Journal of Information and Communication Technology

One significant challenge in sentiment analysis is the presence of negation, which reverses the meanings of sentences, transformingpositive statements into negative ones and impacting the sentiment conveyed in the text. This issue is particularly pronounced in Arabic, a language known for its complex morphology. Detecting negation is crucial for enhancing sentiment analysis performance and various natural language processing applications. This paper presents an approach for automatically detecting negation in user-generated Arabic hotel reviews through lexical and structural features. It comprises several stages: data collection, text pre-processing, feature extraction, supervised learning classification, and evaluation. The study employed multiple supervised classification techniques, including naïve Bayes, random forest, logistic regression, support vector machines, and deep learning, to analyse lexical and structural features extracted from the dataset. The results of the experiments yielded promising outcomes, demonstrating the feasibility of the approach for practical applications. The classifiers exhibited highly comparable performance in identifying negation, with only marginal deviations in their performance metrics. Notably, the deep learning classifier consistently emerged as the top performer, achieving an exceptionally high overall accuracy rate of 99.24 percent, surpassing established benchmarks in Arabic text processing and underscoring its potential for practical applications. These findings hold significant implications for advancing Arabic text processing, particularly in sentiment analysis and related NLP tasks. The high accuracy of 99.24 percent achieved by the deep learning classifier highlights its robustness in accurately detecting negation, a critical challenge in sentiment analysis. This classifier performance demonstrates the potential to be integrated into real-world applications, such as automated review systems and opinion mining tools, where accurate sentiment interpretation is essential.

Ahmed Suliman Abuhammad

Mahmoud Ali Ahmed

Automatic Negation Detection for Semantic Analysis in Arabic Hotel Reviews Through Lexical and Structural Features: A Supervised Classification

通过词汇和结构特征进行阿拉伯语酒店评论的自动否定检测以实现语义分析：一种有监督的分类方法

Old Jawi Manuscripts (OJM) are crucial to historical studies, offering insights into past societies. However, degradation from mishandling and environmental factors can impair their legibility. To preserve OJM, image inpainting and segmentation are essential for restoring corrupted areas and identifying text. Recently, the Gaussian Regularization Segmentation (GRS) model has shown effectiveness in intensity inhomogeneity grayscale image segmentation, though it was not designed for corrupted OJM images. Therefore, this study aimed to reformulate the GRS model to restore and segment text from real corrupted OJM images. The methodology begins with the incorporation of the Mumford-Shah and Bertalmio inpainting models into the GRS model as new fitting terms, resulting in the Modified Gaussian Regularization Segmentation Mumford-Shah (MGRSM) model and the Modified Gaussian Regularization Segmentation Bertalmio (MGRSB) model, respectively. MATLAB was used to implement these models, and their performance was assessed on 30 corrupted OJM samples from Malay Ethnomathematics Research Group, with expert evaluations and efficiency measured by average elapsed time. The MGRSM model achieved 38.4 percent and 12.4 percent higher overall total scores from experts in terms of segmentation accuracy compared to the GRS and MGRSB models, respectively. While the GRS model is the fastest, the MGRSM model provides superior accuracy, with an average processing time of 9.35 seconds, making it the most optimal for restoring and segmenting OJM images. This approach not only enhances the preservation of historical manuscripts but also provides a practical tool for researchers and historians in safeguarding our cultural heritage.

Akmal Shafiq Badarul Azam

Abdul Kadir Jumaat

Amisha Balkis Badarul Azam

Nur Afiqah Sabirah Mohammad Sabri

Amiratul Munirah Yahaya

Ahmad Thaqif Ismail

Muhammad Anas Abdul Razak

Mohd Azdi Maasar

Mohamed Faris Laham

Restoration and Segmentation of Old Jawi Manuscripts using Variational Image Inpainting and Active Contour Models

使用变分图像修复和活动轮廓模型的旧爪夷文手稿修复与分割

This study presents a Reinforcement Learning-based algorithm designed to optimise irrigation for Durio Zibethinus (i.e., durian) trees, aiming to maximise tree growth and reduce water usage. Traditional irrigation methods, as well as current machine learning models, often focus only on soil moisture and weather data, neglecting critical factors like actual tree growth. This study proposed a reinforcement learning irrigation (RL-Irr) algorithm incorporating tree growth stages, soil moisture, and weather conditions to determine precise irrigation needs. The algorithm was developed by calibrating the AQUACROP model using data from actual durian plantations where rain-fed irrigation (rain-fed) was practised. Daily irrigation volumes were calculated based on real-time soil moisture, weather forecasts, and weekly tree growth measurements. The reinforcement learning method was used to optimise irrigation schedules, with rewards based on soil moisture, tree growth, rainfall, and weather conditions. The algorithm was tested using AQUACROP simulations and compared against soil moisture balance irrigation (SMB-Irr) and rain-fed. The results showed that the RL-Irr reduced water use by up to 75 percent while maintaining tree growth. These findings suggest the algorithm could significantly improve water efficiency in durian farming, though real-world applications should consider potential model limitations.

