Wasit Journal of Computer and Mathematics Science

Wireless Sensor Networks (WSNs) consist of large number of small nodes that sense the surround environment and transmit the data to the central collection points. Since these nodes rely on batteries as power source, the effective power management considered as a vital to ensuring that the network continues to operate over long periods. In this research, we offer innovative adaptive activity cycling technique aims to enhance power management in Wireless Sensor Networks. The suggested technique rely on duty cycling concept, where the node is turned on for a certain period and turned off for another period to save energy. Our approach is characterized by dynamically adjusting the on and off periods based on changing network conditions. The adaptive cycling setup according network traffic, so that, in the low traffic cases, the off periods is increased to save power, while these periods decreases in the high traffic cases to ensure transmit data efficiently. In addition, the technique, regard remaining node battery level, which ensure node continue for longer possible period by modifying adapting cycles based on remaining battery level. Simulation tools NS3 and MATLAB are used to evaluate the performance of innovative technique. The results showed, the technique achieve significant enhancements in term energy consumption efficiency comparing with traditional techniques. Furthermore, the technology was able to maintain the quality of service in terms of response time reduction and increase packet delivery ratio. In conclusion, this research demonstrates that using adaptive activity cycling technique can contribute significantly in terms of prolong network lifetime and enhance energy efficiency in Wireless Sensor Networks without sacrificing the quality of service. This technology is promising and can be used in a variety of applications like procession agriculture, environmental monitoring, health care, which support the effectiveness and sustainability of wireless sensor networks in different environments.

In Middle Eastern nations, social media has grown in importance in influencing political and governmental choices. In Iraq, Facebook is regarded as one of the most widely used social networking sites. The underutilization of this tool for evaluating institutional performance persists. Thus, using sentiment analysis on Facebook, this study suggests a methodology that aids organizations like the Ministry of Justice in Iraq in assessing their own performance. The model makes use of a variety of machine learning methods, including Naive Bayes, Logistic Regression, and Support Vector Machine. TF-IDF (Term Frequency-Inverse Document Frequency) was used to convert the textual data into numerical features, which is essential for effective text analysis. Additionally, features were carefully managed by utilizing both unigram and bigram models. Using datasets from (Facebook pages belonging to the Iraqi Ministry of Justice), a thorough experimental investigation was conducted. The results of our experiments showed that the SVM algorithm produced the best accuracy, at 98.311%. following the suggested model's retention of a few stop words, which was shown to significantly improve the algorithm's performance and guarantee accurate categorization of comments while maintaining the content of the phrase.

Fingerprint matching is one of the most important services that related to the issue of identifying individuals, as it can be used to verify the validity of contracts, documents or in other security fields such as criminal investigations. Fingerprints are also one of the most common and accurate forms, as they are easy to obtain, it also has a unique features and cannot be identical to any individuals. The proposed service exploits the advantages provided by cloud computing, such as ease of use, availability of the service anytime and anywhere, and no need to own expensive devices with high specifications, in addition to being low cost. In this service, all fingerprint image preprocessing operations and images enhancement, in addition to the matching, verification and decision-making process are done with the help of tools which provided by cloud computing environment. While the client's role in this service is limited to feeding the system with the fingerprint to be matched. The experimental results proved that this service has achieved high accuracy in matching fingerprint images, in addition to greatly facilitating the matching process for the user, and it can be used anytime and anywhere thanks to the cloud environment facilities adopted by this service.

The electronic communication of today is beset by unwanted junk messages. These are not just a nuisance; they waste our time and energy and clog up our inboxes, which are supposed to be, in many ways, our digital front doors. But how do we keep them from coming? Well, as today's article makes clear, the situation is hardly straightforward. There are various methods that can be used, both traditional and modern, and each has its own set of pros and cons. One method, however, stands out in today's article as one that we think might work well in practice, the good old-fashioned neural network.

The method's novelty derives from its use of neural networks, a type of machine learning, in conjunction with the POA. The POA is a powerful optimization algorithm that can perform very well when fine-tuning the weights and biases of a neural network. We therefore used the POA to optimize these two crucial components of our model and achieved a surprising level of accuracy—98.7%. This figure surpasses the next nearest competitor by 2% and demonstrates the efficacy of our method in spam detection while also highlighting the POA's potential in this field.

The overall proposed method pushes the spam-detection field forward, but it also demonstrates something even broader: the power of hybrid approaches in machine learning. Neural networks are great—but you have to train them properly, and the use of a particle swarm for that purpose is a neat idea. But in the end, a hybrid spam filter is still a spam filter, and this one does what it's supposed to do better than many rivaling methods.

