Wasit Journal of Computer and Mathematics Science

As topology has become extremely fast, VANETs (vehicular ad-hoc networks) have become increasingly complex, creating new routing protocols for geographic routing. A GPSR-enhanced system (E-GPSR) is presented in this paper, which includes extended capabilities that select the best route and bypass previous vehicles delivering such packets. The E-GPSR protocol also prevents packets from being sent to neighbours similarly, thus eliminating packet routing loops. Road accidents and dead-end roads are unavoidable reasons for link breakage, which the E-GPSR protocol helps to resolve. Simulation of Urban Mobility (SUMO) and Network Simulator-version 3 (NS-3.33) platforms were used to compare E-GPSR with traditional GPSR and Maxduration-Minangle GPSR (MM-GPSR). GPSR and MM GPSR have better packet loss ratios (PLR) and packet delivery ratios (PDR) than the proposed E-GPSR protocol.,

Distributed average consensus represents the amount of computing of inputs held by multiple agents that communicate through peer-to-peer networks. Collaboration among operators is essential for any distributed standard consensus protocol as every specialist needs to contribute to different operators, typically the adjacent (neighbouring) operators. Internet-of-Things (IoT) implementation is challenging because of its heterogeneous, massively distributed nature. The challenges of this challenge can be addressed with blockchain-based platforms and technologies. Testing and evaluation platforms are required for Blockchain deployments in IoT. A realistic and configurable network environment is presented in this paper to evaluate consensus algorithms. Many blockchain
evaluation platforms do not provide a configurable and realistic network environment or are tied to a specific consensus protocol. With our simulator, practitioners can evaluate how consensus algorithms affect network events in congested or contested scenarios to determine the best consensus algorithm. It is proposed to achieve this task by generalizing consensus methods. The Blockchain simulator employs Discrete event network simulations for increased fidelity and scalability. In addition to evaluating the time, state block rate (%), estimation error, average throughput, and simulation time, we evaluate the performance of the proposed techniques based on the number of peer nodes. A comparison of the average transaction delivery rate with a traditional protocol is shown. The proposed protocol has a higher throughput average than the traditional one.

Wireless sensor networks use thousands of sensors to monitor the physical environment. This sensor node consumes a limited amount of energy since it is battery-powered. Researchers regularly develop energy-efficient routing protocols to extend the lifetime of WSNs. The threshold-based cluster head (CH) selection techniques used by hierarchical routing protocols have been shown to extend network lifetime in many studies. Through the implementation of several routing protocols, sensor networks can be made to last longer and consume less energy. Z-SEP introduces a new heterogeneous routing protocol using a hybrid communication mechanism. Others use clustering mechanisms, while others use the base station directly. Energy efficiency and stability are improved by 11% and 19%, respectively, over Z-SEP

Mobile ad hoc networks (MANETs) are wireless networks that can be configured at will. It has no infrastructure and centralized control, so it is only suitable for provisional communications. In a dynamically topological and resource-constrained network, ensuring QoS and security is challenging. MANETs are dynamic networks, so navigating them can be challenging and more susceptible to attacks. MANET requires significant memory, speed, and transmission bandwidth for conventional security measures like cryptographic techniques. Consequently, these methods are unsuitable for identifying malicious behaviour or self-centered nodes. Nodes that are malicious, selfish, or malfunctioning can be identified based on the trust method, which calculates how much trust exists between them. A trust-based QOS-aware routing protocol is proposed in this paper to calculate trust in MANET (I-TQAR). The tree important performance metrics are considered for result validation such as delay, throughput and packet delivery ratio (PDR). I-TQAR offers significantly improved performance in all areas compared to the existing TQR and TQOR protocols.

This bibliometric analysis explores research trends and patterns in the intersection of decision-making and cybersecurity. Using Scopus data, we conducted a systematic search and identified 4,637 relevant documents published between 2018-2024. Quantitative analysis reveals rising annual publications with a peak in 2023, the predominance of journal articles, and robust international collaboration networks. China and the USA lead global scientific production. Key topics include risk assessment, network security, decision support systems, and emerging technologies like machine learning and artificial intelligence. Core journals with high citation impact such as IEEE Access and Expert Systems with Applications highlight significant sources of literature. The study provides a holistic overview of the landscape, evolution, contributors, and themes within decision-making and cybersecurity research.

