International Journal of Intelligent Systems and Applications最新文献

Infrared Images Spectra Multi-class Classification Model Based on Deep Learning 基于深度学习的红外图像光谱多类分类模型

International Journal of Intelligent Systems and Applications Pub Date : 2024-08-08 DOI: 10.5815/ijisa.2024.04.02

Asmaa S. Abdo, Kamel K. Mohammed, Rania Ahmed, Heba Alshater, Samar A. Aly, Ashraf Darwish, A. Hassanein

{"title":"Infrared Images Spectra Multi-class Classification Model Based on Deep Learning","authors":"Asmaa S. Abdo, Kamel K. Mohammed, Rania Ahmed, Heba Alshater, Samar A. Aly, Ashraf Darwish, A. Hassanein","doi":"10.5815/ijisa.2024.04.02","DOIUrl":"https://doi.org/10.5815/ijisa.2024.04.02","url":null,"abstract":"The classification of Fourier Transform Infrared spectra images is crucial in chemometrics. This paper proposes an efficient model based on deep learning approaches for enhancement and classification of the Fourier Transform Infrared Spectra (FTIR) images. The proposed model integrates three deep learning models including ResNet101, EfficientNetB0, and Wavelet Scattering transform (WST) to extract several features from FTIR. Then the obtained features were fused in conjunction with standard statistical feature extraction. It followed by a subsequent classification phase that employs a Convolutional Neural Network (CNN) architecture, which demonstrates high accuracy in classifying the infrared spectra images into six different classes of ligands and their metal complexes. During the training phase, the network’s weights are iteratively updated using the Adam optimization algorithm. This model addresses the challenge of small and imbalanced datasets through an image oversampling process. Using random over-sampling technique, it enhances the training process and overall classification performance. The extracted features were analyzed using t-distributed Stochastic Neighbor Embedding (t-SNE) to visualize high-dimensional data in two dimensions. The results of the proposed model show high classification accuracy of 0.91%, low error rate of 0.08%, a sensitivity of 0.89% and a precision of 0.89%, false positive rate of 0.01%, F1 score of 0.89, Matthews Correlation Coefficient of 0.87 and Kappa of 0.68.","PeriodicalId":516885,"journal":{"name":"International Journal of Intelligent Systems and Applications","volume":"23 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141925764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Product Defect Detection Using Deep Learning 利用深度学习检测产品缺陷

International Journal of Intelligent Systems and Applications Pub Date : 2024-08-08 DOI: 10.5815/ijisa.2024.04.03

Venkatesh Khemlapure, Ashwini Patil, Nikita Chavan, Nisha Mali

{"title":"Product Defect Detection Using Deep Learning","authors":"Venkatesh Khemlapure, Ashwini Patil, Nikita Chavan, Nisha Mali","doi":"10.5815/ijisa.2024.04.03","DOIUrl":"https://doi.org/10.5815/ijisa.2024.04.03","url":null,"abstract":"To maximize production efficiency, product quality control is paying more attention to the quick and reliable automated quality visual inspection. Product defect detection is a critical part of the inspection process. Manual defect detection has a lot of flaws that can be overcome using a deep learning approach. In this paper we have proposed and implemented the deep learning models to detect defects in the manufactured product. Two types of classification, i.e., binary and multiclass classification, is done using CNN, AlexNet, and YOLO algorithms. For the binary classification which is just used to check whether there is a defect in the product, we have proposed three different architectures of CNN, out of which the third CNN model gave 99.44% and 97.49% for training and testing, respectively. We also tested the AlexNet model and got accuracy of 97.6%. And for the multiclass classification that is used for identification of type(s) of defects, the YOLOv8 model is proposed and implemented, which gives better results by attaining a remarkable accuracy of 98.7% for multiclass classification. We also designed and developed the Android Application, which is used on the field for defect detection in the manufacturing industry.","PeriodicalId":516885,"journal":{"name":"International Journal of Intelligent Systems and Applications","volume":"47 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141927958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

TourMate: A Personalized Multi-factor Based Tourist Place Recommendation System Using Machine Learning TourMate：使用机器学习的基于多因素的个性化旅游景点推荐系统

International Journal of Intelligent Systems and Applications Pub Date : 2024-08-08 DOI: 10.5815/ijisa.2024.04.04

