗教社会影响力的衰弱。然而“宗教世俗化”在不同文明的语境中，理解侧重点不一样。在中国语境下，对于“宗教世俗化”的理解并非建立在描述宗教与世俗相分离的基础上，不少相关领域研究者更倾向于将宗教向世俗层面的下沉过程描述为“宗教世俗化”。通过将宗教教义的通俗化，宗教仪轨的简约化，信众群体结构的大众化等趋势视作宗教领域的“世俗化”，中国研究者构建了属于自己的“宗教世俗化”概念并应用于具体研究当中。本文将以净土信仰的流变为例，通过分析相关领域研究者笔下，净土信仰在宗教教义、宗教仪轨、信众群体的发展趋向，展现出中西方语境中“宗教世俗化”概念的不同。