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A Machine Learning Model: Student Verification and Job Placement with Gradient Boosting Machines 15

A Machine Learning Model: Student Verification and Job Place...

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15th international conference on advances in computing, Control, and Telecommunication technologies, ACT 2024

作者： Sujatha, E. Sakthivelan, R.G. Subha, S. Sree Gopirajan, P.V. Thandalam Chennai India Department of Computer Science and Engineering Gopalan College of Engineering and Management Bengaluru India Chennai India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Campus Chennai603203 India

ISBN: (纸本)9798331300579

Every year, millions of students graduate, making it difficult to track and process their data. The One Point Student Verification Machine learning Model (OSVMLM) collects rich data on students, including biometric information, secondary education details, list of degrees completed, and detailed information on their last or current degree. In addition, the student's fingerprint is also collected and stored. The government's Aadhar database is used to verify the authenticity of the student. To ease the recruiter, the details collected by OSVMLM were assigned with ranks, information will be sent to the candidates autonomously based on the current job description. This makes it easy for students to identify and apply for available positions. OSVMLM application is built on Ionic, MongoDB, NodeJS and Gradient boosting machines (GBMs). OSVMLM is immensely useful for recruiters, as it makes data collection and processing effortless. This increases the visibility of various jobs open in different domains and suggests suitable job description to the job seekers. The key difference between OSVMLM and other platforms is that it is not limited to institutions and is accessible to all. This saves a lot of time for recruiters during initial screening. This proposed OSVMLM was validated with the real-time students dataset and achieved 91% prediction accuracy. © Grenze Scientific Society, 2024.

关键词： Students

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Educational Record Sharing Private and Securely 15

Educational Record Sharing Private and Securely

引用

15th international conference on advances in computing, Control, and Telecommunication technologies, ACT 2024

作者： Sujatha, E. Sivakumar, A. Department of Computer Science and Engineering Saveetha Engineering College Tamil Nadu Chennai India

ISBN: (纸本)9798331300579

In order to facilitate the exchange of academic data across educational institutions, this study presents a state-of-the-art platform for sharing educational records. The platform ensures the safety and integrity of student records by utilizing secure and interoperable technologies to enable smooth data transfer. With a focus on intuitive user interfaces, the system promotes effective cooperation between colleges, universities, and other educational institutions. Standardized data formats to improve interoperability, strong encryption methods to protect private data, and real-time updating features to provide immediate access to the most recent academic records are important characteristics. The technology offers thorough insights into students' academic trajectories, which fosters openness, lowers administrative costs, and speeds up decision-making processes. Additionally, the study investigates how might be integrated for unchangeable record keeping, creating a system that is impenetrable to tampering and ensures the legitimacy of data. The proposed platform also addresses privacy concerns through granular access controls, empowering institutions to selectively share information while adhering to data protection regulations. Through extensive usability testing, the platform demonstrates its effectiveness in reducing bureaucratic barriers and fostering a collaborative educational environment. The findings suggest that implementing such a system not only enhances administrative efficiency but also contributes to a more holistic and data-driven approach to education management. Overall, this research offers a forward-looking solution to revolutionize educational record sharing for the benefit of students, educators, and institutions alike. © Grenze Scientific Society, 2024.

关键词： data integrity

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Parameter findings in Healthcare Analysis using K-means and Decision Tree Algorithm 15

Parameter findings in Healthcare Analysis using K-means and ...

引用

15th international conference on advances in computing, Control, and Telecommunication technologies, ACT 2024

作者： Hakkaladavar, Divya S. Khanai, Rajashri Department of Computer Science Engineering Dr. M. S. Sheshgiri College of Engineering and Technology Belgaum India Department of Computer Science Engineering KLE Dr. M. S. Sheshgiri College of Engineering and Technology Belgaum India

ISBN: (纸本)9798331300579

Our heart is one of the most fundamental or significant parts of our body. The heart is responsible for maintaining blood flow and forming it. There are thousands of cases on the planet connected to heart disease. A growing number of people are committing suicide because of coronary illness. Different side effects like chest pain, fast heartbeat, etc are referenced. The medical industry tracks down information. This paper discusses anticipating coronary illness using computer-aided calculations. Here, we will utilize different AI calculations like a help vector classifier, K-means, and D-tree algorithms. Calculations are used based on highlights and to anticipate coronary illness. By applying data mining techniques, it takes less time to predict disease. Based on the highlights, computations forecast cardiac illness. Using data mining techniques like D-tree and K-means algorithms speeds up and enhances disease prediction precision. © Grenze Scientific Society, 2024.

