International Journal on Recent and Innovation Trends in Computing and Communication
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Personalized E-Commerce Recommendations Using Hybrider Recommender Systems
Get PDFIn the rapidly evolving landscape of e-commerce, understanding customer preferences and product features is paramount for enhancing user experience and driving sales. This research presents a comprehensive exploration of hybrid recommender systems, which effectively combine various algorithms to provide personalized product recommendations. By integrating collaborative filtering and content-based filtering methods, we demonstrate how these systems can leverage user behavior and item characteristics to improve recommendation accuracy. The study highlights the challenges of combining different algorithms, emphasizing the importance of careful selection and adjustment to ensure optimal performance. Furthermore, we explore the role of smart technology in understanding customer desires through keyword analysis, moving beyond traditional purchase history methods. Our findings underscore the significance of user modeling processes in content-based filtering, which utilize diverse data-mining techniques to learn about customer preferences from online activities. This research contributes to the ongoing discourse on enhancing the transparency and effectiveness of hybrid recommender systems, addressing the complexities of user trust and data privacy in sensitive domains such as healthcare and finance. Ultimately, this work aims to provide valuable insights for researchers and practitioners seeking to optimize recommendation systems in e-commerce environments
Batteries on the Move: Navigating Challenges, Expanding Horizons for Indian EVs
Get PDFIn India, the widespread adoption of swappable battery systems for electric vehicles faces significant hurdles such as regulatory gaps, infrastructure limitations, technological constraints, economic uncertainties, and environmental complexities. Overcoming these challenges demands a cohesive strategy involving thorough regulatory assessment, infrastructure expansion, tech collaboration, financial scrutiny, and environmental evaluations. This holistic approach is key to unlocking the untapped potential of swappable batteries, paving the way for an innovative, eco-friendly electric mobility landscape in India. This research paper aims to provide valuable insights for policymakers, industry players, and researchers grappling with the adoption of swappable battery systems in India's EV sector
?Implementation of Security Protocol for Intrusion Detection Systems in Wireless Sensor Networks
Get PDFSensor networks consist of compact sensors and actuators capable of monitoring physical conditions. Wireless Sensor Networks (WSNs) with limited power and dynamic topology require effective security mechanisms. Insider attacks pose a greater challenge than outsider attacks. This work proposes an Intrusion Detection approach in WSNs to detect attacks, emphasizing experimental results, parameter analysis, and Performance Evaluation based on accuracy and minimizing false positives
Essential T-Goldie-Supplemented Modules
Get PDFIn our work, we defined Essential T--equivalent is relation between two submodules of a module N. A submodules K and H of a modules N are Essential- T- equivalent (K H), if and , for some . We give many characterizations of the relation equivalent. So, we defined the Essential T- Goldie* lifting Modules and we give many properties related with this type. Keyword: ET-small submodule, Essential T-lifting module, Essential T-Goldie*lifting module
Weighted Residual Target Proximity Kernel Pursuit Regression based Students Admission Prediction for Higher Education
Get PDF Education plays a significant role in providing individuals with the knowledge, skills, and tools needed for personal as well as academic growth. Due to the increasing number of higher education graduates, student admissions process is essential for selecting qualified candidates for admission in a universities or colleges. An admissions system with suitable and reliable criteria is important to select students who performing well academically as well as other activities at institutions. Therefore, each university or college needs to use the best possible techniques for analyzing the history of a student's academic performance and other extracurricular activities before admitting them. Education Data Mining (EDM) involves the application of data mining techniques to large educational databases with the aim of discovering useful information. Several machine learning techniques have been developed in this area, but there are issues related to time efficiency and errors in prediction of admissibility for higher education. To address the aforementioned challenge, a novel technique named Weighted Residual Target Proximity Kernel Pursuit Regression (WRTPKPR) has been developed. This technique aims for the accurate prediction of