International Journal of Computer (IJC - Global Society of Scientific Research and Researchers, GSSRR)
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The Impact of Machine Learning Algorithms on Improving the User Experience in E-Commerce
Full text linkThis article explores the transformative impact of machine learning algorithms on improving the user experience in e-commerce. As e-commerce develops, it is becoming a key sector that uses advanced technologies to meet the changing needs of consumers. Machine learning plays a crucial role in personalizing user interactions, optimizing inventory management through predictive analytics, and improving recommendation systems. The article examines the various methodologies used, including collaborative filtering and contextual networks, and highlights the benefits of artificial intelligence-based chatbots to improve customer interaction. It should be noted that potentially in the future it will be possible to use machine learning in e-commerce, which will lead to solving problems such as data privacy and algorithm bias. Ultimately, the article highlights the need to adapt and innovate in the field of e-commerce to maintain user loyalty and satisfaction in a growing competitive market
Hybrid Skin Lesion Detection Integrating CNN and XGBoost for Accurate Diagnosis
Full text linkSkin cancer, particularly melanoma, remains one of the most challenging medical conditions due to its rapid progression and high mortality rate when not detected early. The growing prevalence of skin cancer highlights a significant problem in medical diagnostics: the need for automated, accurate, and efficient classification systems that can aid dermatologists in diagnosing various types of skin lesions. This issue is exacerbated by the imbalance in available datasets, underrepresentation of certain lesion classes, and a lack of generalizable diagnostic tools, ultimately impacting patient outcomes and healthcare efficiency.
This study aimed to develop and evaluate a hybrid model integrating Convolutional Neural Networks (CNNs) for feature extraction and XGBoost for classification to address the problem of skin lesion classification. This study\u27s guiding conceptual framework was applying deep learning techniques combined with ensemble models to enhance classification accuracy and model interpretability.
The study utilized the HAM10000 dataset, comprising 10,015 dermatoscopic images across seven skin lesion classes. Dynamic resampling based on power analysis ensured class balance by selecting 158 samples per class. Image preprocessing techniques, such as resizing, hair removal, and Gaussian blurring, were applied to standardize the data. The CNN model extracted hierarchical features, while the XGBoost model performed classification on these features. The research methodology involved a quantitative approach using performance metrics such as accuracy, precision, recall, F1-score, and ROC-AUC to evaluate the model’s effectiveness.
The results demonstrated that the CNN-XGBoost hybrid model achieved superior classification performance with an accuracy of 86.46% on the test dataset, outperforming the standalone CNN model. The hybrid model effectively addressed class imbalance and exhibited high discriminatory power across all lesion classes, as confirmed by an average ROC-AUC score of 0.98.
The study concludes that the hybrid CNN-XGBoost model holds significant potential for assisting dermatologists in early skin lesion detection and improving diagnostic accuracy. Recommendations for future research include validation using diverse datasets, incorporating clinical metadata, and enhancing model interpretability for real-world deployment. These findings contribute to advancing AI-driven healthcare solutions, offering promising implications for dermatological diagnostics and patient care
Construction of Real Time Data Warehouse
Full text linkData warehouse is a copy of transaction data specifically structured for querying, analysis and reporting Data warehouse is a database application that actually store and collect the data from any particular business domain for decision support system. There are different ways to implement and design the data warehouse. Some processes are involved the data extraction, transformation and loading. Integration of the data from various sources in to the data warehouse is major concept. So, while the design of the data warehouse such that to entertain all these processes accurately then we can guarantee the data purity. Approach of recent times is becoming very much famous now days that is real time data warehouse. Real time data warehouse actually load the data from the transactional and operational data stores when in real time. As soon the data is coming up in the external data source it will appear into the real time data warehouse so this paper will cater the discussion of structure of real time data warehouse. Real time data warehouse approach has major deficit of extraction and loading process. It could be very efficient source of decision support system if we can eliminate the deficiencies
Investigating the Casual Effect in Traffic Accident
