Asian Journal For Convergence In Technology (AJCT) ISSN -2350-1146

Time-Dependent Demand and Price Effects on Inventory Models: An Analytical Study

2024-12-23T04:56:38-05:00

This analytical study investigates the impact of time-dependent demand and pricing on inventory models, focusing on deteriorating items. Inventory management plays a critical role in operational efficiency and cost minimization. Understanding how demand and price fluctuations over time affect inventory models is crucial for optimizing stock levels and reducing waste. By reviewing recent advancements and methodologies, this study highlights key findings, identifies research gaps, and suggests areas for further investigation. The analysis encompasses various models and approaches, providing a comprehensive overview of the current state of research in this domain.

Semantic Coherence and NLP: Redesigning post-COVID Mental Health Diagnostics with CNNs and LSTMs

2024-12-23T04:59:36-05:00

The COVID-19 pandemic has intensified the need for innovative, scalable diagnostic tools for mental health, given the surge in related disorders globally. This study presents a novel neural symbolic approach leveraging natural language processing (NLP) to analyze semantic coherence in text data, aimed at predicting mental health outcomes. Integrating convolutional neural networks (CNNs) with long short-term memory networks (LSTM) and an attention mechanism, this model excels in extracting and emphasizing critical linguistic features from vast datasets of online textual communications. Our evaluations show that the model achieves an accuracy of 92.4%, with precision, recall, and an F1-score significantly superior to traditional LSTM models. The ROC-AUC score of 0.92 highlights its effectiveness at distinguishing various mental health states, while the attention mechanism enhances the model’s interpretability, shedding light on key text features indicative of mental distress. This research underscores the potential of AI in enhancing mental health diagnostics in the context of current events, proposing a powerful tool for early detection and intervention.

Quick Sort Optimized for Non-decreasing Data set

2024-12-23T05:01:32-05:00

The Sorting Algorithm proposed by C.A.R Hoare in 1961 by name Quick sort, which is popularly known for being the fastest sorting algorithm. Quick sort is still being practiced in the field of computers systems and its applications. The Algorithm whose efficiency to sort random data set is represented in asymptotic notation as O(n log₂ n) and when quick sort algorithm has a input data set which is already Ordered then it takes a quadratic execution time which is considered as a worst case performance and this behavior is represented in asymptotic notation as O(n²). The worst case performance is due the scan over heads which occur over the pre-sorted data set, in other words the partitioning gets skewed due to recursive calls and hence results in a quadratic complexity. This research paper presents an algorithm which minimizes a worst case execution time making it linear when the input list is in non-decreasing order. The paper describes how the improvements are accommodated in the existing quick sort. A priori analysis of proposed algorithm for different cases is made along with a proof of correctness. Later the algorithm is verified for its correctness and asymptotic performance. The algorithm is implemented using C++ and also we have compared with other popular quick-sort version.

Comparative Evaluation of Predictive Models on Kidney, Lung Cancer and Heart Disease

2024-12-23T05:03:31-05:00

This study supports advances in machine learning to improve early detection and treatment planning for lung cancer, cardiovascular disease, and kidney disease. We compare traditional models such as decision trees and logistic regression with complex techniques such as support vector machines, random forests, and KNN and evaluate them on publicly available data. This hybrid approach uses random forest and decision tree classifiers, leveraging adaptive learning to improve model accuracy. Results showed high prediction accuracy for kidney disease and lung cancer , while prediction accuracy for heart disease was average . This difference indicates the need for better work and more information. Future studies will focus on improving cardiovascular models, addressing data uncertainty, and integrating predictive models into clinical practice to support early diagnosis and personalized treatment to improve patient outcomes. This study demonstrates the potential for machine learning to have a major impact on diagnosis and patient management.

Al-Based Signal Intelligence for Real-Time Threat Detection

2024-12-23T05:07:32-05:00

Technology in AI and signal processing has changed signal intelligence (SIGINT) in recent years. This study examines AI-based Signal Intelligence (AI-SIGINT) systems for real-time threat detection in military, cyber security, and critical infrastructure protection. AI-SIGINT uses cutting-edge machine learning (ML) and deep learning (DL) algorithms to evaluate massive volumes of signal data from radio frequency (RF), satellite, and mobile networks to detect and neutralize threats in real time. AI-SIGINT systems autonomously monitor, intercept, and decode signal communications to quickly identify aberrant patterns that may indicate hostile activity or impending threats. A key component of AI-based signal intelligence is adaptive danger detection. Using reinforcement learning (RL) and anomaly detection, the system continuously evolves to improve threat perception. This adaptability detects sophisticated, changing threats like jamming attempts, frequency hopping, and cyber intrusions. This research also examines AI-driven SIGINT's ethical issues, including data privacy and unlawful surveillance. It also addresses technology issues like merging AI algorithms with SIGINT infrastructure and the necessity for high computational resources.

Design and Optimization considerations for real-time video conferencing using IMS in 4G/LTE Networks

2024-12-23T05:09:49-05:00

IP Multimedia Subsystem (IMS) offers a plethora of functionalities in 4G/LTE networks. One such functionality is the video conference where three or more User Equipments (UEs) communicate with each other using VoLTE. This research paper presents optimizations in the design and implementation of an IMS video conference server for reducing the video display delay and improving the performance thereof. Both application as well as network level parameters are found to impact the video display delay at the UE, hence performance improvement of both are considered in this paper. Optimization techniques adopted in this implementation resulted in enhanced performance of video conference calls over the LTE network.

