�ݺ�ߣshows by User: ijaia

MOVIE RECOMMENDATION SYSTEM BASED ON MACHINE LEARNING USING PROFILING

ijaia@slideshare.net(ijaia) — Tue, 11 Feb 2025 14:35:40 GMT

With the increasing amount of data available, recommendation systems are important for helping users find relevant content. This paper introduces a movie recommendation system that uses user profiles and machine learning techniques to improve the user experience by offering personalized suggestions. We tested different machine learning methods, including k nearest neighbors (KNN), support vector machines (SVM), and neural networks. We used several datasets, such as MovieLens and Netflix Prize, to check how accurate the recommendations were and how satisfied users were with them.

AE-ViT: Token Enhancement for Vision Transformers via CNN-based Autoencoder Ensembles

ijaia@slideshare.net(ijaia) — Tue, 11 Feb 2025 14:33:56 GMT

While Vision Transformers (ViTs) have revolutionized computer vision with their exceptional results, they struggle to balance processing speed with visual detail preservation. This tension becomes particularly evident when implementing larger patch sizes. Although larger patches reduce computational costs, they lead to significant information loss during the tokenization process. We present AE-ViT, a novel architecture that leverages an ensemble of autoencoders to address this issue by introducing specialized latent tokens that integrate seamlessly with standard patch tokens, enabling ViTs to capture both global and fine-grained features. Our experiments on CIFAR-100 show that AE-ViT achieves a 23.67% relative accuracy improvement over the baseline ViT when using 16×16 patches, effectively recovering fine-grained details typically lost with larger patches. Notably, AE-ViT maintains relevant performance (60.64%) even at 32×32 patches. We further validate our method on CIFAR-10, confirming consistent benefits and adaptability across different datasets. Ablation studies on ensemble size and integration strategy underscore the robustness of AE-ViT, while computational analysis shows that its efficiency scales favorably with increasing patch size. Overall, these findings suggest that AE-ViT provides a practical solution to the patch-size dilemma in ViTs by striking a balance between accuracy and computational cost, all within a simple, end-to-end trainable design.

EXPLORING THE INTEGRATION OF ARTIFICIAL INTELLIGENCE INTO THE FUNCTIONS OF AN ACCOUNTING DEPARTMENT

ijaia@slideshare.net(ijaia) — Tue, 11 Feb 2025 14:32:07 GMT

Artificial intelligence is transforming various fields, including accounting, by representing a significant technological innovation. Artificial intelligence combines hardware and software to simulate human cognitive processes, enabling machines to perform complex tasks such as learning, reasoning, and decision-making. This paper explores the advantages and disadvantages of integrating artificial intelligence into accounting practices. While artificial intelligence presents numerous benefits for accountants, it also introduces challenges that must be addressed. The paper also contributes to the expanding knowledge base on artificial intelligence in accounting by offering practical recommendations for accountants on effectively adopting artificial intelligence. Even with the challenges presented from integrating artificial intelligence in accounting, such integration offers considerable efficiency gains. This positions artificial intelligence as a strategic investment for organizations aiming to improve the performance and effectiveness of their accounting departments.

DIVERGENT ENSEMBLE NETWORKS : IMPROVING PREDICTIVE RELIABILITY AND COMPUTATIONAL EFFICIENCY

ijaia@slideshare.net(ijaia) — Tue, 11 Feb 2025 14:30:42 GMT

The effectiveness of ensemble learning in improving prediction accuracy and estimating uncertainty is well- established. However, conventional ensemble methods often grapple with high computational demands and redundant parameters due to independent network training. This study introduces the Divergent Ensemble Network (DEN), a novel framework designed to optimize computational efficiency while maintaining prediction diversity. DEN achieves superior predictive reliability with reduced parameter overhead by leveraging shared representation learning and independent branching. Our results demonstrate the efficacy of DEN in balancing accuracy, uncertainty estimation, and scalability, making it a robust choice for real- world applications.

