International Journal of Grid and Utility Computing (14 papers in press)

Regular Issues

• Recommendation system based on space-time user similarity
  by Wei Luo, Zhihao Peng, Ansheng Deng 
  Abstract: With the advent of 5G, the way people get information and the means of information transmission have become more and more important. As the main platform of information transmission, social media not only brings convenience to people's lives, but also generates huge amounts of redundant information because of the speed of information updating. In order to meet the personalised needs of users and enable users to find interesting information in a large volume of data, recommendation systems emerged as the times require. Recommendation systems, as an important tool to help users to filter internet information, play an extremely important role in both academia and industry. The traditional recommendation system assumes that all users are independent. In this paper, in order to improve the prediction accuracy, a recommendation system based on space-time user similarity is proposed. The experimental results on Sina Weibo dataset show that, compared with the traditional collaborative filtering recommendation system based on user similarity, the proposed method has better performance in precision, recall and F-measure evaluation value.
  Keywords: time-based user similarity; space-based user similarity; recommendation system; user preference; collaborative filtering.
  
  
• Joint end-to-end recognition deep network and data augmentation for industrial mould number recognition  
  by RuiMing Li, ChaoJun Dong, JiaCong Chen, YiKui Zhai 
  Abstract: With the booming manufacturing industry, the significance of mould management is increasing. At present, manual management is gradually eliminated owing to need for a large amount of labour, while the effect of a radiofrequency identification (RFID) system is not ideal, which is limited by the characteristics of the metal, such as rust and erosion. Fortunately, the rise of convolutional neural networks (CNNs) brings down to the solution of mould management from the perspective of images that management by identifying the digital number on the mould. Yet there is no trace of a public database for mould recognition, and there is no special recognition method in this field. To address this problem, this paper first presents a novel data set aiming to support the CNN training. The images in the database are collected in the real scene and finely manually labelled, which can train an effective recognition model and generalise to the actual scenario. Besides, we combined the mainstream text spotter and the data augmentation specifically designed for the real world, and found that it has a considerable effect on mould recognition.
  Keywords: mould recognition database; text spotter; mould recognition; data augmentation.
  
  
• University ranking approach with bibliometrics and augmented social perception data  
  by Kittayaporn Chantaranimi, Rattasit Sukhahuta, Juggapong Natwichai 
  Abstract: Typically, universities aim to achieve a high position in ranking systems for their reputation. However, self-evaluating rankings could be costly because the indicators are not only from bibliometrics, but also the results of over a thousand surveys. In this paper, we propose a novel approach to estimate university rankings based on traditional data, i.e., bibliometrics, and non-traditional data, i.e., Altmetric Attention Score, and Sustainable Development Goals indicators. Our approach estimates subject-areas rankings in Arts & Humanities, Engineering & Technology, Life Sciences & Medicine, Natural Sciences, and Social Sciences & Management. Then, by using Spearman rank-order correlation and overlapping rate, our results are evaluated by comparing with the QS subject ranking. From the result, our approach, particularly the top-10 ranking, performed estimating effectively and then could assist stakeholders in estimating the university's position when the survey is not available.
  Keywords: university ranking; rank similarity; bibliometrics; augmented social perception data; sustainable development goals; Altmetrics.
  
  
• Assessment of a cuckoo search-based intelligent system for mesh routers placement optimisation in WMNs considering various distributions of mesh clients  
  by Shinji Sakamoto 
  Abstract: Wireless Mesh Networks (WMNs) have many advantages. However, they have several issues related to wireless communication. An effective approach to deal with these problems is the optimization of mesh routers placement in WMNs, but this is an NP-hard problem. Thus, heuristic and intelligent algorithms are needed. In the previous work, we developed an intelligent simulation system based on Cuckoo Search (CS) (WMN-CS), which is a meta-heuristic algorithm. In this work, we evaluate the WMN-CS performance for various distributions of mesh clients: Uniform, Normal, Exponential, Weibull and Chi-square distributions. The simulation results show that for Normal distribution the WMN-CS system can find suitable locations of mesh routers for about 30 phases. The Uniform distribution has the lowest performance compared to the other distributions. Also, Exponential andWeibull distributions converge slower than Chi-square distribution. However, Exponential distribution converges faster than Weibull distribution.
  Keywords: wireless mesh networks; node placement problem; cuckoo search; client distributions.
  DOI: 10.1504/IJGUC.2024.10068275
   