graduate admissions with minimal error by mapping the course to the students based on their interests and CGPA secured. The proposed WRTPKPR technique includes three major phases namely data acquisition, preprocessing, and feature selection for accurate predictive analytics.Top of Form The WRTPKPR technique initiates by collecting information from the dataset during the data acquisition phase. Following data acquisition, the WRTPKPR technique undergoes data preprocessing to transform the input data into a suitable format for accurately predicting whether the student is admissible or not. Two key processes are conducted in the data preprocessing phase, namely, missing data imputation and outlier data detection. In the initial step, the Horvitz–Thompson Weighted imputation method is applied to generate missing data points based on other known data points in the dataset. In the second step, an outlier detection method based on the maximum normalized residual test is employed to identify data points that significantly deviate from the rest of the data point in the dataset. With the preprocessed dataset, the target feature selection process is conducted by applying Kernel Cook's Proximity Projection Pursuit Regression. Based on the selected target features, accurate admission predictions are made for higher education graduates with minimal time consumption. Experimental evaluation considers factors such as admission prediction accuracy, precision, recall, F1-score and admission prediction time. The results demonstrate that the proposed WRTPKPR technique achieves efficient performance outcomes, including higher accuracy, precision, with minimized time
Photoplethysmography (PPG) Signal Heart Rate Monitoring During Exercise and Reduces Motion Artifacts
Get PDFPhotoplethysmography (PPG) is a non-invasive technique for monitoring cardiovascular parameters such as heart rate during various activities, including exercise. However, the accuracy of PPG-based heart rate monitoring can be compromised by motion artifacts caused by body movements. This study explores the effectiveness of three distinct algorithms – Random Forest, Decision Tree, and a novel Lion Optimization Algorithm-enhanced Long Short-Term Memory (LOA-LSTM) – in improving PPG-based heart rate monitoring accuracy during exercise while mitigating motion artifacts.The Random Forest algorithm harnesses ensemble learning to aggregate Decision Trees, providing robustness against noise and improving heart rate predictions. Decision Trees offer transparent decision-making based on PPG features, aiding in rapid classification of heart rate trends. The LOA-LSTM algorithm uniquely combines the Lion Optimization Algorithm's ability to adaptively explore and exploit with the temporal sequence learning capacity of LSTM. This integration aims to achieve high accuracy by dynamically optimizing LSTM parameters, effectively reducing motion artifacts and improving exercise-related heart rate predictions.In this comparative study, these algorithms were evaluated using a diverse dataset collected during exercise sessions. Experimental results demonstrate that while all three algorithms enhance heart rate monitoring accuracy and reduce motion artifacts, the LOA-LSTM algorithm outperforms the others, consistently achieving the highest accuracy rates about 99%. The proposed approach holds significant promise for improving real-time heart rate monitoring accuracy during exercise, contributing to more reliable fitness tracking and healthcare applications
Evaluation of the Transform Domain DCT and Spatial Domain LSB Steganography Algorithms' Performance
Get PDFSecuring data in the modern world is highly important and data encryption is one of the key agents of this mission of securing data. The problem of security of data is because of growth in internet usage and easy availability of the internet. Ensuring the right to privacy and keeping confidential data safe is one of the most important concerns of every means of communication. Steganography is defined as a technique which is used in information security that includes the hiding of data in other data structures so that the information itself cannot be accessed by unauthorized participants. The current paper is aimed to bring a complete review of the steganography algorithms used for hiding the data and for comparing the steganography algorithms in both the spatial and transform domain with the help of effectiveness parameters they have such as average error rate, peak signal to noise ratio, encryption time, & decryption time
Transformative Trade Credit Takaful Model: A Progressive Alternative to Credit Insurance