AI-BASED EARLY PREDICTION AND INTERVENTION FOR STUDENT ACADEMIC PERFORMANCE IN HIGHER EDUCATION

ijaia@slideshare.net(ijaia) — Tue, 11 Feb 2025 14:29:29 GMT

Accurately identifying at-risk students in higher education is crucial for timely interventions. This study presents an AI-based solution for predicting student performance using machine learning classifiers. A dataset of 208 student records from the past two years was preprocessed, and key predictors such as midterm grades, previous semester GPA, and cumulative GPA were selected using information gain evaluation. Multiple classifiers, including Support Vector Machine (SVM), Decision Tree, Naive Bayes, Artificial Neural Networks (ANN), and k-Nearest Neighbors (k-NN), were evaluated through 10-fold cross- validation. SVM demonstrated the highest performance with an accuracy of 85.1% and an F2 score of 94.0%, effectively identifying students scoring below 65% (GPA < 2.0). The model was implemented in a desktop application for educators, providing both class-level and individual-level predictions. This user- friendly tool enables instructors to monitor performance, predict outcomes, and implement timely interventions to support struggling students. The study highlights the effectiveness of machine learning in enhancing academic performance monitoring and offers a scalable approach for AI-driven educational tools.

LEVERAGING NAIVE BAYES FOR ENHANCED SURVIVAL ANALYSIS IN BREAST CANCER

ijaia@slideshare.net(ijaia) — Wed, 14 Aug 2024 12:16:07 GMT

The study aims to predict breast cancer survival using Naïve Bayes techniques by comparing different machine learning models on a comprehensive dataset of patient records. The main classification groups were survival and non-survival. The objective was to assess the performance of the Naïve Bayes classifier in the field of data mining and to achieve significant results in survival classification, aligning with current academic research. The Naive Bayes classifier attained an average accuracy of 91.08%, indicating consistent performance, though with some variability across different folds. Conversely, Logistic Regression achieved a higher accuracy of 94.84%, demonstrating proficiency in recognizing instances of class 1, yet encountering challenges with class 0.The Decision Tree model, with an accuracy of 93.42%, exhibited similar performance patterns. With an accuracy of 95.68%, Random Forest surpassed the Decision Tree. Nonetheless, all models encountered challenges in accurately classifying instances of class 0. The Naive Bayes algorithm was juxtaposed with K-Nearest Neighbors (KNN) and Support Vector Machines (SVM). Future research aims to enhance prediction models with novel methods and tackle the challenge of accurately identifying instances of class 0.

Comparing LLMs Using a Unified Performance Ranking System

ijaia@slideshare.net(ijaia) — Wed, 14 Aug 2024 12:15:12 GMT

Large Language Models (LLMs) have transformed natural language processing and AI-driven applications. These advances include OpenAI’s GPT, Meta’s LLaMA, and Google’s PaLM. These advances have happened quickly. Finding a common metric to compare these models presents a substantial barrier for researchers and practitioners, notwithstanding their transformative power. This research proposes a novel performance ranking metric to satisfy the pressing demand for a complete evaluation system. Our statistic comprehensively compares LLM capacities by combining qualitative and quantitative evaluations. We examine the advantages and disadvantages of top LLMs by thorough benchmarking, providing insightful information on how they compare performance. This project aims to progress the development of more reliable and effective language models and make it easier to make well-informed decisions when choosing models.

EXPLORING AI APPLICATIONS FOR ADDRESSING ALTERNATIVE CONCEPTIONS IN TEACHING PHYSICS: FOCUS ON ELECTRICAL CIRCUITS AT THE SECONDARY LEVEL

ijaia@slideshare.net(ijaia) — Wed, 14 Aug 2024 12:14:29 GMT

This article introduces our initial foray into a comprehensive, long-term study on the learning practices of high school students using chatbots as educational tools. In this section, we explore the correlation between the logic underlying the responses generated by two chatbots for problems related to electrical circuits and the well-known alternative conceptions prevalent in the field of science education. To achieve this, we employed a methodology involving the presentation of ten questions to the chatbots, followed by an analysis of their answers in conjunction with established knowledge in physics and the Theory of Conceptual Fields. The objective was to bridge the gap between AI-generated responses and human responses. Our primary findings reveal a close resemblance between these two groups. This initial endeavor lays the foundation for developing an investigative methodology that will facilitate a comprehensive understanding and categorization of the various forms of interaction between students and chatbots.