Azmain Abid Khan, Mahfuzulhoq Chowdhury

{"title":"TourMate: A Personalized Multi-factor Based Tourist Place Recommendation System Using Machine Learning","authors":"Azmain Abid Khan, Mahfuzulhoq Chowdhury","doi":"10.5815/ijisa.2024.04.04","DOIUrl":"https://doi.org/10.5815/ijisa.2024.04.04","url":null,"abstract":"Building a personalized travel recommendation system is important to enhance the satisfaction and experience of travelers. Due to the lack of an efficient online-based tourist assistance system, tourists have faced several challenges in Bangladesh, such as difficulties in planning their trips and making informed decisions. To overcome the existing challenges, in this paper, a prediction model has been developed to predict the suitability of a travel destination based on the user’s preferences and some other relevant factors. Then the system offers personalized recommendations for the best local places to visit, hotels to stay in, transportation services, and travel agencies with the necessary details. This paper utilizes various machine learning classification algorithms to predict the best-suited travel destinations and local tourist spot recommendations for users based on their budget and preferences. The examined results verified that the random forest algorithm provides the best accuracy of 98 percent and is used for tourist place eligibility prediction. The user rating analysis visualized that the proposed mobile application received satisfactory remarks from more than 60 percent of reviewers regarding its effectiveness.","PeriodicalId":516885,"journal":{"name":"International Journal of Intelligent Systems and Applications","volume":"38 19","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141928380","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Detecting Sarcasm Text in Sentiment Analysis Using Hybrid Machine Learning Approach 使用混合机器学习方法检测情感分析中的讽刺文本

International Journal of Intelligent Systems and Applications Pub Date : 2024-08-08 DOI: 10.5815/ijisa.2024.04.05

Neha Singh, U. C. Jaiswal, Ritu Singh

引用次数: 0

A Self-driving Car Controller Module Based on Rule Base Reasoner 基于规则推理器的自动驾驶汽车控制器模块

International Journal of Intelligent Systems and Applications Pub Date : 2024-06-08 DOI: 10.5815/ijisa.2024.03.05

Anik Kumar Saha, M. Razzaque

引用次数: 0

Performance Evaluation of Lagerre Transform and Neural Network-based Cryptographic Techniques for Network Security 基于拉格瑞变换和神经网络的网络安全密码技术性能评估

International Journal of Intelligent Systems and Applications Pub Date : 2024-06-08 DOI: 10.5815/ijisa.2024.03.01

L. A. Akinyemi, Bukola H. Akinwole

{"title":"Performance Evaluation of Lagerre Transform and Neural Network-based Cryptographic Techniques for Network Security","authors":"L. A. Akinyemi, Bukola H. Akinwole","doi":"10.5815/ijisa.2024.03.01","DOIUrl":"https://doi.org/10.5815/ijisa.2024.03.01","url":null,"abstract":"As the world evolves day by day with new technologies, there is a need to design a secure network in such a way that intruders and unauthorized persons should not have access to the network as well as information regarding the personnel in any firm. In this study, a new cryptographic technique for securing data transmission based on the Laplace-Laguerre polynomial (LLP) is developed and compared to an existing auto-associative neural network technique (AANNT).The performance of the LLPT and AANNT was tested with some selected files in a MATLAB environment and the results obtained provided comparative information (in respect of AANNT versus LLPT) as follows: encryption time (1.67 ms versus 3.9931s), decryption time (1.833 ms versus 2.1172s), throughput (26.2975 Kb/s versus 0.01098 Kb/s), memory consumption (3.349 KB versus 15.958 KB). From the compared results, it shows that AANNT offers a faster processing time, higher throughput, and takes up les memory space than the LLPT. However, cryptanalysis of the AANNT is possible if the network's weight and design are known; hence, the technique is unreliable for ensuring the data integrity and confidentiality of encrypted data. The proposed LLP cryptographic algorithm is designed to provide a higher security level by making LLP algorithm computationally tedious to invert using the standard Laplace transform inversion method. When compared to the AANN-based cryptographic technique, cracking the algorithm to uncover the encryption key takes time. This shows the strength and robustness of the proposed LLP cryptographic algorithm against attacks, as well as its suitability for solving the problem of data privacy and security when compared to the AANN-based cryptographic.","PeriodicalId":516885,"journal":{"name":"International Journal of Intelligent Systems and Applications","volume":" 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141369561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hybrid Deep Optimal Network for Recognizing Emotions Using Facial Expressions at Real Time 利用面部表情实时识别情绪的混合深度优化网络