Get PDFTrade credit insurance is a common method of risk management used by suppliers to safeguard themselves against non-payment by credit buyers. The manufacturer extending trade credit risks facing cash flow problems and potential customer non-payment. Manufacturers can increase sales with the aid of trade credit insurance and significantly reduce their default risk. A few companies in certain countries have introduced trade credit insurance as a relatively new service. However, they do not offer the Takaful model. Generally, people increasingly seek Shariah-compliant solutions, such as Islamic banking, to address their financial needs. Therefore, the government must bolster its presence in this domain. The overarching objective of this study is to propose an innovative Credit-related Takaful industry product for consideration. This study employed a qualitative approach, with unstructured interviews conducted to gather insights. The study sample comprises experts from the Shariah compliance department, Shariah scholars, and the Takaful industry experts, who were interviewed to gain a comprehensive understanding of Shariah principles. These interviews were conducted to gather insights into which Takaful model would be most suitable for Trade Credit in Pakistan. The majority of Shariah Scholars recommended the Waqf Wakalah model for the implementation of Credit Takaful in Pakistan. The findings of this study will help the Takaful industry of Pakistan to launch a new product for credit trading. This study will help further to know the importance of Credit Takaful and tap the gap in the Credit Takaful market, especially in Pakistan. Lastly, the findings will help the regulators of the Takaful industry to introduce new rules for the industry, providing a baseline for researchers to enhance their knowledge. Further investigation into Credit Takaful in Pakistan will build off the findings of this study to fill out the full circle of the Islamic financial system. It is a step in the right direction toward ending poverty and income inequality and increasing public awareness of the importance of Credit Takaful in today's world
AI-Driven Decision Support Systems in Management: Enhancing Strategic Planning and Execution
Get PDFArtificial intelligence (AI) is transforming strategic decision-making processes across various industries. Organizations increasingly rely on AI-driven decision support systems that leverage massive amounts of data and real-time analytics to enable more informed planning and predictive capabilities. However, less focused research has explored the integration and impact of such tools specifically within managerial strategy and execution contexts. This study conducts qualitative and quantitative analysis on the deployment of machine learning-based recommendation systems aimed at enhancing the strategic capabilities of management teams. Results indicate that AI decision tools led to improved analytic capacities, competitive response times, and reimagined vision planning, yet also posed transparency and trust challenges around advanced automation techniques. Findings provide novel implications into AI’s emerging role in augmenting and extending higher-level organizational strategy design and enactment by key decision-makers and leaders. Future directions are discussed related to addressing responsible development issues as adoption continues accelerating
Using Semi-Supervised Learning to Predict Weed Density and Distribution for Precision Farming
Get PDFIf weed growth is not controlled, it can have a devastating effect on the size and quality of a harvest. Unrestrained pesticide use for weed management can have severe consequences for ecosystem health and contribute to environmental degradation. However, if you can identify problem spots, you can more precisely treat those areas with insecticide. As a result of recent advances in the analysis of farm pictures, techniques have been developed for reliably identifying weed plants. . On the other hand, these methods mostly use supervised learning strategies, which require a huge set of pictures that have been labelled by hand. Therefore, these monitored systems are not practicable for the individual farmer because of the vast variety of plant species being cultivated. In this paper, we propose a semi-supervised deep learning method that uses a small number of colour photos taken by unmanned aerial vehicles to accurately predict the number and location of weeds in farmlands. Knowing the number and location of weeds is helpful for a site-specific weed management system in which only afflicted areas are treated by autonomous robots. In this research, the foreground vegetation pixels (including crops and weeds) are first identified using an unsupervised segmentation method based on a Convolutional Neural Network (CNN). There is then no need for manually constructed features since a trained CNN is used to pinpoint polluted locations. Carrot plants from the (1) Crop Weed Field Image Dataset (CWFID) and sugar beet plants from the (2) Sugar Beets dataset are used to test the approach. The proposed method has a maximum recall of 0.9 and an accuracy of 85%, making it ideal for locating weed hotspots. So, it is shown that the proposed strategy may be used for too many kinds of plants without having to collect a huge quantity of labelled data