关键词： Heart

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data Security using Audio-Video Steganography 14

Data Security using Audio-Video Steganography

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14th international conference on advances in computing, Control, and Telecommunication technologies, ACT 2023

作者： Bhoomika, S. Bhoomika, C.B. Monisha, P. Sharath, M.N. Computer Science and Engineering Rajeev Institute of Technology Hassan India

As a result of the internet's role in the global expansion of digital technology, data security has emerged as a basic requirement for all people. A system that uses these two techniques for audio files is proposed in order to meet the demand for a highly secure data transmission method that combines cryptography and steganography. A reliable communication system is built to guarantee network availability, secrecy, and integrity and the protection of data from nefarious attacks. The DES algorithm is used to create cryptography which hides a message so that it cannot be seen, as opposed to steganography, which uses the LSB method to scramble a message so that it cannot be comprehended. © Grenze Scientific Society, 2023.

关键词： Cryptography

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Augmented Reality for Next Generation Connected and Automated Vehicles 5

Augmented Reality for Next Generation Connected and Automate...

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5th IEEE international conference on computing, Power, and Communication technologies, IC2PCT 2024

作者： Sharma, Sachin Bhadula, Shuchi Juyal, Amit Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttarakhand Dehradun India Graphic Era Hill University Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350383522

Augmented Reality (AR) may be an innovation that upgrades real-world situations with computer-generated substance. AR for Another Era Associated and Mechanized Vehicles includes joining AR advances into the plan and operation of vehicles. This paper points to supply drivers with data and interaction in a more instinctive and intuitively way, and to upgrade the driving encounter in associated and computerized vehicles. This incorporates utilizing AR for route, vehicle control, infotainment, and support. Also, AR seem offer assistance move forward street security by giving real-time data to drivers and upgrading their situational mindfulness. The integration of AR in next-gen vehicles has the potential to revolutionize the way we drive and associated with vehicles, making the encounter more secure, more helpful, and more agreeable. © 2024 IEEE.

关键词： Augmented Reality Connected and Automated Vehicles Next Generation

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LSB based Variable Capacity data Hiding Approach and Enhanced AES Security with Seam Carving 14

LSB based Variable Capacity Data Hiding Approach and Enhance...

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14th international conference on advances in computing, Control, and Telecommunication technologies, ACT 2023

作者： Singh, Juhi Singla, Mukesh Department of computer Science and Engineering BMU Haryana Rohtak India

Steganography is a technology in ‘information hiding’ that allows us to hide a message (text, document, image, audio, video, etc.) in an image or *** technique is very simple and popular data hiding method in which cover image holds on bit secret data in each pixel. So no of data bit in cover image depends on no of pixels in cover image. In this study we are going to discuss that the capacity of the cover image can be increased via incorporating two mathematical tools in it. One is compressing secret message using DCT compression and second using Seam Carving for increasing the resolution of the cover image. © Grenze Scientific Society, 2023.

关键词： Steganography

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Forecasting Medical Insurance Costs: A data-Driven Approach 15

Forecasting Medical Insurance Costs: A Data-Driven Approach

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15th international conference on advances in computing, Control, and Telecommunication technologies, ACT 2024

作者： Patil, Chandrashekhar H. Garud, Ashish Sonawane, Ajinkya Gavhane, Akash Jadhav, Mayur Dr. Vishwanath Karad MIT World Peace University Department of Computer Science and Application Pune India Maharashtra Pune412105 India

ISBN: (纸本)9798331300579

This project's primary goal is to forecast medical insurance costs using SVM, XGBoost, Random Forest, and Linear Regression machine learning algorithms. The process involves creating a dataset from scratch that is similar to real health data, preparing it, applying various algorithms to it, and evaluating its performance. The Target variable in the dataset is medical insurance expenses, while other parameters include age, BMI, children, gender, smoking habits, region, blood pressure, cholesterol levels, and diabetes. Min-Max scaling is used to normalize numerical characteristics, whereas one-hot encoding is used for categorical variables. To optimize performance, the models are trained using the pre-processed data. Model evaluation uses evaluation metrics like R-squared and Mean Squared Error (MSE). Cross-validation assures consistency in model prediction. The results contain data extracted from best-fit lines illustrating the predictions generated by every program. The program provides an in-depth review of medical insurance cost forecasts for the benefit of healthcare and insurance decision-makers. © Grenze Scientific Society, 2024.

