IAES International Journal of Artificial Intelligence (IJ-AI)
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Supply chain efficiency transformation: analysis of raw material staff selection based on preference selection index
Get PDFIn the era of intense business globalization, supply chain management is becoming a vital key to improving the efficiency and competitiveness of enterprises. The selection of raw material supply staff is an important aspect of supply chain management, affecting smooth supply, efficiency and cost control. This research focuses on using the preference selection index (PSI) method in the selection of raw material supply staff. PSI is a tool that integrates data from multiple criteria in the selection process. The results show that PSI provides an effective evaluation in staff selection, identifies key variables that affect selection success and analyzes the impact of using PSI on supply chain efficiency and company productivity. This research fills the knowledge gap in the application of PSI in the context of raw material supply staff selection and contributes to the understanding of efficient and sustainable supply chain management. The results provide valuable insights for industries and organizations that depend on reliable raw material supply and demonstrate the potential to improve the overall staff selection process. The outcome of this study found that Muliyono received a PSI score of 0.9643 and was ranked first, while Ramli received a PSI score of 0.9548 and was ranked second
A new deep steganographic technique for hiding several secret images in one cover
Get PDFDeep learning has been integrated with image steganography to enhance steganographic security by automatically acquiring the ability to hide information. The issue with current models is that if the cover image is accessible, it is possible to expose the hidden information by simply calculating the differences between the cover image and the steganographic image. This paper introduces a novel image steganography model that utilizes convolutional neural network (CNN) to enhance the dissimulation and extraction capabilities. Specifically, we propose a model that hides two images in a single cover image. Before being hidden within the cover image, a random pixel image is generated and combined with the real secret image. Experimental results show that our proposed method is more effective and relevant
Hybrid semantic model based on machine learning for sentiment classification of consumer reviews
Get PDFDigital information is regularly produced from a variety of sources, including social media and customer service reviews. For the purpose of increasing customer happiness, this written data must be processed to extract user comments. Consumers typically share comments and thoughts about consumable items, technological goods, and services supplied for payment in the modern period of consumerism with simple access to social networking globe. Each object has a plethora of remarks or thoughts that demand special attention due to their sentimental worth, especially in the written portions. The goal of the current project is to do sentiment prediction on the Amazon Electronics, Kindle, and Gift Card datasets. In order to predict sentiment and evaluate utilizing many executions evaluates admitting accuracy, recall, and F1-score, a hybrid soft voting ensemble method that combines lexical and ensemble methodologies is proposed in this study. In addition to calculating a subjectivity score and sentiment score, this study also suggests a non-interpretive sentiment class label that may be used to assess the sign of the evaluations applying suggested method for sentiment categorization. The effectiveness of our suggested ensemble model is examined using datasets from Amazon customer product reviews, and we found an improvement of 2-5% in accuracy compared to the current state-of-the-art ensemble method
Artificial intelligence multilingual image-to-speech for accessibility and text recognition
Get PDFThe primary challenge for visually impaired and illiterate individuals is accessing and understanding visual content, which hinders their ability to navigate environments and engage with text-based information. This research addresses this problem by implementing an artificial intelligence (AI)-powered multilingual image-to-speech technology that converts text from images into audio descriptions. The system combines optical character recognition (OCR) and text-to-speech (TTS) synthesis, using natural language processing (NLP) and digital signal processing (DSP) to generate spoken outputs in various languages. Tested for accuracy, the system demonstrated high precision, recall, and an average accuracy rate of 0.976, proving its effectiveness in real-world applications. This technology enhances accessibility, significantly improving the quality of life for visually impaired individuals and offering scalable solutions for illiterate populations. The results also provide insights for refining OCR accuracy and expanding multilingual support
Effective task allocation in fog computing environments using fractional selectivity model
Get PDFIn recent scenario, fog computing is a new technology deployed between cloud computing systems and internet of things (IoT) devices to filter out important information from a massive amount of collected IoT data. Cloud computing offers several advantages, but also has the disadvantages of high latency and network congestion, when processing a vast amount of data collected from various devices and sources. For overcoming these problems in fog computing environments, an efficient model is proposed in this article for precise load balancing (LB). The proposed fractional selectivity model significantly handles LB in fog computing by reducing network bandwidth consumption, latency, task-waiting time, and also enhances the quality of experience. The proposed model allocates the required resources by eliminating sleepy, unreferenced, and long-time inactive services. The fractional selectivity model’s performance is investigated on three application scenarios, namely virtual reality (VR) game, electroencephalogram (EEG) healthcare, and toy game. The efficiency of the introduced model is analyzed on the basis of makespan, average resource utilization (ARU), load balancing level (LBL), total cost, delay, and energy consumption. Specifically, in comparison to the traditional task allocation models, the proposed model reduces almost 5 to 15% of the total cost and makespan time
GradeZen: automated grading ecosystem using deep learning for educational assessments