Social engineering techniques are often viewed negatively due to their association with deceptive practices. However, these techniques can also be utilized ethically, as many cybersecurity professionals do, particularly when evaluating vulnerabilities and testing security defenses. This paper presents EDC (Ethical Data Collector), an Android application that utilizes social engineering techniques to discreetly collect valuable data from an Android device for ethical purposes. EDC employs deception through a simulated UI (fake) to engage the target for a period, while secretly collecting data such as device information, active phone number, and images in the background, then sending them to a designated server via the internet. The researcher argues that EDC could help identify inexperienced cybercriminals or extortionists without complex efforts or significant cost, provided that its capabilities are judiciously utilized and subject to proper controls and oversight. EDC's development methodology emphasizes understanding the target's personality, predilections, and preferences to tailor the app experience as required for attracting the target to install and run the application. The paper describes the core functions and workflows for collecting and sending data. Additionally, permissions handling has been addressed as being critical for enabling EDC to collect the required information. Testing on Android emulators demonstrated that the EDC's APK file size is 4 MB, and data collection and transmission processes functioned as intended across various Android versions. The minimum SDK version required to run EDC is level 16. The total estimated time to complete the fake UI process (cumulative user engagement time) is 57-60 seconds, where each activity takes 8 seconds based on the adopted development approach.

Malicious URLs are a very prominent, dangerous form of cyber threats in view of the fact that they can enable many evils like phishing attacks, malware distribution, and several other kinds of cyber fraud. The techniques of detection conventionally applied are based on blacklisting and heuristic analyses, which are gradually becoming inefficient against sophisticated, rapidly evolving threats. In this paper, the authors present various machine learning techniques applied in malicious URL detection. In the present paper, we will look at three machine learning models: Logistic Regression, Random Forest, and Support Vector Machines. We used a methodology that involved collecting data and feature extraction, training a model, then evaluating its performance with different metrics such as accuracy, precision, recall, and F1-score. We implemented and optimized three models—Logistic Regression, Random Forest, and Support Vector Machines (SVM)—based on the literature available that indicates the effectiveness of these models. Logistic Regression shows promising results to detect the malicious URLs, according to Vanitha and Vinodhini. Random Forest models are found to be very robust and accurate according to Cui et al. and Vanhoenshoven et al., SVM models are evidenced to have very high accuracy according to Manjeri et al., Further works on deep learning models emphasized their potentials. In our study, the optimized Random Forest model in our case showed the best performance, and its training accuracy was 99%, while validation accuracy was 90.5%, also logistic Regression and SVM achieved training accuracy was 89.31%, while validation accuracy was 90.5%. All the optimization processes, model performances, and integration into the real-time cybersecurity infrastructures, along with the strengths and limitations, are discussed in this paper. The paper will, therefore, discuss the benefits and challenges for each model in this aspect—emphasizing continuous updating of the models and integrating them into real-time cybersecurity infrastructures.

Recently, there has been a significant research focus on addressing disease prevalence, especially in dealing with the complexities of big data associated with disease data. The challenge is achieving high accuracy due to missing value problems in big data. This study aims to use AI techniques to develop a system that predicates the optimal solutions for disease, regardless of the type of disease, i.e. the system can be applied to any type of disease. The approach involves handling missing values and normalizing disease datasets. The Whale Optimization Algorithm (WOA) will be used to improve predictions for effective disease treatments. We obtained good results for predicting the appropriate treatment for the disease in the proposed research, compared to the results obtained when applying the PSO algorithm before development in state of earlier, where the results obtained in the proposed research had higher accuracy than the results in in state of earlier at high iterations starting from 200 iterations and also had a lower error rate.:

Texture analysis and classification are crucial in many applications such as medical applications, satellite imagery analysis, and so on. This paper presents a new technique that incorporates the LBP with the HOG to improve the texture analysis and classification. LBP is famous for finding out the local texture descriptors by comparing the pixel intensities it deals with and can challenge light variations and image transformations. On the other hand, there is HOG which emphasizes edge and gradient information to detect the orientation and shape of a texture in a given image. While LBP describes the local micro-patterns HOG gives the perspective of the global gradient orientation. The combined use of local and global texture information is helpful in the presented problem when prioritizing the recognition of complex texture patterns that cannot be deciphered by a single algorithm. To demonstrate the efficiency of the proposed hybrid method, several experiments are performed on several texture databases. According to the findings, the proposed LBP-HOG strategy proves more beneficial than basic LBP and HOG techniques concerning the classification rate and computing time. The combination method shows improved performance in terms of agreement with the reference of fine and coarse level of texture descriptions along with providing large-scale description of the texture which results in the improved discriminatory power of the texture classes.

By increasing Internet of Things (IoT) development, it will face new challenges due to its application in various fields of science and industry. One of these challenges is the routing problem in communication which has many effects in various parts and the quality criteria of the IoT in communication. In this research, a new approach provide with an effective and optimal method for routing in the IoT, which will use 6LoWPAN as the default protocol. This protocol has qualitative weaknesses and its goal is to improve and extend it as much as possible with the Markov Reinforcement Learning (MRL). Improving power consumption and reducing energy beside quality of services criteria optimization is the main targets of this approach. The results represented the improvement of the proposed approach compared to other similar protocols such as Z-WAVE and Zig Bee and the classic 6LoWPAN protocol.