关键词： BMI Healthcare Machine Learning Medical insurance

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Automated Product Categorization and Sales Forecasting: A Machine Learning Approach for Retail Analytics 15

Automated Product Categorization and Sales Forecasting: A Ma...

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15th international conference on advances in computing, Control, and Telecommunication technologies, ACT 2024

作者： Harshini, G. Reddy, T. Thrisha Prathima, T. Sirisha, A. Department of Artificial Intelligence and Data Science Chaitanya Bharathi Institute of Technology Telangana Hyderabad India Department of Information Technology Chaitanya Bharathi Institute of Technology Telangana Hyderabad India

ISBN: (纸本)9798331300579

Due to new technologies, sales forecasting has become increasingly popular as a way to improve market operations and productivity in the retail industry. Although the business has historically concentrated on a stand-ard statistical model, machine-learning techniques have garnered increased attention in recent years. A key com-ponent of retail is store sales. The most trustworthy models are scrutinized by administrators in order to help fore-cast future sales. Making decisions based on historical and present data might be facilitated by forecasting future variations or increases in store sales. By recognizing sales patterns and trends, accurate forecasting will help busi-nesses or retailers increase profitability and enhance the customer experience. Predicting retail store sales with deep learning and machine learning (ML) approaches yields high. This study summarizes ML techniques to identify the best algorithm for predicting retail sales based on a collection of prior research articles. This work detects the most influencing attributes that affect sales, and suitable ML algorithms for sales forecasting. © Grenze Scientific Society, 2024.

关键词： Random forests

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Emotion Detection from Text Using ML Framework

Emotion Detection from Text Using ML Framework

引用

2024 international conference on advances in Modern Age technologies for Health and Engineering science, AMATHE 2024

作者： Jasvitha, Bommisetty Durga Kanagaraj, Kanaganandini Murali, Keerthana Venugopalan, Manju Department Of Computer Science And Engineering Amrita School Of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350371567

With technology improving at astonishing strides, most people have access to Internet for communication via text, image, audio and video. Emotions are expressed through words, gestures, expressions and in today's world through tweets and comments. With the applications tending to be more user personalised this data needs to be processed in real time. Their behaviour needs to be analysed by their emotions. The dataset is sourced from Kaggle where the data has been streamed from X (formerly known as Twitter). This data is first converted into a vectorised form by the TF-IDF algorithm and then supervised classification techniques such as such as K-means, Naive Bayes, SVM and Ensemble Learning Techniques like Gradient Boosting, Random Forest, Decision Tree, XG Boost, Cat Boost and deep learning network named Multi-Layer Perceptron is used to determine and correctly classify the data into one of the 6 labels: sad, joy, love, anger, fear and surprise. The data balancing technique SMOTE is applied to enhance the performance of classifiers. MLP classifier was able to achieve the best results followed by the tree-based classifiers Random Forest, Decision Tree, and SVM with 0.99 F-measure. © 2024 IEEE.

关键词： Decision trees

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Integrated RFID data Acquisition and Cloud-based Attendance Management System with Real-Time Google Sheets Synchronization 15

Integrated RFID Data Acquisition and Cloud-based Attendance ...

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15th international conference on advances in computing, Control, and Telecommunication technologies, ACT 2024

作者： Ninawe, Swapnil S. Pavithra, G. Manjunath, T.C. Samyama Gunjal, G.H. Department of Electronics & Communication Engineering Dayananda Sagar College of Engineering Karnataka Bangalore India Department of Computer Science & Engineering University Visvesvaraya College of Engineering Bangalore University Bangalore India

ISBN: (纸本)9798331300579

This paper presents the development of an RFID-based attendance monitoring system utilizing NodeMCU ESP8266 microcontroller, MFRC522 RFID card module, and Google Sheets integration. The proposed system aims to automate the attendance process, offering a convenient and efficient solution for various educational and organizational settings. The system employs RFID technology for identification and authentication, and leverages the internet connectivity of NodeMCU ESP8266 to upload attendance data to Google Sheets in real-time. The integration with Google Sheets provides seamless data management and accessibility for administrators and stakeholders. The hardware components including NodeMCU ESP8266, MFRC522 RFID card module, 5V buzzer, breadboard, and jumper wires are utilized to build a compact and cost-effective attendance monitoring setup. The system architecture, hardware design, software implementation, and integration with Google Sheets are discussed in detail. Experimental results demonstrate the effectiveness and reliability of the proposed system in accurately recording attendance data and streamlining administrative tasks. © Grenze Scientific Society, 2024.

关键词： Information management

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