International Journal of Intelligent Systems and Applications Pub Date : 2024-06-08 DOI: 10.5815/ijisa.2024.03.04

Rakshith M. D., H. Kenchannavar

引用次数: 0

A Hyper-chaotic Medical Image Encryption with Optimized Key Value 具有优化密钥值的超混乱医学图像加密技术

International Journal of Intelligent Systems and Applications Pub Date : 2024-06-08 DOI: 10.5815/ijisa.2024.03.02

Subhajit Das, M. Sanyal

{"title":"A Hyper-chaotic Medical Image Encryption with Optimized Key Value","authors":"Subhajit Das, M. Sanyal","doi":"10.5815/ijisa.2024.03.02","DOIUrl":"https://doi.org/10.5815/ijisa.2024.03.02","url":null,"abstract":"This article delves into a medical image encryption/decryption method based on hyper chaotic dynamics and genetic algorithms. The proposed algorithm boasts simplicity in implementation, featuring straightforward operations that render it well-suited for real-time applications while elevating its security measures. Leveraging the sensitivity of chaotic behaviour to initial conditions, a genetic algorithm is employed to select optimal initial conditions for the 5D multi-wing hyper-chaotic system. Initially, a secret key generation method based on the input image is applied, followed by stages of diffusion and encryption utilizing the chaotic system. The secret key undergoes optimization through a genetic algorithm, considering specific parameters within the encrypted image as encryption factors. Subsequently, the encrypted image with the optimized secret key is finalized, serving as the basis for decrypting the cipher image. The proposed method undergoes simulation, testing, and comparison against other image encryption algorithms. Both experimental results and computer simulations affirm the robustness of this cryptographic system, showcasing a significant key space value (2^256), high key sensitivity (Number of Pixels Change Rate: NPCR > 99.55%, Unified Average Changing Intensity: UACI > 33.37%), and its ability to fend off various types of attack.","PeriodicalId":516885,"journal":{"name":"International Journal of Intelligent Systems and Applications","volume":" 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141369541","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Proposed Stacked Machine Learning Model to Predict the Survival of a Patient with Heart Failure 预测心力衰竭患者存活率的堆叠式机器学习模型提案

International Journal of Intelligent Systems and Applications Pub Date : 2024-06-08 DOI: 10.5815/ijisa.2024.03.03

Md. Raihan Mahmud, Dip Nandi, Md. Shamsur Rahim, Christe Antora Chowdhury

引用次数: 0

From Nature to UAV - A Study on Collision Avoidance in Bee Congregation 从大自然到无人飞行器--关于蜜蜂群落避免碰撞的研究

International Journal of Intelligent Systems and Applications Pub Date : 2024-06-08 DOI: 10.5815/ijisa.2024.03.06

Nahin Hossain Uday, Md. Zahid Hasan, Rejwan Ahmed, Md. Mahmudur Rahman, Abhijit Bhowmik, D. Karmaker

{"title":"From Nature to UAV - A Study on Collision Avoidance in Bee Congregation","authors":"Nahin Hossain Uday, Md. Zahid Hasan, Rejwan Ahmed, Md. Mahmudur Rahman, Abhijit Bhowmik, D. Karmaker","doi":"10.5815/ijisa.2024.03.06","DOIUrl":"https://doi.org/10.5815/ijisa.2024.03.06","url":null,"abstract":"Insects engage in a variety of survival-related activities, including feeding, mating, and communication, which are frequently motivated by innate impulses and environmental signals. Social insects, such as ants and bees, exhibit complex collective behaviors. They carry out well-organized duties, including defense, nursing, and foraging, inside their colonies. For analyzing the behavior of any living entity, we selected honeybees (Apis Mellifera) and worked on a small portion of it. We have captured the video of honeybees flying close to a hive (human-made artificial hive) while the entrance was temporarily sealed which resulted in the” bee cloud”. An exploration of the flight trajectories executed and a 3D view of the” bee cloud” constructed. We analyzed the behaviors of honeybees, especially on their speed and distance. The results showed that the loitering honeybees performed turns that are fully coordinated, and free of sideslips so thus they made no collision between themselves which inspired us to propose a method for avoiding collision in unmanned aerial vehicle. This paper gives the collective behavioral information and analysis report of the small portion of data set (honeybees), that bee maintains a safe distance (35mm) to avoid collision.","PeriodicalId":516885,"journal":{"name":"International Journal of Intelligent Systems and Applications","volume":" 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141369890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0