Full text linkAn important field of study that attempts to increase road safety and lower the frequency and severity of accidents is the investigation of traffic accidents. For the purpose of creating effective preventative methods and policies, it is imperative to comprehend the underlying causes of traffic accidents. The practice of analyzing the relationship between two or more variables to ascertain whether one has a causal effect on the other is known as causal analysis. Through the integration of mutual information for causality analysis and Support Vector Machine (SVM) for prediction, this system is intended to examine the causative impacts of traffic accidents. The system primarily looks at the reasons behind traffic accidents in Thailand between 2016 and 2019, trying to pinpoint important elements and create practical preventative measures. The system gathers a wealth of information, such as the date, time, and position of each collision as well as information on the type of vehicle, the characteristics of the road, driver demographics, and weather. Mutual information is used to quantify dependencies, highlight important interactions, and study the causal linkages between various components. These analyses show how changes in one variable may have an impact on another. By concentrating on the most important variables, the mutual information and SVM integration improves the system\u27s analytical skills and improves model accuracy and interpretability. As a result, our technology produces thorough reports and visualizations that give stakeholders—such as legislators and traffic safety authorities—actionable insights. These observations aid in the creation of focused initiatives and laws meant to lower the frequency and seriousness of traffic accidents in Thailand
Delineating International Cooperation in the Fight against Cybercrime in Cameroon
Full text linkThe emergence of novel technologies and their increasing usage has significantly changed how things are done in modern society. While this is beneficial, it has also facilitated the global commission of modern crimes, such as cybercrimes, through electronic means. This new wave of criminal activity poses severe threats to the global community and has undermined the sovereignty of nations. At present, it is imperative for the global economy to acknowledge the impact of Cybercrime. It is evident that Cybercrime has increased security risks for critical infrastructures, resulting in massive privacy invasions and attacks on businesses and state security. As technology evolves and the world becomes more interconnected, preventing cybercrimes is becoming increasingly challenging. To address these challenges, there is a need for a well-coordinated and collective effort from governments worldwide. In line with this, the Cameroonian government has taken significant steps through the 2010 law on Cybersecurity and Cyber criminality (hereafter referred to as Cyber Law) to promote cooperation with other nations in combating the spread of Cybercrime in Cameroon. However, despite these efforts, the widespread nature of these offences continues to hinder government initiatives. This paper is aimed at assessing the efficiency of the measures taken by the Cameroonian Government to enhance international cooperation in combating cybercrime
Application of International Standards to Improve Competitiveness in the Gaming Industry
Full text linkThis review article explores the importance of international standards in optimizing processes and enhancing the competitiveness of companies in the rapidly growing market for video games. The author delves into the existing standards created by renowned organizations such as the International Organization for Standardization (ISO), the International Electrotechnical Commission (IEC), and the Institute of Electrical and Electronics Engineers (IEEE). Among the standards examined are ISO/IEC 25010, which covers systems and software quality models, ISO/IEC 33020, which assesses process capability, ISO/IEC 29110, which outlines lifecycle profiles for small businesses, IEEE 2861 for evaluating and optimizing mobile gaming performance, and ISO/IEC/IEEE 29119 for software testing. The article highlights the key features of these standards and explains how they contribute to process optimization, quality improvement, and enhanced user experience (UX). It also addresses the risks associated with implementing these standards and suggests strategies to minimize or eliminate them
Harnessing Generative AI for Optimizing Power Generation Innovations and Applications in Energy Efficiency
Full text linkThis article explores modern approaches to the integration of advanced technologies such as generative artificial intelligence (AI), the Internet of Things (IoT), and 5G, aimed at developing digital infrastructure and strategic partnerships with technology companies in the energy sector. The focus is on methods such as the use of generative AI models for big data analysis, failure prediction, and the optimization of energy grid operational processes. Special attention is given to the integration of IoT and 5G to create a flexible and resilient infrastructure capable of adapting to real-time changes. The key conclusions from this work show that these technologies not only reduce operational costs but also significantly enhance environmental sustainability through the integration of renewable energy sources. Furthermore, the analysis indicates that the implementation of Vehicle-to-Grid systems contributes to more efficient energy management, and when combined with IoT and Phasor Measurement Units (PMUs), improves the monitoring and control of electrical networks. The article emphasizes that despite the need for adaptation of existing infrastructure and significant computational resources, the potential of these technologies will continue to grow, offering innovative solutions for reducing energy consumption and enhancing productivity in the long term