ENHANCE THE DETECTION OF DOS AND BRUTE FORCE ATTACKS WITHIN THE MQTT ENVIRONMENT THROUGH FEATURE ENGINEERING AND EMPLOYING AN ENSEMBLE TECHNIQUE

ijaia@slideshare.net(ijaia) — Wed, 14 Aug 2024 12:13:27 GMT

The rapid development of the Internet of Things (IoT) environment has introduced unprecedented levels of connectivity and automation. The Message Queuing Telemetry Transport (MQTT) protocol has become recognized in IoT applications due to its lightweight and efficient features; however, this simplicity also renders MQTT vulnerable to multiple attacks that can be launched against the protocol, including denial of service (DoS) and brute-force attacks. This study aims to improve the detection of intrusion DoS and bruteforce attacks in an MQTT traffic intrusion detection system (IDS). Our approach utilizes the MQTT dataset for model training by employing effective feature engineering and ensemble learning techniques. Following our analysis and comparison, we identified the top 10 features demonstrating the highest effectiveness, leading to improved model accuracy. We used supervised machine learning models, including Random Forest, Decision Trees, k-Nearest Neighbors, and XGBoost, in combination with ensemble classifiers. Stacking, voting, and bagging ensembles utilize these four supervised machinelearning methods to combine models. This study's results illustrate the proposed technique's efficacy in enhancing the accuracy of detecting DoS and brute-force attacks in MQTT traffic. Stacking and voting classifiers achieved the highest accuracy of 0.9538. Our approach outperforms the most recent study that utilized the same dataset.

CLASSIFYING EMERGENCY PATIENTS INTO FAST-TRACK AND COMPLEX CASES USING MACHINE LEARNING

ijaia@slideshare.net(ijaia) — Thu, 13 Jun 2024 11:45:49 GMT

Emergency medicine is a lifeline specialty at hospitals that patients head to for various reasons, including serious health problems, traumas, and adventitious conditions. Emergency departments are restricted to limited resources and personnel, complicating the optimal handling of all received cases. Therefore, crowded waiting areas and long waiting durations result. In this research, the databases of MIMIC-IV-ED and MIMIC-IV were utilized to obtain records of patients who visited the Beth Israel Deaconess Medical Center in the USA. Triage data, dispositions, and length of stay of these individuals were extracted. Subsequently, the urgency of these cases was inferred based on standards stated in the literature and followed in developed countries. A comparative framework using four different machine learning algorithms besides a reference model was developed to classify these patients into complex and fast–track categories. Moreover, the relative importance of employed predictors was determined. This study proposes an approach to deal with non-urgent visits and lower overall waiting times at the emergency by utilizing the powers of machine learning to identify high-severity and low-severity patients. Given the provision of the required resources, the proposed classification would help improve the overall throughput and patient satisfaction.

SYSTEMATIC REVIEW OF MODELS USEDTO HANDLE CLASS IMBALANCE IN ANOMALY DETECTION FOR ENERGY CONSUMPTION

ijaia@slideshare.net(ijaia) — Thu, 13 Jun 2024 11:43:54 GMT

The widespread integration of Smart technologies into energy consumption systems has brought about a transformative shift in monitoring and managing electricity usage. The imbalanced nature of anomaly data often results in suboptimal performance in detecting rare anomalies. This literature review analyzes models designed to address this challenge. The methodology involves a systematic literature review based on the five-step framework proposed by Khan, encompassing framing research questions, identifying relevant literature, assessing article quality, conducting a critical review, and interpreting results. The findings show that classical machine learning models like Support Vector Machines (SVM) and Random Forests (RF) are commonly used. In conclusion, classical machine learning models like SVM and RF struggle to recognize rare anomalies, while deep learning models, notably Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM), show promise for automatically learning elaborate representations and improving performance while dealing with class imbalance.

IS PROMPT ENGINEERING A PROFESSION?.....

ijaia@slideshare.net(ijaia) — Thu, 13 Jun 2024 11:42:30 GMT

Prompt Engineering, the systematic design and construction of prompts for human-AI interaction, raises questions regarding its professional status. This paper examines Prompt Engineering and evaluates whether it qualifies as a distinct profession. Through an analysis of its defining characteristics, including specialized skills, ethical considerations, and societal impact, this study explores the parallels between Prompt Engineering and established professions. Drawing on examples from various fields, it argues for the recognition of Prompt Engineering as a legitimate profession. By addressing the complexities of human-AI interaction and the evolving demands of technology, this research contributes to the ongoing discourse on the professionalization of emerging disciplines.