Lung carcinoma is one of the main causes of deaths over the whole world, causing a global burden of morbidity and mortality. Detecting lung tumors at their early stages can help reducing the risk of having lung cancer. This paper proposes a deep learning algorithm using EfficientNet B3 for lung cancer detection. The purpose is to improve detection accuracy highlighting potential to revolutionize the field of medical imaging and improve patient care. The proposed approach is build based on EfficientNet B3 model to classify four different types of lung cancer. The approach used CT scan images labeled into Normal, Squamous.cell.carcinoma, Large.cell.carcinoma, and Adenocarcinoma for the purpose of lung cancer detection. The results showed that the proposed model provided an improvement rate of 2.13% compared with the best-trained classifier with accuracy of 96%. This model can be generalized to improve lung cancer detection. The finding of deep neural networks, particularly EfficientNet B3, in supporting the diagnosis and detection of the lung disease, particularly in its early times.

In the era of vast and continuous video content creation, manually identifying crucial events becomes a tedious and inefficient task. To address this challenge, we propose a CNN-GRU model that automatically detects and classifies significant events in videos. This model employs ResNet50 Convolutional Neural Networks (CNNs) to extract visual features from video frames, followed by Gated Recurrent Units (GRUs) for temporal modelling and event recognition. By leveraging the sequential data handling capabilities of GRUs, our model captures temporal patterns across frames. We evaluate the model's performance using accuracy and F1-score metrics on the VIRAT dataset, containing 1,555 events across 12 event classes. Our approach achieves promising results, with an event classification accuracy of 75.22%.

In modern communication systems, secret keys are used to secure sensitive information such as personal data, financial transactions and government secrets. However, the security of these systems is only as strong as the secrecy of the key utilized for encrypting and decrypting the data. The use of easy guessable or compromised keys can lead to unauthorized access, data breaches, and identity theft. Additionally, the distribution and management of secret keys present challenges such as key generation, key distribution, key revocation, and key renewal, which can be complex and costly. Therefore, finding secure and efficient methods to manage secret keys is crucial for ensuring the confidentiality, integrity, and availability of sensitive information in today's digital age. Therefore, to improve the security of the symmetric encryption algorithms, The new method to encrypt and decrypt the difference size files using multiple keys is proposed. In addition to investigate the impact of using multiple keys on the security and performance of symmetric algorithms. However, the multiple keys are generated previously using MD5 and SHA2. The chosen algorithms in this study are Blowfish and CAST, with chosen three modes of operation: CBC, CFB and CTR. The results presented in the paper demonstrated that the Blowfish algorithm with generated multiple keys outperforms in the CBC mode despite has the most encryption and decryption time-consuming and memory usage. Where the randomness of Blowfish with mode CBC and CTR is the best throughput then it follows Blowfish with mode CFB.

Modern technology has recently become a meaningful part of all life sectors, as software, sensors, smart machines, and expert systems are successfully integrated into the physical environment. This technology relies in its work on artificial intelligence techniques to make the right decisions at the right time. These technologies have a significant role in improving productivity, product quality, and industry outputs by significantly reducing human labour and errors that humans may cause. Artificial intelligence techniques are increasingly being integrated into animal husbandry and animal revolution management because they provide advantages and means that serve agriculturalists. These techniques monitor the emotional state of animals, milk production and herd management, feeding habits, the movement of animals, and their health status. AI-powered sensors can monitor the health of livestock and detect early signs of illness or stress to which they are exposed. Also, these techniques contribute to assisting agriculturalists in customising feeding programs, reducing waste, and improving product quality. This article will discuss the role of artificial intelligence techniques in animal control, farm management, disease surveillance, and sustainable resource optimisation practices.

Intrusion detection systems are management programs that detect possible attacks on networks and
computers, and usually do so by identifying information in the header of packages. But the cargo of packages
containing the main information can help detect abnormal traffic. This article examines the types of malicious cargo
and the different types of penetration detection systems and the methods offered to detect based on cargo. At the
end of this article, we will also introduce the Metasploit Framework, which is a framework used by testers and even
attackers and contains a variety of ready-made cargoes for different systems

Thousands of real-life ants have been used to improve the Ant Colony Optimization (ACO) technique. Significant improvement can be noticed in segmented images using ACO-based rather than random initialization. The segmentation quality has improved as a result of the noise reduction. Clustering based on FCM is used to segment medical images. To avoid local optimal results, cluster centres are initially determined using ACO. This paper shows that our approach (ACO-FCM) provides significant improvements. In ACO-FCM, brain tissues are classified more accurately because there are more correctly classified pixels. As a result of our approach, such tissues can be classified more accurately based on spatial information