  
• Analysis of cybersecurity attacks and solution approaches  
  by Ali Yılmaz, Resul Das 
  Abstract: Numerous cybersecurity vulnerabilities have emerged as a result of the quickly changing digital environment, posing serious risks to people, corporations, and governments alike The article "An analysis cybersecurity attacks and solution approaches" delves into the subtleties of this contemporary conflict zone and offers a thorough analysis of the many assault vectors, techniques, and their far-reaching effects This article examines the full spectrum of online hazards, from well-known enemies such as malware, phishing, and denial-of-service attacks to more sophisticated and sneaky threats such as advanced persistent threats (APT) It explores the methods and strategies employed by cybercriminals, offering light on their always-changing approaches This article also sheds light on preventive measures and problem-solving techniques created to combat these risks Provides an overview of a wide range of cybersecurity techniques and tools, including artificial intelligence, intrusion detection systems, encryption, and multifactor authentication Individuals and organizations can protect their digital domains from the onslaught of cyberattacks by being aware of the threats and having the necessary tools and knowledge. With a clearer grasp of the cyberthreat landscape and the various solutions to reduce these risks, readers should be better equipped to use the internet safely as a result of this thorough analysis.
  Keywords: information security; cybersecurity; malware; network security; attacks; cyberthreats.
  DOI: 10.1504/IJGUC.2024.10068492
   
  
• A two-stage intrusion detection framework in IoT using random forest for binary and multi-class classification  
  by Arash Salehpour, Pejman Hosseinioun, MohammadAli Balafar 
  Abstract: The proliferation of IoT devices due to cyber threats, which have become increasingly sophisticated, requires a strong security framework. This paper proposed a new framework for Intrusion Detection System-IoT-IDs using a Random Forest classifier to first classify the attack into binary features and prepare a new dataset that would enable multiclass classification. It achieved an overall accuracy of 0.98 on the comprehensive UNSW-NB15 dataset, with very good performance in detecting 'Generic' attacks, having almost perfect precision, recall, and F1-score. it also presented cases of 'Analysis' and 'Backdoor' types of attacks, where further improvements should be done. All these models have been analyzed to find the pros and cons in IoT settings. The Random Forests, XGBoost and MLP. Further studies based on the research could be done on multiplying models with improved features, intrusion detection in real time, and more strong AI techniques. This paper focuses on addressing challenges with imbalanced classes and scalability concerns using data privacy preservation methods for improving the performance of IDS.
  Keywords: IDS; intrusion detection system; IoT; internet of things; ensemble learning; UNSWNB15; cybersecurity; hybrid models.
  DOI: 10.1504/IJGUC.2024.10071203
   
  
• Fractional red panda optimisation-based cluster head selection and routing in IoT  
  by Nandkumar Prabhakar Kulkarni, Meena Chavan, Amar Rajendra Mudiraj 
  Abstract: In the present world, the IoT has been developed as a widespread network for various smart devices in numerous applications. IoT is considered a significant technology for achieving the requirements for a variety of applications. However, load balancing, energy inadequate battery power, and security may affect the performance of IoT. Therefore, the Fractional Red Panda Optimization (FrRPO)-based Cluster Head (CH) selection and routing is proposed in this paper. The IoT network simulation is the primary process. The Deep Q Net (DQN) is used for predicting the energy. The FrRPO with fitness factors like predicted energy, delay, and distance are used to select the CH. Moreover, the FrRPO with fitness factors like throughput, energy, distance, and reliability are utilized for routing. The metrics like energy consumption, delay, and throughput are considered to validate the model, which attains the optimal results of 0.555 J, 0.666 s, and 89.02 Mbps.
  Keywords: cluster head; red panda optimisation; internet of things; routing; fractional calculus.
  DOI: 10.1504/IJGUC.2025.10072128
   
