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The search algorithm based on symbiotic organisms’ interactions is a relatively recent bio-inspired algorithm of the swarm intelligence field for solving numerical optimization problems. It is meant to optimize applications based on the simulation of the symbiotic relationship among the distinct species in the ecosystem. The task scheduling problem is NP complete, which makes it hard to obtain a correct solution, especially for large-scale tasks. This paper proposes a modified symbiotic organisms search-based scheduling algorithm for the efficient mapping of heterogeneous tasks to access cloud resources of different capacities. The significant contribution of this technique is the simplified representation of the algorithm’s mutualism process, which uses equity as a measure of relationship characteristics or efficiency of species in the current ecosystem to move to the next generation. These relational characteristics are achieved by replacing the original mutual vector, which uses an arithmetic mean to measure the mutual characteristics with a geometric mean that enhances the survival advantage of two distinct species. The modified symbiotic organisms search algorithm (G_SOS) aims to minimize the task execution time (makespan), cost, response time, and degree of imbalance, and improve the convergence speed for an optimal solution in an IaaS cloud. The performance of the proposed technique was evaluated using a CloudSim toolkit simulator, and the percentage of improvement of the proposed G_SOS over classical SOS and PSO-SA in terms of makespan minimization ranges between 0.61–20.08% and 1.92–25.68% over a large-scale task that spans between 100 to 1000 Million Instructions (MI). The solutions are found to be better than the existing standard (SOS) technique and PSO.

Background and Objectives: Computer-aided diagnostic systems have achieved remarkable success in the medical field, particularly in diagnosing malignant tumors, and have done so at a rapid pace. However, the generalizability of the results remains a challenge for researchers and decreases the credibility of these models, which represents a point of criticism by physicians and specialists, especially given the sensitivity of the field. This study proposes a novel model based on deep learning to enhance lung cancer diagnosis quality, understandability, and generalizability. Methods: The proposed approach uses five computed tomography (CT) datasets to assess diversity and heterogeneity. Moreover, the mixup augmentation technique was adopted to facilitate the reliance on salient characteristics by combining features and CT scan labels from datasets to reduce their biases and subjectivity, thus improving the model’s generalization ability and enhancing its robustness. Curriculum learning was used to train the model, starting with simple sets to learn complicated ones quickly. Results: The proposed approach achieved promising results, with an accuracy of 99.38%; precision, specificity, and area under the curve (AUC) of 100%; sensitivity of 98.76%; and F1-score of 99.37%. Additionally, it scored a 00% false positive rate and only a 1.23% false negative rate. An external dataset was used to further validate the proposed method’s effectiveness. The proposed approach achieved optimal results of 100% in all metrics, with 00% false positive and false negative rates. Finally, explainable artificial intelligence (XAI) using Gradient-weighted Class Activation Mapping (Grad-CAM) was employed to better understand the model. Conclusions: This research proposes a robust and interpretable model for lung cancer diagnostics with improved generalizability and validity. Incorporating mixup and curriculum training supported by several datasets underlines its promise for employment as a diagnostic device in the medical industry.

A brain tumour is one of the major reasons for death in humans, and it is the tenth most common type of tumour that affects people of all ages. However, if detected early, it is one of the most treatable types of tumours. Brain tumours are classified using biopsy, which is not usually performed before definitive brain surgery. An image classification technique for tumour diseases is important for accelerating the treatment process and avoiding surgery and errors from manual diagnosis by radiologists. The advancement of technology and machine learning (ML) can assist radiologists in tumour diagnostics using magnetic resonance imaging (MRI) images without invasive procedures. This work introduced a new hybrid CNN-based architecture to classify three brain tumour types through MRI images. The method suggested in this paper uses hybrid deep learning classification based on CNN with two methods. The first method combines a pre-trained Google-Net model of the CNN algorithm for feature extraction with SVM for pattern classification. The second method integrates a finely tuned Google-Net with a soft-max classifier. The proposed approach was evaluated using MRI brain images that contain a total of 1426 glioma images, 708 meningioma images, 930 pituitary tumour images, and 396 normal brain images. The reported results showed that an accuracy of 93.1% was achieved from the finely tuned Google-Net model. However, the synergy of Google-Net as a feature extractor with an SVM classifier improved recognition accuracy to 98.1%.

