Egyptian Informatics Journal最新文献

Climate-resilient water management: Leveraging IoT and AI for sustainable agriculture

IF 5 3区计算机科学

Egyptian Informatics Journal Pub Date : 2025-04-26 DOI: 10.1016/j.eij.2025.100691

Syed Muhammad Zaigham Abbas Naqvi , Saddam Hussain , Muhammad Awais , Muhammad Naveed Tahir , Shoaib Rashid Saleem , Fuad A.M. Al-Yarimi , Mirjalol Ashurov , Oumaima Saidani , M.Ijaz Khan , Junfeng Wu , Zhang Wei , Jiandong Hu

{"title":"Climate-resilient water management: Leveraging IoT and AI for sustainable agriculture","authors":"Syed Muhammad Zaigham Abbas Naqvi , Saddam Hussain , Muhammad Awais , Muhammad Naveed Tahir , Shoaib Rashid Saleem , Fuad A.M. Al-Yarimi , Mirjalol Ashurov , Oumaima Saidani , M.Ijaz Khan , Junfeng Wu , Zhang Wei , Jiandong Hu","doi":"10.1016/j.eij.2025.100691","DOIUrl":"10.1016/j.eij.2025.100691","url":null,"abstract":"<div><div>Climate change is the phenomenon of permanent change in the environmental conditions of an area. However, it is now affecting the earth by causing a permanent seasonal shift. This seasonal shift is not only decreasing the yields of crops by shortening their growth duration but also critically affecting the water availability for irrigation purposes. This article addresses the irrigation management strategies to mitigate the impacts of climate changes using advance techniques like internet of things (IoT). IoT is the setup of smart sensory devices which are interconnected using internet. They collect the data from field and analyze using artificial intelligence based algorithmic models. The irrigation management strategies using the artificial intelligence (AI) to mitigate the climate change impacts by reducing the wastage of essential resources in the environment has not been adopted by many developed countries. This article briefly explained the applications of AI in smart agriculture. Manuscript further describes the idea to protect the agricultural system from water scarcity and flooding by the efficient use of sensors, IoT and AI by automating the traditional agricultural practices. Different variable rate applications, smart irrigation methods like weather-based smart irrigation and moisture-based smart irrigation have been discussed in this review. Different countries have adapted different technologies of smart irrigation which can mitigate climate changes effectively and a case study with this respect is discussed. Moreover, implementations of integrated neural network models with the decision support system of irrigation management strategies to decide the supply of water in the field in real-time have been discussed in this review.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"30 ","pages":"Article 100691"},"PeriodicalIF":5.0,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143876612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hybrid signal algorithm detection in NOMA 5G waveform: Transforming smart healthcare connectivity by reducing latency

IF 5 3区计算机科学

Egyptian Informatics Journal Pub Date : 2025-04-25 DOI: 10.1016/j.eij.2025.100677

Arun Kumar , Nishant Gaur , Aziz Nanthaamornphong

{"title":"Hybrid signal algorithm detection in NOMA 5G waveform: Transforming smart healthcare connectivity by reducing latency","authors":"Arun Kumar , Nishant Gaur , Aziz Nanthaamornphong","doi":"10.1016/j.eij.2025.100677","DOIUrl":"10.1016/j.eij.2025.100677","url":null,"abstract":"<div><div>The detection of hybrid signal algorithms in Non-Orthogonal Multiple Access (NOMA) 5G waveforms is changing the face of smart healthcare. The integration of NOMA allows multiple simultaneous connections in a given system, which significantly enhances spectral efficiency, ensuring unmatched communication between different medical devices and monitoring systems. Interference mitigation is guaranteed by the proper employment of hybrid signal algorithms that improve correct data interpretation, and are important for maintaining robust connectivity among healthcare facilities with heavy demands. These developments have overcome some of the key challenges in the domain of smart healthcare such as real-time data transmission for remote monitoring, telemedicine, and emergency response. Lowering latency and improving signal reliability will support rapid decision making and patient safety in critical situations. In this paper, we propose a hybrid signal detection algorithm that combines a zero-forcing equalizer (ZFE) and minimum mean square error (MMSE) for the NOMA-MIMO structure with Rician and Rayleigh channels, highlighting its role in empowering next-generation healthcare solutions through enhanced connectivity, reliability, and efficiency. For 16 × 16 and 64 × 64 MIMO-NOMA, the Bit error rate (BER) was evaluated and compared for Long Short-Term Memory (LSTM), ZFE, MMSE, and Maximum likelihood (ML) detection, and the proposed ZFE-MMSE algorithms. The simulation results revealed that the projected LSTM obtains a better BER at a low SNR with high complexity. However, ZFE-MMSE effectively detects the signal at a low SNR, outperforming contemporary algorithms at complexity similar to MMSE and ZFE, and can enhance the latency performance for smart health care applications.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"30 ","pages":"Article 100677"},"PeriodicalIF":5.0,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143870578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A novel hybrid fuzzy logic and federated learning framework for enhancing cybersecurity and fraud detection in IoT-enabled metaverse transactions