Get PDFThis study introduces a groundbreaking software solution poised to revolutionize grading procedures in higher education through advanced artificial intelligence and machine learning techniques. Leveraging cutting-edge technologies such as YOLOv8 for real-time object detection, transformer-based optical character recognition (TrOCR), and Mixtral 8x7B transformer models for complex data analysis, the software automates the grading process. By significantly reducing the time and effort required for manual grading, it aims to streamline educational practices while ensuring consistency and scalability. The study provides a comprehensive analysis of use cases, identifies key issues in current grading methods, and elucidates the rationale driving its development. This innovative approach holds immense promise for transforming educational practices, fostering student success through efficient and artificial intelligence assisted automated assessment methodologies
Comprehensive survey of automated plant leaf disease identification techniques: advancements, challenges, and future directions
Get PDFThis survey paper extensively researches into the domain of timely plant disease detection, crucial for alleviating agricultural losses and ensuring food security. It accentuates the significance of early identification in efficient disease management and informed agricultural decisions. Conventional manual methods, constrained by labor intensity and subjectivity, pave the way for investigating automated disease detection avenues, prominently leveraging image processing and deep learning techniques. In the subsequent exploration of related work, a panoramic view encompasses an array of methodologies, encompassing neural networks and convolutional neural networks (CNNs), paramount in automated disease detection. The synthesis of image processing intricacies, pre-processing strategies, and feature extraction paradigms alongside deep learning models is meticulously expounded. As the field advances, the paper accentuates lingering challenges in early-stage detection, alongside insightful solutions like data augmentation and sophisticated deep learning models. This survey paper culminates by underlining the dynamic trajectory of automated plant disease identification, accentuating its paramount role in upholding global food security
The main weaknesses of using Manhattan distance for solving sliding tile puzzles
Get PDFHeuristics are a big improvement over blind searching in pathfinding. The node's test, run, and finish time are reasonable. Artificial intelligence (AI) uses Manhattan distance (MD), a good and simple heuristic, in various subjects to reduce the number of exploring nodes while requiring fewer calculations. The MD heuristics examined approximately 25 times fewer states than the blind search. Unfortunately, can’t reach the goal of pathfinding when the domain size increases, as it becomes similar to brute force or blind search algorithm results. Previous studies have concentrated on MD's weakness, specifically its low bound value for calculation results, and attempted to improve this value in various ways. Unfortunately, to our knowledge, none of the presented research has been able to find the optimal path for all slide tile puzzle sizes. This work discusses the detailed reasons for the low bound value and other related factors that contribute to its weakness. This paper discovered that the distribution of MD values within the domain, not lowbound values, is the critical issue that complicates the search. The MD's summation method for all tiles has an impact on the calculated duplication values. The total number of nodes in the optimal path also affects the search performance
Improvisation in detection of pomegranate leaf disease using transfer learning techniques
Get PDFTo provide the growing world population with food and satisfy their fundamental requirements, agriculture is a vital industry. The cultivation of cereals and vegetables is indispensable for both human sustenance and the worldwide economy. Many farmers in rural areas suffer substantial losses because they rely on manual monitoring of crops and lack sufficient information and disease detection methods. Digital farming techniques may provide a novel way to swiftly and simply identify illnesses in the leaves of plants. This article uses image processing and transfer learning techniques for identifying plant leaf ailments and taking preventative action in the agriculture business in order to address these problems. Global food security and agricultural productivity are seriously threatened by leaf disease. Crop losses may be considerably decreased, and crop output can be increased by promptly identifying and diagnosing leaf diseases. Deep learning can mitigate the adverse impact of artificially picking disease spot data, enhance objectivity in extracting plant disease traits, and expedite the advancement of new technologies. This article presents a novel approach using deep learning to diagnose leaf diseases. This article advances the development of efficient and successful techniques for recognizing and diagnosing leaf diseases, which will eventually aid farmers and maintain the security of the world's food supply
A smart grid fault detection using neuro-fuzzy deep learning algorithm
Get PDFThis paper proposes a novel data analysis framework that integrates deep learning with a binary neuro-fuzzy algorithm to address the problem of fault localization in smart power grids. In the first stage, a long short-term memory (LSTM) network is employed to train data samples collected from smart meters. The resulting learned features are subsequently utilized by an adaptive neuro-fuzzy inference system (ANFIS) for accurate fault detection and classification. Through this intelligent hybrid approach, multi-phase faults can be efficiently identified using a limited amount of data. The proposed method distinguishes itself by its capacity to rapidly train and test large datasets while maintaining high computational efficiency. To evaluate the performance of the model, an advanced simulation of the IEEE 123-node test feeder is conducted. The robustness and effectiveness of the proposed framework are validated using multiple performance metrics, including precision, recall, accuracy, F1-score, computational complexity, and the ROC curve. The results demonstrate that the proposed deep learning–based model significantly outperforms existing approaches in the literature, achieving a fault detection and classification precision of 99.99%