PHOTOQR: A NOVEL ID CARD WITH AN ENCODED VIEW

ijaia@slideshare.net(ijaia) — Thu, 13 Jun 2024 11:41:42 GMT

There is an increasing interest in developing techniques to identify and assess data to allow an easy and continuous access to resources, services or places that require thorough ID control. Usually, in order to give access to these resources, different kinds of documents are mandatory. In order to avoid forgeries without the need of extra credentials, a new system –named photoQR, is here proposed. This system is based on a ID card having two objects: one person’s picture (pre-processed via blur and/or swirl techniques) and one QR code containing embedded data related to the picture. The idea is that the picture and the QR code can assess each other by a proper hash value in the QR. The QR without the picture cannot be assessed and vice versa. An open source prototype of the photoQR system has been implemented in Python and can be used both in offline and real-time environments, which effectively combines security concepts and image processing algorithms to obtain data assessment.

DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODEL

ijaia@slideshare.net(ijaia) — Thu, 13 Jun 2024 11:40:50 GMT

As digital technology becomes more deeply embedded in power systems, protecting the communication networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3) represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities. Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network (CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to train and test our model. The results of our experiments show that our CNN-LSTM method is much better at finding smart grid intrusions than other deep learning algorithms used for classification. In addition, our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection accuracy rate of 99.50%.

INFORMATION EXTRACTION FROM PRODUCT LABELS: A MACHINE VISION APPROACH

ijaia@slideshare.net(ijaia) — Tue, 09 Apr 2024 16:27:58 GMT

This research tackles the challenge of manual data extraction from product labels by employing a blend of computer vision and Natural Language Processing (NLP). We introduce an enhanced model that combines Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) in a Convolutional Recurrent Neural Network (CRNN) for reliable text recognition. Our model is further refined by incorporating the Tesseract OCR engine, enhancing its applicability in Optical Character Recognition (OCR) tasks. The methodology is augmented by NLP techniques and extended through the Open Food Facts API (Application Programming Interface) for database population and text-only label prediction. The CRNN model is trained on encoded labels and evaluated for accuracy on a dedicated test set. Importantly, our approach enables visually impaired individuals to access essential information on product labels, such as directions and ingredients. Overall, the study highlights the efficacy of deep learning and OCR in automating label extraction and recognition.

RESEARCH ON FUZZY C- CLUSTERING RECURSIVE GENETIC ALGORITHM BASED ON CLOUD COMPUTING BAYES FUNCTION

ijaia@slideshare.net(ijaia) — Tue, 09 Apr 2024 16:27:02 GMT

Aiming at the problems of poor local search ability and precocious convergence of fuzzy C-cluster recursive genetic algorithm (FOLD++), a new fuzzy C-cluster recursive genetic algorithm based on Bayesian function adaptation search (TS) was proposed by incorporating the idea of Bayesian function adaptation search into fuzzy C-cluster recursive genetic algorithm. The new algorithm combines the advantages of FOLD++ and TS. In the early stage of optimization, fuzzy C-cluster recursive genetic algorithm is used to get a good initial value, and the individual extreme value pbest is put into Bayesian function adaptation table. In the late stage of optimization, when the searching ability of fuzzy C-cluster recursive genetic is weakened, the short term memory function of Bayesian function adaptation table in Bayesian function adaptation search algorithm is utilized. Make it jump out of the local optimal solution, and allow bad solutions to be accepted during the search. The improved algorithm is applied to function optimization, and the simulation results show that the calculation accuracy and stability of the algorithm are improved, and the effectiveness of the improved algorithm is verified.