Diabetic Retinopathy (DR) remains a leading cause of preventable vision impairment among individuals with diabetes, particularly when not identified in its early stages. Conventional diagnostic techniques typically employ either fundus photography or Optical Coherence Tomography (OCT), with each modality offering distinct yet partial insights into retinal abnormalities. This study proposes a multimodal diagnostic framework that fuses both structural and spatial retinal characteristics through the integration of fundus and OCT imagery. We utilize a curated subset of 222 high- quality, modality- paired images (111 fundus + 111 OCT), selected from a larger publicly available dataset based on strict inclusion criteria including image clarity, diagnostic labeling, and modality alignment. Feature extraction pipelines are optimized for each modality to capture relevant pathological markers, and the extracted features are fused using an attention- based weighting mechanism that emphasizes diagnostically salient regions across modalities. The proposed approach achieves an accuracy of 90.5% and an AUC- ROC of 0.970 on this curated subset, indicating promising feasibility of multimodal fusion for early- stage DR assessment. Given the limited dataset size, these results should be interpreted as preliminary, demonstrating methodological potential rather than large- scale robustness. The study highlights the clinical value of hybrid imaging frameworks and AI- assisted screening tools, while emphasizing the need for future validation on larger and more diverse datasets.

The advent of social networks and micro-blogging sites online has led to an abundance of user-generated content. Hence, the enormous amount of content is viewed as inappropriate and unimportant information by many users on social media. Therefore, there is a need to use personalization to select information related to users’ interests or searchers on social media platforms. Therefore, in recent years, user interest mining has been a prominent research area. However, almost all of the emerging research suffers from significant gaps and drawbacks. Firstly, it suffers from focusing on the explicit content of the users to determine the interests of the users while neglecting the multiple facts as the personality of the users; demographic data may be a valuable source of influence on the interests of the users. Secondly, existing work represents users with their interesting topics without considering the semantic similarity between the topics based on clusters to extract the users’ implicit interests. This paper is aims to propose a novel user interest mining approach and model based on demographic data, big five personality traits and similarity between the topics based on clusters. To demonstrate the leverage of combining user personality traits and demographic data into interest investigation, various experiments were conducted on the collected data. The experimental results showed that looking at personality and demographic data gives more accurate results in mining systems, increases utility, and can help address cold start problems for new users. Moreover, the results also showed that interesting topics were the dominant factor. On the other hand, the results showed that the current users’ implicit interests can be predicted through the cluster based on similar topics. Moreover, the hybrid model based on graphs facilitates the study of the patterns of interaction between users and topics. This model can be beneficial for researchers, people on social media, and for certain research in related fields.

The Internet of Things (IoT) Investigation of Forensics (IoTFI) is one of the subdomains of Digital Forensics that aims to record and evaluate incidents involving the Internet of Things (IoT). Because of the many different standards, operating systems, and infrastructure-based aspects that make up the Internet of Things industry, this sector is extremely varied, ambiguate, and complicated. Many distinct IoTFI models and frameworks were developed, each one based on a unique set of investigation procedures and activities tailored to a particular IoT scenario. Because of these models, the domain becomes increasingly complicated and disorganized among those who perform domain forensics. As a result, the IoTFI domain does not have a general model for managing, sharing, and reusing the processes and activities that it offers. With the use of the metamodeling development process, this work aims to create an Internet of Things Forensic Investigation Metamodel (IoTFIM) for the IoTFI domain. Utilizing the metamodeling development process allows for the construction and validation of a metamodel and the verification that the metamodel is both comprehensive and consistent. The IoTFIM is divided into two phases: the first phase identifies the problem, and the second phase develops the IoTFIM. It is utilized to structure and organize IoTFI domain knowledge, which makes it easier for domain forensic practitioners to manage, organize, share, and reuse IoTFI domain knowledge. The purpose of this is to detect, recognize, extract, and match various IoTFI processes, concepts, activities, and tasks from various IoTFI models in an IoTFIM that was established, facilitating the process of deriving and instantiating solution models for domain practitioners. Utilizing several metamodeling methodologies, we were able to validate the generated IoTFMI’s consistency as well as its applicability (comparison against other models, frequency-based selection). Based on the findings, it can be concluded that the built IoTFIM is consistent and coherent. This makes it possible for domain forensic practitioners to simply instantiate new solution models by picking and combining concept elements (attribute and operations) based on the requirements of their models.