In recent years, there has been a lot of interest in detecting object-based video forgeries. There has been a lack of satisfactory performance with object-based forgery detectors until recently since a majority of them are still based on handcrafted features. There has been a great deal of interest in passive video forensics in recent years. Forgery of video encoded with advanced codec frameworks remains one of the biggest challenges in object-based forgery research. An object-based forgery detection approach is presented in this paper. To evaluate the proposed method, a derived test dataset of variable video lengths and frame sizes is also used in addition to the SYSU-OBJFORG dataset. This process's efficacy is verified by comparing its results with other methods. When tested on datasets with degraded-quality videos, the proposed framework performed better in real-life scenarios.

There is less than a third of clinically competent brain patients in developed and developing countries utilizing brain rehabilitation through exercise. The lack of hospital-based rehabilitation services and long travel times impede participation. As a consequence, calls have been made for the creation of more adaptable substitutes. The creation of a system that enables the transmission of a patient's unique EEG, sample rate, N-GPS based speed, and position, as well as walking-based brain rehabilitation. A programmed handheld mobile device (HMD) transmits these data to a secure server where an exercise scientist can view them in real-time. To evaluate the viability of this approach, 134 brain patients who were unable to engage in hospital-based rehabilitation underwent remotely supervised exercise assessment and exercise sessions. Completion rates, technical participation, the ability to spot EEG changes, and the six-minute walk test conducted both before and after the participation. The device's efficiency and speed were praised. The participants were able to finish a six-week exercise-based rehabilitation program while they were on the go or close to their homes or places of employment. The bulk of sessions went off without a hitch or technical issues, however sporadic signal loss in underserved areas occasionally caused issues. There were many post-workout and exercise-related EEG abnormalities found. In all countries with data available, less than a third of clinically competent patients use exercise-based brain rehabilitation.

Medical imaging serves as an indispensable tool for the diagnosis and continuous monitoring of a diverse array of health conditions. A recent and exciting development in this field is the integration of Swarm Intelligence (SI) algorithms, which draw inspiration from the collective behaviors observed in social insects. This collaborative effort between nature and technology is progressively transforming medical image analysis, elevating both its quality and efficiency. In this book chapter we have presented various SI optimization algorithms like ACO, BCO, FA, FSA and WOA in detail. By exploring these algorithms, we aim to provide an in-depth understanding of their respective benefits and limitations when applied to medical image analysis. This knowledge empowers practitioners to choose the most appropriate algorithm for specific tasks, ensuring optimal outcomes. Furthermore, we shed light on SI-Based Segmentation methodologies, elucidating the advantages and constraints associated with these approaches. The ability of SI algorithms to innovate in the realms of image segmentation, feature extraction, and classification is emphasized, with a focus on their potential to enhance diagnostic accuracy and elevate the quality of patient care. One of the most exciting prospects on the horizon is the amalgamation of SI with cutting-edge technologies like deep learning and big data analytics. This union has the potential to revolutionize medical imaging by providing solutions that are not only more accurate and efficient but also highly clinically relevant. As these developments continue to unfold, the synergy between SI and emerging technologies promises to reshape the medical imaging landscape, ultimately enhancing patient care and improving healthcare outcomes in unprecedented ways

Fraudulent internet transactions have caused considerable harm and losses for both people and organizations over time. The growth of cutting-edge technology and worldwide connectivity has exacerbated the rise in online fraud instances. To offset these losses, robust fraud detection systems must be developed. ML and statistical approaches are critical components in properly recognizing fraudulent transactions. However, implementing fraud detection models presents challenges such as limited data availability, data sensitivity, and imbalanced class distributions. The confidentiality of records adds complexity to drawing inferences and constructing improved models in this domain. This research explores multiple algorithms suitable for classifying transactions as either genuine or fraudulent using the Credit Card Fraud dataset. Given the extremely unbalanced nature of the dataset, the SMOTE approach was used for oversampling to alleviate the class distribution imbalance. In addition, feature selection was carried out, and the dataset was divided into training and test data. The experiments utilized NB, RF, and MLP algorithms, all of which demonstrated high accuracy in detecting credit card fraud. MLP method achieved 99.95% accuracy as compared to other methods.