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Research Paper

Fingerprint Sensing Gun

In this modern age of science and technology, threats to our lives are exposed to a lot of unplanned factors, one of them which could being shot by a gun, rather intentionally or not! Fingerprint-sensing gun technology can prove to be a great savior for this crisis. It allows fast authentication of one’s fingerprint to unlock the gun and is used only when necessary. Unauthorized users cannot fire the weapon which itself gives an upper hand to the owner of the weapon. By the use of a handful of electronic components, such a gun can be designed. It could prevent children from shooting a family member and untrained individuals.

Published by: Rohitkumar Mukeshbhai Mistri, Kapil Virbhadra Mathpati

Author: Rohitkumar Mukeshbhai Mistri

Paper ID: V9I5-1194

Paper Status: published

Published: November 7, 2023

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Research Paper

SMS spam detection in Machine Learning using Natural Language Processing

This paper presents the identification of Spam and ham messages using supervised machine learning algorithms Random forest Classifier, and Logistic Regression algorithms and Analyzes how each filter performs when detecting Ham and Spam. A spam message is a big issue in mobile communication to reduce this effective spam detection techniques should be built Preprocessing is done using the NLTK library with various Stemming Algorithms, Word clouds are used and tokenizing is also performed. The data set is divided into two categories for training and testing the classifiers . the results demonstrated that the performance of Random Forest is better than Logistic Regression. Random forest achieved a better accuracy of 97%.

Published by: Thanniru Lakshman, Singarapu Sanjay Kumar, Ulligaddala Satish Kumar, Yenikepalli Sri Sekhar, Yellamati Suresh

Author: Thanniru Lakshman

Paper ID: V9I5-1190

Paper Status: published

Published: November 2, 2023

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Review Paper

The Dilemma in Quantum Mechanics

This study possesses the ambition to further investigate and indulge in the world of quantum mechanics and its complexities and possibly ascertain a solution for one major issue attributed to it: the measurement problem, which refers to, in layman's words, the issue of how and why a wave function collapses. According to papers published during the late 1930s, the measurement problem was an issue that could be partially resolved by utilizing the loosely shaped ideas of the Copenhagen Interpretation, which was later coined by Werner Heisenberg during the 1950s, as well as the newborn Pilot-Wave theory, which was further developed and brought to prominence by David Bohm’s work during the 1950s. However, due to the deficiency of new information from modernized research modalities and experiments, these papers lack the new approaches proposed by various physicists, which places a circumscription on the aptitude possessed on this topic. Hence, through this paper, I possess the ambition to utilize the theories of Many Worlds (MWI), QBism, and the further developed Pilot Wave Theory (PWT) to hopefully solve this issue. Contrary to what has customarily been believed, the measurement problem can be partially solved by twisting the functioning of quantum mechanics according to certain theories, which would then act as a base for them and substantiate their claims regarding the measurement issue, such as the MWI theory going against traditional quantum theory and claiming that particles possess definite properties, with many other theories doing something similar, which I will be articulating upon in this paper. To deduce this prolonged abstract, I hope to provide you with a higher magnitude of perception on this infamous issue and probably resolve some of your pertaining doubts on quantum physics.

Published by: Samarth Krishna Mathur

Author: Samarth Krishna Mathur

Paper ID: V9I5-1172

Paper Status: published

Published: October 31, 2023

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Research Paper

Hybrid deep approach for malware detection

Malware is malicious software designed to compromise computer systems, and poses a significant threat to businesses, with potential repercussions ranging from financial losses to damaged reputations and eroded customer trust. To address this challenge, we propose a hybrid deep learning approach that combines the power of Long Short Term Memory (LSTM) and Gated Recurrent Units (GRUs), both of which are models in the Recurrent Neural Network (RNN) family. Our research focuses on assessing the potential improvements achieved by this hybrid approach, leveraging a benchmark dataset known as NSL-KDD+. This dataset offers a temporal dimension and encompasses a diverse array of malware samples and network traffic scenarios for comprehensive testing and evaluation. We employ a range of performance metrics, including Accuracy, Precision, F1 Score, Mean Absolute Error (MAE), and others, to comprehensively gauge the effectiveness of our proposed approach.

Published by: Vadduri Uday Kiran, P. Shiva Prasad Reddy, V. Sri Harsha, R. Vijay Kumar, Y. Venkata Narayana

Author: Vadduri Uday Kiran

Paper ID: V9I5-1187

Paper Status: published

Published: October 30, 2023

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Research Paper

Care: Cardiac attack risk estimation using Machine Learning

We are revolutionizing heart attack risk assessment with our ground-breaking initiative, "CARE: Cardiac Attack Risk Estimation Using Machine Learning," by utilizing machine learning models' predictive power. Our technology uses past data analysis to forecast the likelihood of a subsequent heart attack based on user-supplied details such as physical attributes, symptoms, and medical background. Our project's main goal is to reduce the burden on the healthcare system by providing users with remote access to screening facilities that can identify people at both low and high risk.With the goal of improving the precision of heart attack risk predictions, our ground-breaking platform, the "Heart Attack Risk Predictor," is a groundbreaking venture into the field of machine learning.

Published by: Patan Imran Khan, Kothamasu Surya Ratna, Meda Gopi Krishna, Nutalpati Ashok

Author: Patan Imran Khan

Paper ID: V9I5-1186

Paper Status: published

Published: October 28, 2023

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Research Paper

Analysis of the effectiveness of the digital enrollment and grading system

The Online Enrollment and Grading System of Bato Institute of Science and Technology is a comprehensive platform that helps manage all aspects of student enrollment and grading. It provides an efficient way to store and manage student data, grade reports, and enrollment records, ensuring that all relevant information is tracked from the beginning of a student's course until the end. The system uses a completely computerized process, reducing the chance of human error and maintaining accurate data. The system also includes a backup system that allows for the retrieval of important information in case of any system malfunctions, ensuring that student and organizational data is secure. There are two access modes: administrator and user. The administrator module is responsible for maintaining the system by creating user accounts, scheduling system updates, and making sure everything runs smoothly. The user module allows staff and students to access their grades and other reports. To evaluate the system's effectiveness, the researcher used Descriptive Developmental Research to gather feedback from both staff and students. Based on the results of the data, the researcher concluded that the Online Enrollment and Grading System is highly effective with a "very good" rating. However, there is always room for improvement in any system to prevent potential issues. Therefore, the researchers recommend continuous evaluation and improvement of the platform to maintain high standards of service and efficiency. In conclusion, the Online Enrollment and Grading System of Bato Institute of Science and Technology is an efficient and effective platform that meets the needs of both staff and students, providing a valuable solution for managing enrollment and grading processes.

Published by: Mary Jane Pagay Cierva, Rhoderick D. Malangsa

Author: Mary Jane Pagay Cierva

Paper ID: V9I5-1179

Paper Status: published

Published: October 28, 2023

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