EMPLOYEE ATTRITION PREDICTION USING MACHINE LEARNING MODELS: A REVIEW PAPER

ijaia@slideshare.net(ijaia) — Tue, 09 Apr 2024 16:25:59 GMT

Employee attrition refers to the decrease in staff numbers within an organization due to various reasons. As it has a negative impact on long-term growth objectives and workplace productivity, firms have recognized it as a significant concern. To address this issue, organizations are increasingly turning to machine-learning approaches to forecast employee attrition rates. This topic has gained significant attention from researchers, especially in recent times. Several studies have applied various machine-learning methods to predict employee attrition, producing different results depending on the employed methods, factors, and datasets. However, there has been no comprehensive comparative review of multiple studies applying machinelearning models to predict employee attrition to date. Therefore, this study aims to fill this gap by providing an overview of research conducted on applying machine learning to predict employee attrition from 2019 to February 2024. A literature review of relevant studies was conducted, summarized, and classified. Most studies agree on conducting comparative experiments with multiple predictive models to determine the most effective one. From this literature survey, the RF algorithm and XGB ensemble method are repeatedly the best-performing, outperforming many other algorithms. Additionally, the application of deep learning to employee attrition prediction issues also shows promise. While there are discrepancies in the datasets used in previous studies, it is notable that the dataset provided by IBM is the most widely utilized. This study serves as a concise review for new researchers, facilitating their understanding of the primary techniques employed in predicting employee attrition and highlighting recent research trends in this field. Furthermore, it provides organizations with insight into the prominent factors affecting employee attrition, as identified by studies, enabling them to implement solutions aimed at reducing attrition rates.

THE TRANSFORMATION RISK-BENEFIT MODEL OF ARTIFICIAL INTELLIGENCE:BALANCING RISKS AND BENEFITS THROUGH PRACTICAL SOLUTIONS AND USE CASES

ijaia@slideshare.net(ijaia) — Tue, 09 Apr 2024 16:24:58 GMT

This paper summarizes the most cogent advantages and risks associated with Artificial Intelligence from an in-depth review of the literature. Then the authors synthesize the salient risk-related models currently being used in AI, technology and business-related scenarios. Next, in view of an updated context of AI along with theories and models reviewed and expanded constructs, the writers propose a new framework called “The Transformation Risk-Benefit Model of Artificial Intelligence” to address the increasing fears and levels of AIrisk. Using the model characteristics, the article emphasizes practical and innovative solutions where benefitsoutweigh risks and three use cases in healthcare, climate change/environment and cyber security to illustrate unique interplay of principles, dimensions and processes of this powerful AI transformational model.

AN IMPROVED MT5 MODEL FOR CHINESE TEXT SUMMARY GENERATION

ijaia@slideshare.net(ijaia) — Thu, 08 Feb 2024 09:55:21 GMT

Complicated policy texts require a lot of effort to read, so there is a need for intelligent interpretation of Chinese policies. To better solve the Chinese Text Summarization task, this paper utilized the mT5 model as the core framework and initial weights. Additionally, In addition, this paper reduced the model size through parameter clipping, used the Gap Sentence Generation (GSG) method as unsupervised method, and improved the Chinese tokenizer. After training on a meticulously processed 30GB Chinese training corpus, the paper developed the enhanced mT5-GSG model. Then, when fine-tuning the Chinese Policy text, this paper chose the idea of “Dropout Twice”, and innovatively combined the probability distribution of the two Dropouts through the Wasserstein distance. Experimental results indicate that the proposed model achieved Rouge-1, Rouge-2, and Rouge-L scores of 56.13%, 45.76%, and 56.41% respectively on the Chinese policy text summarization dataset.

A MACHINE LEARNING ENSEMBLE MODEL FOR THE DETECTION OF CYBERBULLYING

ijaia@slideshare.net(ijaia) — Thu, 08 Feb 2024 09:54:25 GMT

The pervasive use of social media platforms, such as Facebook, Instagram, and X, has significantly amplified our electronic interconnectedness. Moreover, these platforms are now easily accessible from any location at any given time. However, the increased popularity of social media has also led to cyberbullying.It is imperative to address the need for finding, monitoring, and mitigating cyberbullying posts on social media platforms. Motivated by this necessity, we present this paper to contribute to developing an automated system for detecting binary labels of aggressive tweets.Our study has demonstrated remarkable performance compared to previous experiments on the same dataset. We employed the stacking ensemble machine learning method, utilizing four various feature extraction techniques to optimize performance within the stacking ensemble learning framework. Combining five machine learning algorithms,Decision Trees, Random Forest, Linear Support Vector Classification, Logistic Regression, and K-Nearest Neighbors into an ensemble method, we achieved superior results compared to traditional machine learning classifier models. The stacking classifier achieved a high accuracy rate of 94.00%, outperforming traditional machine learning models and surpassing the results of prior experiments that utilized the same dataset. The outcomes of our experiments showcased an accuracy rate of 0.94% in detection tweets as aggressive or non-aggressive.