Muhammad Shahrul Azwan Ramli

Mohamad Shukri Zainal Abidin

Nor Shahida Hasan

Mohd Nadzri Md Reba

Keshinro Kazeem Kolawole

Rizqi Andry Ardiansyah

Sikudhan Lucas Mpuhus

Reinforcement Learning Algorithm for Optimising Durian Irrigation Systems: Maximising Growth and Water Efficiency

优化榴莲灌溉系统的强化学习算法：最大化生长与水资源效率

This study seeks to deliver a comprehensive bibliometric analysis of blockchain research undertaken from 2013 to 2023. It seeks to map the field’s evolution, highlight emerging trends, and identify key contributors and thematic areas. Understanding these aspects is crucial for guiding future research and policymaking in the rapidly growing domain of blockchain technology. The study utilises a systematic bibliometric analysis approach and the Scopus database was used for collecting data. The analysis includes keyword trends, citation patterns, author productivity, and network analysis. The results reveal significant expansion and diversification in blockchain research, with 13,369 publications and 52,009 contributing authors. Key metrics include a total citation count of 266,483 and an average of 19.93 citations per paper, underscoring the field’s scholarly impact. The study identifies core research themes such as smart contracts, security, IoT integration, and emerging topics like AI and sustainability. The findings highlight the interdisciplinary nature of blockchain research and its global distribution, with notable contributions from China and the United States. The study’s reliance on selected databases and predefined keywords may exclude relevant grey literature and industry reports. Future research should integrate these sources for a more complete view. This study provides a panoramic view of blockchain research, offering valuable insights into its development, current state, and future directions. Identifying key trends, influential contributors, and emerging themes contributes to the academic discourse and supports strategic planning for researchers, practitioners, and policymakers engaged in blockchain technology.

Tahir Hakim

Aidi Ahmi

Shadab Alam

A Decade in Blockchain: A Bibliometric Reflection on the Growth and Interdisciplinary Reach of a Disruptive Technology

区块链十年：一项颠覆性技术的成长与跨学科影响之文献计量学反思

High-dimensional datasets (HDDs) pose significant challenges in feature selection due to their complex nature. While metaheuristic algorithms like the Whale Optimisation Algorithm (WOA) have shown promise in addressing these challenges, stability issues are least addressed. Through an extensive literature review, it was identified that stability is manifested in the equilibrium of exploration and exploitation. Hence, this study introduced the Hierarchical Whale Optimisation Algorithm (HiWOA), an approach designed to enhance the WOA’s stability and performance in HDD feature selection tasks. The HiWOA incorporates a two-phase strategy comprising a nonlinear control parameter based on the arcsine function and a hierarchical position-update mechanism adapted from the Grey Wolf Optimiser. The proposed HiWOA was evaluated through 23 benchmark optimisation functions and feature selection experiments on 11 medical HDDs. The results indicate that the HiWOA outperformed the WOA and a modified variant (mWOA) in terms of a better fitness value, a more balanced exploration-exploitation ratio, and improved classification accuracy with fewer selected features. These findings demonstrate the HiWOA’s effectiveness in enhancing stability, making it a robust solution for high-dimensional optimisation and feature selection.

Li Yu Yab

Noorhaniza Wahid

Rahayu A Hamid

Impact of Balanced Exploration and Exploitation on Highdimensional Feature Selection with Hierarchical Whale Optimisation Algorithm

平衡探索与开发对基于层次鲸鱼优化算法的高维特征选择的影响

Abbreviation	JICT
Journal Impact	1.03
Quartiles(Global)	COMPUTER SCIENCE, INFORMATION SYSTEMS(Q4)

ISSN	1675-414X, 2180-3862
h-index	16

Publication Information	Publisher: Universiti Utara Malaysia Press，Publishing cycle: ，Journal Type: journal，Open Access Journals: Yes
Basic data	Year of publication: 2011，Proportion of original research papers: 87.50%，Self Citation Rate:30.00%， Gold OA Rate: 88.89%
Average review cycle	网友分享经验：12 Weeks

Journal of Information and Communication Technology

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Automatic Negation Detection for Semantic Analysis in Arabic Hotel Reviews Through Lexical and Structural Features: A Supervised Classification

Restoration and Segmentation of Old Jawi Manuscripts using Variational Image Inpainting and Active Contour Models

Reinforcement Learning Algorithm for Optimising Durian Irrigation Systems: Maximising Growth and Water Efficiency

A Decade in Blockchain: A Bibliometric Reflection on the Growth and Interdisciplinary Reach of a Disruptive Technology

Impact of Balanced Exploration and Exploitation on Highdimensional Feature Selection with Hierarchical Whale Optimisation Algorithm

Automatic Negation Detection for Semantic Analysis in Arabic Hotel Reviews Through Lexical and Structural Features: A Supervised Classification

Restoration and Segmentation of Old Jawi Manuscripts using Variational Image Inpainting and Active Contour Models

Reinforcement Learning Algorithm for Optimising Durian Irrigation Systems: Maximising Growth and Water Efficiency

A Decade in Blockchain: A Bibliometric Reflection on the Growth and Interdisciplinary Reach of a Disruptive Technology

Impact of Balanced Exploration and Exploitation on Highdimensional Feature Selection with Hierarchical Whale Optimisation Algorithm