International Journal of Research in Science & Engineering (IJRISE) ISSN: 2394-8299

Fake Credit Transaction Detection Using Machine Learning

Mon, 01 Apr 2024 00:00:00 +0000

In today's digital age, detecting and preventing fraudulent credit card transactions is of paramount importance. As technology advances, criminal methods are also becoming more sophisticated. The use of machine learning in credit card fraud detection and mitigation has grown significantly. In this research study, a novel method for identifying fraudulent credit card transactions with machine learning algorithms is presented. The proposed system leverages past transaction data and various characteristics associated with each transaction, such as location, transaction amount, and time, to build a predictive model. These models are trained to recognize patterns that point to fraudulent activity using supervised learning methods like random forests and support vector machines. Several metrics, including accuracy, precision, recall, and F1 score, are used to assess the performance of the model. According to experimental data, the suggested method works better than conventional rule-based fraud detection systems and achieves high accuracy. The system can effectively detect fraudulent credit card transactions while minimizing false positives. Machine learning improves the security of credit card transactions by detecting fraud in real time. In summary, this study advances the field of credit card fraud detection by using machine learning algorithms to counteract the constantly changing tactics used by fraudsters.

Implementation of FPGA-based Accelerator for Convolutional Neural Networks

Abdullah Farhan Siddiqui, Prof. B. Rajendra Naik — Mon, 01 Apr 2024 00:00:00 +0000

This research paper presents a novel FPGA-based accelerator tailored for Convolutional Neural Networks (CNNs), specifically implemented on the Virtex-7 evaluation kit. By harnessing the inherent parallel processing capabilities of FPGAs, the architecture of the accelerator is meticulously crafted using Verilog. The FPGA implementation demonstrates a resource-efficient design, making use of 588 Look-Up Tables (LUTs) and 353 Flip Flops. Notably, the efficient utilization of these resources signifies a careful balance between computational efficiency and the available FPGA resources. This research significantly contributes to the field of hardware acceleration for CNNs by offering an optimized solution for high-performance deep learning applications. The presented architecture serves as a promising foundation for future advancements in FPGA-based accelerators, providing valuable insights for researchers and engineers working in the domain of hardware optimization for Convolutional Neural Networks.

Improving Product Marketing by Predicting Early Reviewers on E-Commerce Websites

Mon, 01 Apr 2024 00:00:00 +0000

Customers now often consult online surveys before making a smart purchase decision. Much of the time, the early surveys of an item fundamentally affect the deals of that item later on.In this study, we move up and focus on the conduct characteristics of early analysts via their posted surveys on two actual enormous web based company stages, i.e., Amazon and Cry. To be clear, we divide an item's lifecycle into three discrete phases: the beginning, the middle, and the end. Early commentators are clients who have posted surveys during the pilot phase. We provide a quantitative portrait of the first reviewers based on their rating habits, the popularity of the items they reviewed, and the ratings of support they received from other users. We have viewed that as (1) Early commenter will often downgrade a higher average rating, and (2) An early analyst tends to publish more positive polls.

Effects of Calcium Propionate in Drosophila Melanogaster

Aishwarya H — Mon, 01 Apr 2024 00:00:00 +0000

Calcium propionate, a common food preservative, has been widely used to inhibit mold growth and prolonging the shelf life of various food products. However, its potential the impact on living organisms, especially in the context of eating, remains largely unexplored. In This study aimed to elucidate the effects of calcium propionate supplementation Drosophila melanogaster, a model organism widely used in biological research. In this study, we studied survival rates, lifespan, motility changes, and gut Microbes and the reproductive cycle of D.melanogaster.

Enhancing Medical Data Analysis with Federated Learning in the Internet of Medical Things

Alyaa Ali Hameed Kjwan, Omar Hasan Mohammad — Mon, 01 Apr 2024 00:00:00 +0000

The Internet of Things refers to physical items, which are equipped with software, sensors, computing power, and other technologies, and that communicate with other electronic devices and systems over communication networks or the Internet. A collection of medical devices and software programmes known as the Internet of Medical Things (IoMT) link to healthcare networks via internet computing. Machine-to-machine communication, which is the foundation of IoMT, is made feasible by medical equipment that includes Wi-Fi. IoMT devices have the ability to analyse and store collected data by connecting to cloud services. IoMT is a different moniker for IoT in healthcare. Since data is transferred via the internet and the IoMT creates a lot of data, privacy concerns are important. The vast volume of data produced by IoMT devices calls for big data processing, and federated learning tackles privacy issues as a way to overcome these difficulties. The big data health care framework for IoMT is discussed in this article. It is built on federated learning.

Impact of Aggressive HGV Platoons and Human-Driven Heavy Goods Vehicles on Signalized Intersections Performance

Mustafa Albdairi, Alaan Ghazi, Aras Aldawoodi — Wed, 15 May 2024 00:00:00 +0000

This paper presents a study on the performance and environmental impacts of aggressive heavy goods vehicle (HGV) platoons in comparison to human-driven HGVs at a random signalized intersection under varying traffic volumes between 500 and 1500 HGVs. A total of 12 scenarios have been developed, 6 for each of the vehicular behaviors, to quantify the emissions (CO, NOX, VOC) and fuel consumption, travel time, and delays. The analysis is implemented using the PTV VISSIM microscopic traffic flow model. Realization that the majority of the differences in the performance of the two vehicular controls seems to be different as the traffic volume increases was the realization. In most cases, aggressive HGV platoons were found to have lower emissions, in comparison to fuel consumption, while the flow and delay of aggressive HGV platoons were comparatively better against the case with human-driven HGVs. The results have thus provided new avenues for the incorporation of aggressive HGV platoons into urban traffic systems, more so in scenarios that involve a high level of traffic at intersections, as a potentially effective tool for augmenting the efficiency of an intersection and cutting down its environmental impacts. The present study strongly recommends the advancement of traffic management strategies that can capture the dynamics between the two heterogenous traffic flows induced by autonomous and semi-autonomous vehicle technologies.

Validation of Abbreviated Science Anxiety Scale in the Indian Context

Dimpy Balgotra, Dr. Rajib Chakraborty — Thu, 16 May 2024 00:00:00 +0000

The abbreviated science anxiety scale1 was validated in the Indian context with 290 secondary school students of grade 8 as the sample subjects. Exploratory factor analysis revealed the original two factors explaining 54.778 % of total variance in science anxity construct. Confirmatory factor analysis validated the factor structure of the construct with excellent goodness of fit estimates like CMIN/DF = 1.830, CFI = 0.969, TLI = 0.957, RMSEA = 0.054 and SRMR = 0.0392, conducted using the estimator maximum likelihood (ML). The floor and ceiling effect estimation for content validity showed that both the effects are absent with estimates way lesser than the benchmark of 15 % at 6.55 % and 0 % respectively. The internal consistency reliability estimation using Cronbach’s alpha found that the five items of the first factor “Learning science anxiety” and four items of the second factor “Science evaluation anxiety” had this estimate at 0.803 and 0.678 respectively, both of which fairly indicate good measurement of reliability of the scale. The education implications of the study are discussed.