High Performance Approximate Multiplier using reversible logic gates

2024-12-23T05:12:01-05:00

Reversible logic has previously been shown to cause higher power consumption and a significant amount of dissipated energy because of information loss in standard design methods. This project describes the approximate multiplier using Reversible logic gates. In this design, the reversible logic gates replace the half adder and full adders in the multiplier. It uses two RG(Reversible Gate) in place of single reversible gate. So that it reduces the garbage value produced, which helps to decrease the overall delay and power consumption. The proposed Approximate Multiplier uses the product’s least significant half as a constant compensation term and the remaining half is precisely calculated. This can be a effective alternative for exact multipliers in practical error-resilient applications and Digital Image Processing.

Development of CNC Plotter Machine for Printing Application

2024-12-23T05:14:00-05:00

This paper presents the design, development, and application of a CNC (Computer Numerical Control) plotter machine specifically developed for printing applications. The research focuses on the integration of modern CNC technology with printing processes to enhance precision, automation, and versatility in various printing tasks. The paper discusses the hardware design, software control, and operational principles of the CNC plotter, along with the potential applications, challenges, and future trends in the field of printing.

Prototype Development and Testing of a Low-Cost Off-Grid PV Inverter for Sustainable Energy Solutions in Remote Regions

2024-12-23T05:17:10-05:00

This study details the process of creating, modeling, and testing a novel off-grid photovoltaic (PV) inverter system for use in distant, small-scale energy applications. Solar photovoltaic (PV) modules, a battery pack, a charge controller, and a low-power inverter make up the system's structure. A dependable source of electricity for electronics like lights and phone chargers, the inverter transforms direct current (DC) from solar panels into alternating current (AC). The design focuses on optimizing energy storage and conversion for off-grid systems, with a special emphasis on handling variable loads. The effective energy conversion and reliable power production are highlighted by the simulation results, which indicate the inverter's electrical performance. The inverter's practicality and efficacy for renewable energy applications off-grid were demonstrated by the development of a physical prototype, which served to verify these results. Research like this shows that even modest solar power systems have the ability to help find long-term answers to our energy problems. In order to facilitate the widespread use of renewable energy sources, future research may investigate ways to scale these systems and incorporate more sophisticated energy management techniques.

Efficient Sunflower Solar Power Tracking and Monitoring System

2024-12-23T05:19:15-05:00

The amplified need for renewable energy sources has increased the demand for efficient solar energy systems. This paper brings forth an inspiration of a sunflower-solar power tracking and monitoring system. In this approach, the optimum capturing of energy has been achieved by tracking the movement of a natural sunflower as it follows the movement of the sun. It consists of miniature solar panels, N2O gear motors, Li-Po batteries, MG 996 R servo motors, limit switches, light-dependent resistors (LDRs), and an Arduino Nano 328P microcontroller, integrated along with an L293D motor driver. Integration of proactive sensing and real-time tracking capabilities into the proposed system heavily improves the generation of solar energy, thus significantly reducing energy wastage. Experimental results confirm that this new design is effective and promising in improving the efficiency of solar energy.

CRPA for Anti-jamming Capability

2024-12-23T05:20:57-05:00

Controlled Reception Pattern Antennas (CRPAs) are designed to optimize the reception and processing of GPS/GNSS signals, minimizing interference while maximizing accuracy. By leveraging multiple antenna elements and advanced signal processing techniques, CRPAs achieve these objectives effectively. These systems are increasingly being adopted, particularly in the Defense sector. CRPAs are highly efficient in countering jamming and spoofing, as they dynamically adjust to mitigate such disruptive signals. To implement null-steering and beamforming techniques, it is essential to determine the direction of the interference or jammer. With the widespread use of GPS in defense equipment, ensuring reliable GPS performance in mission-critical operations, especially in jamming environments, is vital. As such, studying this technology within systems that can provide anti-jamming capabilities for GPS-enabled devices—such as drones, GPS receivers, target acquisition systems in firearms, tanks, and helicopters—is crucial.

Craftify: A Course Creation Application to Help Instructors with Designing New Online Courses

2024-12-23T04:51:49-05:00

Craftify is a course creation platform designed to simplify and accelerate the web-based course design process, addressing the complex and time-intensive challenges often faced by instructors. Through an intuitive graphic frontend, Craftify integrates key instructional design principles, multimedia assets, and adaptive learning techniques to empower users with ready-to-use, customizable course structures. Its features include automated course structuring, quiz creation, multimedia incorporation, and seamless syncing with popular LMS platforms such as Moodle, Blackboard, and Canvas. A pilot study with 50 lecturers—administered through questionnaires—demonstrated Craftify's effectiveness, showing reductions in course development time, higher self-reported satisfaction levels among instructors, and improved student engagement within online environments. By streamlining these essential elements, Craftify effectively supports instructors in creating dynamic, interactive, and high-quality online courses.