Hybrid Modeling for Sales Prediction Using SARIMA, CNN, LSTM, and Stacking Ensemble
Full text linkAccurately forecasting sales in dynamic supply chain environments is essential for optimizing inventory management, resource allocation, and operational efficiency. This study addresses the challenge of achieving precise demand predictions by developing a hybrid modeling framework that integrates SARIMA, CNN, LSTM, and stacking ensemble methodologies. Ineffective sales forecasting often leads to overstocking, understocking, increased operational costs, and diminished customer satisfaction, adversely affecting global supply chain stakeholders. The research evaluates the effectiveness of combining traditional statistical models with advanced machine learning techniques for demand forecasting. SARIMA models effectively captured seasonal and linear trends, while CNN and LSTM architectures identified non-linear and temporal dependencies. However, integrating SARIMA with aggregated weekly data and CNN and LSTM models using daily granular data posed significant challenges. This mismatch excluded SARIMA from the initial stacking ensemble (XGBoost) integration. To address this limitation, a hybrid SARIMA-XGBoost model was subsequently developed and evaluated for performance. Limited time for fine-tuning CNN and LSTM models presented another challenge, leading to SARIMA outperforming both CNN and LSTM in predictive accuracy. The SARIMA-XGBoost hybrid model demonstrated superior performance compared to standalone CNN and LSTM models but was slightly less effective than SARIMA alone. The hybrid model excelled at capturing seasonal patterns, external variables, and irregular trends within the dataset. Historical sales data from 45 Walmart stores, augmented with external variables such as the Consumer Price Index (CPI), unemployment rates, temperature, and holiday indicators, formed the basis of the study. The findings revealed SARIMA’s robustness in handling linear and seasonal trends under constrained conditions, while the SARIMA-XGBoost hybrid model provided enhanced predictive accuracy.
This study concludes that hybrid frameworks hold substantial potential for improving demand forecasting, particularly in addressing diverse temporal granularities and resource constraints. Future research should focus on integrating additional external variables and optimizing deep learning models to refine the hybrid framework’s applicability across industries. Such advancements can empower supply chain managers with actionable insights to reduce costs, enhance operational efficiency, and improve customer satisfaction
Comparison of Single-Shot and Two-Shot Deep Neural Network Models for Pose Estimation in Assistance Living Application
Full text linkEstimating human posture from an image or video is an essential task in computer vision. This task has detected body key points from a camera for body posture and gesture recognition technology, which enables the following applications: assisted living in the case of fall detection, yoga pose identification, character animation, and an autonomous drone control system. The rapid development of AI-based posture estimation algorithms for picture recognition has resulted in the availability of quick and dependable solutions for recognizing the human body joint in collected films. One major issue in human posture assessment is the system’s capacity to perform with high accuracy in real-time under shifting ambient conditions. The ultimate goal of the proposed transfer learning-based posture estimation assignment is to achieve real-time speed with virtually no drop accuracy. In this research paper, assisted living program (ALP) is implemented by using a single-shot deep estimation network and a pose key points angular feature. Experimental results show that transfer learning-based pose identifies and estimates posture with a frame rate of about 30 frames per second and a detection accuracy rate of 96.81%
Analyzing the Trade-offs between Runtime and Accuracy in Classification Algorithms for Natural Language Processing
Full text linkThis research aims to analyze the trade-offs between runtime and accuracy in classification algorithms for Natural Language Processing (NLP) and propose an optimization framework for balancing these trade-offs. The study employs a quantitative approach and evaluates the performance of different classification algorithms using metrics such as precision, recall, F1-score, and AUC. The population for this study is all publicly available datasets for NLP classification, and the data is collected using open-source NLP tools. The study shows that certain classification algorithms such as Random Forest, Decision Trees, Naive Bayes, SVM, or Neural Networks perform better than others in terms of both runtime and accuracy. However, some algorithms are faster but less accurate, while others are slower but more accurate. The analysis provided insights into how the choice of algorithm affects the trade-offs between runtime and accuracy in NLP. Based on the results, an optimization framework is proposed that can assist researchers and practitioners in NLP to choose the optimal algorithm for a given task and dataset, considering the desired balance between runtime and accuracy. This research provides valuable insights into the trade-offs between runtime and accuracy in NLP classification algorithms and proposes a framework that can help researchers make informed decisions about which algorithm to choose