  
• Comparable IoT and DL methods of drinking water usage  
  by Arber Musliu, Naim Baftiu 
  Abstract: IoT applications have actively employed advanced technology, utilizing neural networks to comprehend and connect with their surroundings. Significantly, Amazon Echo exemplifies an IoT application by bridging physical and human realms with the digital domain, employing deep learning for voice command comprehension. Similarly, Microsoft's Windows facial recognition security system integrates DL to unlock doors upon facial recognition. This research explores the integration of IoT with DL techniques to enhance the monitoring and analysis of drinking water quality assessment, evaluating various methods to determine drinkability. Various machine learning algorithms, including Random Forest, LightGBM, and Bagging Classifier, are employed to predict water quality based on multiple parameters such as pH, conductivity, and turbidity. The study uses a comprehensive dataset featuring 3277 values for nine different water quality indicators. Comparative analysis revealed similar outcomes: Random Forest demonstrated the highest accuracy, achieving a predictive accuracy of 0.824695, followed by Light GBM and and Bagging Classifier. This research contributes to the ongoing efforts to employ advanced computational techniques in environmental monitoring, providing a reliable methodological framework for future studies to enhance water quality assessment.
  Keywords: drinking water usage; IoT; DL methods; water monitoring; smart water systems.
  DOI: 10.1504/IJGUC.2024.10072181
   
  
• Dynamic application placement and resource optimisation technique for heterogeneous fog computing environments  
  by S. Sheela, S. M. Dilip Kumar 
  Abstract: The increase in demand for reducing the latency in service requests of Internet of Things (IoT) applications has led researchers to drift from Cloud computing to Fog computing paradigms. Fog computing brings computing and data storage closer to devices and sensors, reducing latency and improving response time and reliability. However, implementing fog computing successfully requires fog nodes and applications to be deployed effectively to provide high-performance services. In addition, fog computing faces challenges in efficient resource scheduling due to the scarce capacity of fog nodes and the dynamic and heterogeneous nature of devices, leading to complexities in workload allocation and optimal resource utilization. This work presents a simplified model for dynamically placing the application modules in a heterogeneous fog computing environment 1. A new framework for learning scheme is implemented using a Deep Deterministic Policy Gradient (DDPG)-based reinforcement learning technique for predicting the operations and determining the cumulative rewards. A test environment demonstrates that the proposed framework has lower mobility dependency, higher reward, and reduced variance compared to existing schemes.
  Keywords: application placement; cloud computing; bandwidth; deep deterministic policy gradient; dynamic; fog computing; heterogeneous; Markov Model; optimal node placement; resource management.
  DOI: 10.1504/IJGUC.2024.10072572
   
  
• Improved PROMETHEE-based energy efficient host selection framework for cloud data centres  
  by Jagpreet Sidhu, Arvinder Kaur, Yugal Kumar, Pardeep Kumar 
  Abstract: VM consolidation is an effective approach for reducing energy consumption in cloud data centers. The selection of VMs from under-loaded, overloaded machines and migrating them on effective hosts constitute the process of VM consolidation. The algorithms in literature to select hosts for VM deployment are generally based on single criterion. However, VM placement is a multi-criteria decision-making problem. In this paper, an attempt is made to design a host selection technique based on improved Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE) for energy efficient VM deployment. The proposed selection policy uses multiple parameters to find selection index of hosts. The selected hosts help to reduce both energy consumption and service level agreement violations. A case study based approach is followed to validate the proposed VM deployment framework using real data, real hosts and VMs configuration. Results indicate the employability of the framework in real cloud environment.
  Keywords: cloud computing; VM consolidation; energy efficiency; improved PROMETHEE; host selection.
  DOI: 10.1504/IJGUC.2024.10073106
   