IF 5 3区计算机科学

Egyptian Informatics Journal Pub Date : 2025-04-24 DOI: 10.1016/j.eij.2025.100668

Amjad Rehman , Kamran Ahmad Awan , Amal Al-Rasheed , Anees Ara , Fahad F. Alruwaili , Shaha Al-Otaibi , Tanzila Saba

{"title":"A novel hybrid fuzzy logic and federated learning framework for enhancing cybersecurity and fraud detection in IoT-enabled metaverse transactions","authors":"Amjad Rehman , Kamran Ahmad Awan , Amal Al-Rasheed , Anees Ara , Fahad F. Alruwaili , Shaha Al-Otaibi , Tanzila Saba","doi":"10.1016/j.eij.2025.100668","DOIUrl":"10.1016/j.eij.2025.100668","url":null,"abstract":"<div><div>Increasing integration of the Internet of Things (IoT) with virtual environments like the Metaverse has opened up new avenues in the applicability of technologies but faces severe challenges to security and fraud detection. Most of the existing frameworks are incapable of efficiently managing trust and detecting fraudulent activities in a decentralized, resource-constrained environment. In this article, a novel framework of cybersecurity is proposed that integrates hybrid fuzzy logic-based Trust Management with a decentralized model of Federated Learning. The proposed approach assesses and manages at runtime to maintain the degree of trust using fuzzy logic in dynamic conditions of the Metaverse. The optimized federated learning model for IoT devices implements lightweight algorithms with hierarchical aggregation that reduce computational and communication overhead to enhance fraud detection capabilities. The performance evaluation is conducted on different attack scenarios like <span><math><mrow><mi>O</mi><msub><mrow><mi>n</mi></mrow><mrow><mtext>off</mtext></mrow></msub></mrow></math></span>, Whitewashing, DDOS, and Bad Mouthing attacks. It is observed that the proposed approach performs better in comparison with existing approaches by achieving a 0.93 trust score value in low-network scenarios. It reduces computational energy consumption by 25%, thus proving the effectiveness and strength of the framework in fraud detection within IoT-enabled Metaverse environments.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"30 ","pages":"Article 100668"},"PeriodicalIF":5.0,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143870576","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Zero trust anonymous access algorithm for multi cloud storage system based on CP-ABE

IF 5 3区计算机科学

Egyptian Informatics Journal Pub Date : 2025-04-20 DOI: 10.1016/j.eij.2025.100681

Jun Tian

引用次数: 0

A multi-module approach to evaluate online teaching quality in international Chinese education

IF 5 3区计算机科学

Egyptian Informatics Journal Pub Date : 2025-04-20 DOI: 10.1016/j.eij.2025.100674

Yang Ya Ping , Zain Ul Abideen

{"title":"A multi-module approach to evaluate online teaching quality in international Chinese education","authors":"Yang Ya Ping , Zain Ul Abideen","doi":"10.1016/j.eij.2025.100674","DOIUrl":"10.1016/j.eij.2025.100674","url":null,"abstract":"<div><div>The Quality of Online Teaching in International Chinese Education (OTICE) introduces a cutting-edge approach to distance learning, making educational content accessible without limitations related to age, gender, ethnicity, or location. This research aims to establish a robust evaluation framework with high predictive accuracy for assessing OTICE by leveraging ensemble and deep learning techniques. The study explores key questions surrounding sentiment analysis within educational data. Initially, we design an index system and determine evaluation based an online questionnaires framework for OTICE, while simultaneously compiling online data for corpus development. Subsequently, we create the Multi-Module Architecture Driven Model (MMADM), which integrates a 3D-CNN module, a gated mechanism, and a selection module. Across all evaluated setups, combining a gated mechanism with Bag of Words (BoW) and a Word2Vector (W2V) word-embedding model based on the skip-gram approach delivers the highest predictive performance. Empirical findings confirm that deep learning models outperform ensemble learning techniques in the context of educational data mining. Moreover, comparative model analysis reveals that the 3D-CNN module paired with the gated mechanism produces optimal results, achieving precision (P) and F1 scores of 97.91% and 97.90%, respectively. Compared to other models, the overall performance improves by 3% to 5%. These findings underscore the superiority of the proposed model in addressing the OTICE standard task objectives presented in this paper.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"30 ","pages":"Article 100674"},"PeriodicalIF":5.0,"publicationDate":"2025-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143851997","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-task detection of harmful content in code-mixed meme captions using large language models with zero-shot, few-shot, and fine-tuning approaches