Drone Forensics (DRF) is one of the subdomains of digital forensics, which aims to capture and analyse the drone’s incidents. It is a diverse, unclear, and complex domain due to various drone field standards, operating systems, and infrastructure-based networks. Several DRF models and frameworks have been designed based on different investigation processes and activities and for the specific drones’ scenarios. These models make the domain more complex and unorganized among domain forensic practitioners. Therefore, there is a lack of a generic model for managing, sharing, and reusing the processes and activities of the DRF domain. This paper aims to develop A Drone Forensic Metamodel (DRFM) for the DRF domain using the metamodeling development process. The metamodeling development process is used for constructing and validating a metamodel and ensuring that the metamodel is complete and consistent. The developed DRFM consists of three main stages: (1) identification stage, (2) acquisition and preservation stage, and (3) examination and data analysis stage. It is used to structure and organize DRF domain knowledge, which facilitates managing, organizing, sharing, and reusing DRF domain knowledge among domain forensic practitioners. That aims to identify, recognize, extract and match different DRF processes, concepts, activities, and tasks from other DRF models in a developed DRFM. Thus, allowing domain practitioners to derive/instantiate solution models easily. The consistency and applicability of the developed DRFM were validated using metamodel transformation (vertical transformation). The results indicated that the developed DRFM is consistent and coherent and enables domain forensic practitioners to instantiate new solution models easily by selecting and combining concept elements (attribute and operations) based on their model requirement.

The Database Forensics Investigation (DBFI) field is focused on capturing and investigating database incidents. DBFI is a subdomain of the digital forensics domain, which deals with database files and dictionaries to identify, acquire, preserve, examine, analyze, reconstruct, present, and document database incidents. Several frameworks and models have been offered for the DBFI field in the literature. However, these specific models and frameworks have redundant investigation processes and activities. Therefore, this study has two aims: (i) conducting a compressive survey to discover the challenges and issues of the DBFI field and (ii) developing a Unified forensic model for the database forensics field. To this end, the design science research (DSR) method was used in this study. The results showed that the DBFI field suffers from many issues such as the lack of standardization, multidimensional nature, heterogeneity, and ambiguity, making it complex for those working in this domain. In addition, a model was proposed in this paper, called the Unified Forensic Model (UFM), which consists of five main stages: initialization stage, acquiring stage, investigation stage, restoring and recovering stage, and evaluation stage. Each stage has several processes and activities. The applicability of UFM was evaluated from two perspectives: completeness and implementation perspectives. UFM is a novel model covering all existing DBFI models and comprises two new stages: the recovering and restoring stage and the evaluation stage. The proposed UFM is so flexible that any forensic investigator could employ it easily when investigating database incidents.

Due to the advancement and easy accessibility to computer and internet technology, network security has become vulnerable to hacker threats. Ransomware is a frequently used malware in cyber-attacks to trick the victim users to expose sensitive and private information to the attackers. Consequently, victims may not be able to access their data any longer until they pay a ransom for stolen files or data. Different methods have been introduced to overcome these issues. It is evident through an extensive literature review that some lexical features are not always sufficient to detect categories of malicious URLs. This paper proposes a model to detect Ransomware using machine and deep learning approaches. This model was introduced as a novel feature for classification using the idea that starts with “https://www.” This feature was not considered in the earlier papers on malicious URLs identification. In addition, this paper introduced a novel dataset that consists of 405,836 records. Two main experiments were carried out utilizing malicious URL features to defend Ransomware using the proposed dataset. Moreover, to enhance and optimize the experimental accuracy, various hyper-parameters were tested on the same dataset to define the optimal factors of every method. According to the comparative and experimental results of the applied classification techniques, the proposed model achieved the best performance at 99.8% accuracy rate for detecting malicious URLs using machine and deep learning.

The number of social media users has increased. These users share and reshare their ideas in posts and this information can be mined and used by decision-makers in different domains, who analyse and study user opinions on social media networks to improve the quality of products or study specific phenomena. During the COVID-19 pandemic, social media was used to make decisions to limit the spread of the disease using sentiment analysis. Substantial research on this topic has been done; however, there are limited Arabic textual resources on social media. This has resulted in fewer quality sentiment analyses on Arabic texts. This study proposes a model for Arabic sentiment analysis using a Twitter dataset and deep learning models with Arabic word embedding. It uses the supervised deep learning algorithms on the proposed dataset. The dataset contains 51,000 tweets, of which 8,820 are classified as positive, 37,360 neutral, and 8,820 as negative. After cleaning it will contain 31,413. The experiment has been carried out by applying the deep learning models, Convolutional Neural Network and Long Short-Term Memory while comparing the results of different machine learning techniques such as Naive Bayes and Support Vector Machine. The accuracy of the AraBERT model is 0.92% when applying the test on 3,505 tweets.