Graphene oxide (GO) which is a derivative of graphene, has garnered considerable attention due to its remarkable mechanical, thermal and electrical properties. In this study, GO nanoparticles were synthesized using ball milling method with graphite powder as the precursor potassium permanganate as oxidizing agent for inducing structural modifications. The synthesized graphene oxide was characterized by scanning electron microscopy (SEM) and X-ray diffraction (XRD) to confirm the structure, morphology and its functional groups. Subsequently water-based graphene oxide nanofluids of distinct concentrations (1, 3 and 5
Fibre-reinforced polymer composites (FRPCs) are widely used in structural and semi-structural applications across various engineering fields. While previous drilling research on FRPCs has largely focused on optimising processing conditions to enhance hole quality, limited attention has been given to the interplay between drilling parameters, material properties, and production methods. This study investigates high-speed drilling of silane-treated pineapple fibre and waste micro rubber-reinforced vinyl ester composites under thermal (50 °C for 7, 14, and 30 days) and water ageing (room temperature for 7, 14, and 30 days) conditions. Post-drilling analysis examined common defects such as fibre delamination, matrix cracking, degradation, fibre swelling, burning, and edge tearing using an optical microscope. Results show that silane surface treatment with 3-aminopropyltrimethoxysilane significantly reduced drilling-induced damage under temperature ageing conditions, demonstrating its effectiveness in improving composite durability and machinability. These highly stable treated composites could be used in automotive, aerospace, and structural applications where large drill holes and fasteners are needed to be fixed to join the structural member temporarily and permanently.
As applications such as AI and VLSI demand faster and more energy-efficient computing, there has been an growing extension of research into estimate computing technique. This paper presents the High-Speed Digital Approximation Multiplier Design and Performance Study for AI-VLSI Systems. By intentionally reducing the precision of certain arithmetic operations, the proposed multiplier provides significant performance and energy efficiency improvements with an accuracy level appropriate for generic AI benchmarks. By exploring new error-tolerant algorithms, the architecture balances trade-offs of speed, power consumption, and computing precision. The approximate multiplier suggested here reduces power consumption by about 40
As artificial intelligence (AI) and cloud computing have developed, video strategies, some of which are used to manipulate by altering people's face identities, called “deep fakes,” have become more sophisticated and faster. Spotting these fakes is not easy. In recent history, fakes have been employed as influencers to plan terrorist attacks, produce revenge porn, extort individuals and so forth, foment political unrest and much more. Hence, discovering fakes is crucial to allowing their widespread propagating on social media networks to come to a stop. This research article looks into how well Efficient-Net performs at identifying deepfake videos. In this paper, we propose an Efficient Net based efficient deep fake-detection system. To improve the quality of video inputs provided by the user frame extraction and preprocessing are performed. Efficient Net looks at the gathered frames and identifies even the slightest inconsistencies and abnormalities associated with deep fakes. Aggregation of these frame-level predictions leads to a classifier output predicting whether or not the video is fake. As per this study's result, 78
By using machine learning techniques, new outcomes for bank telemarketing could be achieved to increase bank deposits over time. For banks, obtaining time deposits is always a crucial business, and a successful marketing campaign is usually crucial to financial sales. Identifying the consumer segment for this purpose is typically a challenge for banking institutions. The data shows a class imbalance while direct telemarketing operations are not well received by customers. The economic crisis has made it difficult for banks to attract consumers. Marketing is therefore viewed as a helpful tool. The banking sector is making an effort to draw customers’ attention to recurring deposits. Knowing the company's objective, what precisely are we intending to predict in this situation? We ought to be aware of every feature, depending on your involvement or because of domain expertise. This project's primary objective is to predict the probability that a customer would sign up for an agreement to deposit (variable y) with a financial institution. Numerous procedures, including data collection, exploratory data analysis, information preprocessing, and model building, have been used to accomplish this. This is the Cat boost classifier, which uses scientific methods and low-budget resources to provide the most accurate results.