IF 5 3区计算机科学

Egyptian Informatics Journal Pub Date : 2025-04-19 DOI: 10.1016/j.eij.2025.100683

A.K. Indira Kumar , Gayathri Sthanusubramoniani , Deepa Gupta , Aarathi Rajagopalan Nair , Yousef Ajami Alotaibi , Mohammed Zakariah

{"title":"Multi-task detection of harmful content in code-mixed meme captions using large language models with zero-shot, few-shot, and fine-tuning approaches","authors":"A.K. Indira Kumar , Gayathri Sthanusubramoniani , Deepa Gupta , Aarathi Rajagopalan Nair , Yousef Ajami Alotaibi , Mohammed Zakariah","doi":"10.1016/j.eij.2025.100683","DOIUrl":"10.1016/j.eij.2025.100683","url":null,"abstract":"<div><div>In today’s digital world, memes have become a common form of communication, shaping online conversations and reflecting social events. However, some memes can negatively impact people’s emotions, especially when they involve sensitive topics or mock certain groups or individuals. To address this issue, it is important to create a system that can identify and remove harmful memes before they cause further harm. Using Large Language Models for text classification in this system offers a promising approach, as these models are skilled at understanding complex language structures and recognizing patterns, including those in code-mixed language. This research focuses on evaluating how well different Large Language Models perform in identifying memes that promote cyberbullying. It covers tasks like cyberbullying detection, sentiment analysis, emotion recognition, sarcasm detection, and harmfulness evaluation. The results show significant improvements, with a 7.94% increase in accuracy for cyberbullying detection, a 2.68% improvement in harmfulness evaluation, and a 1.7% boost in sarcasm detection compared to previous top models. There is also a 1.07% improvement in emotion detection. These findings highlight the ability of Large Language Models to help tackle cyberbullying and create safer online spaces.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"30 ","pages":"Article 100683"},"PeriodicalIF":5.0,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143850488","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Simulink-Driven Digital Twin Implementation for Smart Greenhouse Environmental Control

IF 5 3区计算机科学

Egyptian Informatics Journal Pub Date : 2025-04-16 DOI: 10.1016/j.eij.2025.100679

Jehangir Arshad , Ch. Ahsan Abbas Sheheryar , Mohammad Khalid Imam Rahmani , Abdul Qayyum , Roumaisa Nasir , Sohaib Tahir Chauhdary , Khalid Jaber Almalki

{"title":"Simulink-Driven Digital Twin Implementation for Smart Greenhouse Environmental Control","authors":"Jehangir Arshad , Ch. Ahsan Abbas Sheheryar , Mohammad Khalid Imam Rahmani , Abdul Qayyum , Roumaisa Nasir , Sohaib Tahir Chauhdary , Khalid Jaber Almalki","doi":"10.1016/j.eij.2025.100679","DOIUrl":"10.1016/j.eij.2025.100679","url":null,"abstract":"<div><div>Sustainable food production must grow unprecedentedly in the face of the growing global hunger crisis. This proposal significantly reduces global hunger by creating an environmentally friendly approach to a smart greenhouse that aligns with zero hunger and sustainable development. This novel study is dissimilar to the conventional implementation of small-scale greenhouse farming as it implements modern sophisticated techniques applied specifically in greenhouses. The novelty of work lies in the integration of Simulink, the digital twin model into the smart greenhouse environment, capable of providing intelligent insights about plant growth patterns, enabling the farmers to make the right decision at the right time with remote monitoring capabilities, while maximizing the yield potential, trained via boosted trees algorithm with 8.4684 RMSE and 85% validation accuracy. Additionally, we have used state-of-the-art CNN model, Internet of Things (IoT) sensors and image-processing techniques to identify and classify diseases of crops in a greenhouse with 98.39% validation accuracy. The reason for this is quite long-term too as it involves not only dealing with the woes befalling greenhouse agriculture but reforming a more sustainable approach to food production.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"30 ","pages":"Article 100679"},"PeriodicalIF":5.0,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143838399","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Network information security protection method based on additive Gaussian noise and mutual information neural network in cloud computing background

IF 5 3区计算机科学

Egyptian Informatics Journal Pub Date : 2025-04-16 DOI: 10.1016/j.eij.2025.100673