  
• Security issues and challenges in cloud of things-based applications for industrial automation  
  by Cheng Liu, Yichao Zhang, Yanfeng Yu 
  Abstract: To address security threats to cloud server serial port communication data, the research introduces a monitoring model that incorporates a passive clustering algorithm. The study uses a density-based approach to identify natural clusters in the data, allowing each cluster to have a different shape and size. The algorithm first evaluates the local densities of the data points and then assigns the data points to the nearest high-density areas based on these density values to form clusters. The results revealed an improvement of up to 0.06% in recall, up to 0.12% in accuracy and up to 0.09% in F1-score. The passive clustering algorithm improved on an average of 32.56% in F1-score, 24.78% in accuracy and 3.38% in recall compared to other methods. More advanced optimisations further improved the detection accuracy to 98.5678%, the false alarm rate to 1.4322% and the detection latency further reduced to 16,888.43 ms, highlighting the potential of the passive clustering algorithm in monitoring the security of serial port data for cloud server communication. As a result, the model constructed by the research can optimise the limitations existing in the traditional sub-cluster model, and then realise the service of user information data security, which has excellent practical value and broad application prospects.
  Keywords: passive clustering algorithm; cloud server; communication serial port; data security; security monitoring.
  DOI: 10.1504/IJGUC.2025.10073314
   
  

Special Issue on: AMLDA 2022 Applied Machine Learning and Data Analytics Applications, Challenges, and Future Directions

• Fuzzy forests for feature selection in high-dimensional survey data: an application to the 2020 US Presidential Election  
  by Sreemanti Dey, R. Michael Alvarez 
  Abstract: An increasingly common methodological issue in the field of social science is high-dimensional and highly correlated datasets that are unamenable to the traditional deductive framework of study. Analysis of candidate choice in the 2020 Presidential Election is one area in which this issue presents itself: in order to test the many theories explaining the outcome of the election, it is necessary to use data such as the 2020 Cooperative Election Study Common Content, with hundreds of highly correlated features. We present the fuzzy forests algorithm, a variant of the popular random forests ensemble method, as an efficient way to reduce the feature space in such cases with minimal bias, while also maintaining predictive performance on par with common algorithms such as random forests and logit. Using fuzzy forests, we isolate the top correlates of candidate choice and find that partisan polarisation was the strongest factor driving the 2020 Presidential Election.
  Keywords: fuzzy forests; machine learning; ensemble methods; dimensionality reduction; American elections; candidate choice; correlation; partisanship; issue voting; Trump; Biden.
  
  
• An efficient intrusion detection system using unsupervised learning AutoEncoder  
  by N.D. Patel, B.M. Mehtre, Rajeev Wankar 
  Abstract: As attacks on the network environment are rapidly becoming more sophisticated and intelligent in recent years, the limitations of the existing signature-based intrusion detection system are becoming more evident. For new attacks such as Advanced Persistent Threat (APT), the signature pattern has a problem of poor generalisation performance. Research on intrusion detection systems based on machine learning is being actively conducted to solve this problem. However, the attack sample is collected less than the normal sample in the actual network environment, so it suffers a class imbalance problem. When a supervised learning-based anomaly detection model is trained with these data, the results are biased toward normal samples. In this paper, AutoEncoder (AE) is used to perform single-class anomaly detection to solve this imbalance problem. The experimental evaluation was conducted using the CIC-IDS2017 dataset, and the performance of the proposed method was compared with supervised models to evaluate the performance
  Keywords: intrusion detection system; advanced persistent threat; CICIDS2017; AutoEncoder; machine learning; data analytics.
  
  

Special Issue on: Cloud and Fog Computing for Corporate Entrepreneurship in the Digital Era

• Study on the economic consequences of enterprise financial sharing model  
  by Yu Yang, Zecheng Yin 
  Abstract: Using enterprise system ideas to examine the business process requirements of firms, the Financial Enterprise Model (FEM) is a demanding program. This major integrates finance, accounting, and other critical business processes. Conventional financial face difficulties due to low economic inclusion, restricted access to capital, lack of data, poor R&D expenditures, underdeveloped distribution channels, and so on. This paper mentions making, consuming, and redistributing goods through collaborative platform networks. These three instances highlight how ICTs (Information and Communication Technologies) can be exploited as a new source of company innovation. The sharing economy model can help social companies solve their market problems since social value can be embedded into their sharing economy cycles. As part of the ICT-based sharing economy, new business models for social entrepreneurship can be developed by employing creative and proactive platforms. Unlike most public organizations, double-bottom-line organizations can create social and economic advantages. There are implications for developing and propagating societal values based on these findings.
  Keywords: finance; economy; enterprise; ICT; social advantage.