Yu Zhong , Xingguo Li

{"title":"Network information security protection method based on additive Gaussian noise and mutual information neural network in cloud computing background","authors":"Yu Zhong , Xingguo Li","doi":"10.1016/j.eij.2025.100673","DOIUrl":"10.1016/j.eij.2025.100673","url":null,"abstract":"<div><div>In the cloud computing environment, data security and privacy have received unprecedented attention, but current information security protection methods cannot simultaneously balance data utility and privacy protection effects. Therefore, a network information security protection method based on Gaussian denoising and mutual information neural network is proposed. The research aims to protect network information while maintaining high data utility. This study utilizes Gaussian noise and K-dimensional perturbation trees to establish a privacy protection scheme, and introduces a Bayesian network-based network intrusion detection method to combine the two for information privacy protection. Afterwards, mutual information is used to evaluate the effectiveness of privacy protection and further optimize the parameters of the protection scheme. The experimental results showed that the proposed method achieved a data utility retention rate of 85%, and the number of privacy breaches did not exceed 3 times. In long-term experiments, through continuous optimization, the number of breaches gradually remained at 0. From this, the proposed privacy protection method can effectively improve the data security and privacy in cloud computing environments, and ensure data utility during transmission and storage processes.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"30 ","pages":"Article 100673"},"PeriodicalIF":5.0,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143834921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Automatic identification method of foreign body intrusion in railway transportation track based on improved LeaderRank identification of key points

IF 5 3区计算机科学

Egyptian Informatics Journal Pub Date : 2025-04-16 DOI: 10.1016/j.eij.2025.100682

Linjie Niu

{"title":"Automatic identification method of foreign body intrusion in railway transportation track based on improved LeaderRank identification of key points","authors":"Linjie Niu","doi":"10.1016/j.eij.2025.100682","DOIUrl":"10.1016/j.eij.2025.100682","url":null,"abstract":"<div><div>To accurately identify foreign object intrusion behaviors in key areas of railway transport tracks, an automatic recognition method is proposed. This method is based on an improved LeaderRank algorithm and is designed to detect foreign object intrusions on railway tracks. First, the improved LeaderRank algorithm identifies key points of trajectories, which are then used as layout points for installing monitoring equipment. Real-time monitoring devices collect video images of key track areas. Next, an improved Gaussian mixture model is used for image segmentation in track monitoring, extracting potential foreground images containing foreign object intrusions. These images are then input into a hybrid deep learning-based automatic recognition model for foreign object intrusion. The firefly algorithm trains this model, constructing a structurally stable hybrid deep learning model that learns the relationship between image combination features and foreign object intrusion behaviors, enabling accurate recognition of foreign object intrusions. Experimental results demonstrate that this method accurately identifies foreign object intrusion, enhancing detection accuracy and reliability. The proposed method, combining the improved LeaderRank algorithm with hybrid deep learning, offers an efficient and accurate solution, providing a new technical approach for railway transport safety management.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"30 ","pages":"Article 100682"},"PeriodicalIF":5.0,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143838397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Effectiveness of Teachable Machine, mobile net, and YOLO for object detection: A comparative study on practical applications

IF 5 3区计算机科学

Egyptian Informatics Journal Pub Date : 2025-04-14 DOI: 10.1016/j.eij.2025.100680

Sheikh Muhammad Saqib , Muhammad Iqbal , Tehseen Mazhar , Tariq Shahzad , Khmaies Ouahada , Habib Hamam

{"title":"Effectiveness of Teachable Machine, mobile net, and YOLO for object detection: A comparative study on practical applications","authors":"Sheikh Muhammad Saqib , Muhammad Iqbal , Tehseen Mazhar , Tariq Shahzad , Khmaies Ouahada , Habib Hamam","doi":"10.1016/j.eij.2025.100680","DOIUrl":"10.1016/j.eij.2025.100680","url":null,"abstract":"<div><div>In this comparative study, the effectiveness of three prominent object detection models—Teachable Machine, MobileNet, and YOLO—was evaluated using a diverse dataset consisting of images from four distinct categories: bird, horse, laptop, and sandwich. The objective was to identify the most efficient model in terms of accuracy, speed, and usability for practical applications in fields such as self-driving vehicles, robotics, security systems, and augmented reality. The dataset was meticulously curated and subjected to training across the three models. Results from the comprehensive analysis indicated that the Teachable Machine model surpassed both MobileNet and YOLO in performance, demonstrating superior accuracy and effectiveness in detecting objects across the specified categories. This research contributes significantly to the domain of artificial intelligence by providing detailed insights and comparisons of model performances, offering a valuable resource for further advancements in object detection technologies. The study not only showcases the Teachable Machine’s superiority in handling multi-class object detection problems but also sets a benchmark for future explorations in enhancing object detection methodologies.</div></div>","PeriodicalId":56010,"journal":{"name":"Egyptian Informatics Journal","volume":"30 ","pages":"Article 100680"},"PeriodicalIF":5.0,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143825671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0