Mesopotamian Journal of Computer Science

Numerous domains have been transformed by the communication technologies made possible by the
Internet of Things (IoT), one of which is health monitoring systems. Patterns associated with diseases
and health conditions can be identified through the utilization of machine learning and cutting-edge AI
techniques. Currently, scientific endeavours are concentrated on enhancing IoT-enabled applications
such as medical report administration, prescription traceability, and infectious disease surveillance
through the amalgamation of blockchain technology (BCT) and machine learning (ML) models.
Although recent advancements have attempted to increase the adaptability of blockchain (BC) and ML
for IoT applications, there are still a number of crucial considerations that must be made for improved
outcomes. This report provides a comprehensive examination of emerging technologies in the healthcare
sector, encompassing the IoT, and blockchain.

Artificial intelligence (AI) is a pivotal technological advancement developed by humans with the aim of
enhancing the quality of human existence. It signifies the capacity of a computerized machine
resembling a robot to execute tasks typically performed by humans and replicate human behavior.
Machine learning (ML), a subfield of AI, involves the construction of systems that acquire the ability to
make predictions about new output values by leveraging existing data, without the need for human
interaction. Currently, ML has been incorporated into various fields, including but not limited to medical
diagnosis, image processing, prediction, classification, learning association, commerce, finance, and
natural language processing. This project aims to employ ML techniques within the human resource
(HR) department. The implemented model will enable the human resources department to effectively
identify the most appropriate candidates for a job opening throughout the recruitment process, utilizing
a comprehensive dataset and considering many criteria, all without the need for manual intervention. The
construction of the model involves the utilization of an authentic dataset comprising recruitment tasks.
Initially, the dataset undergoes a process of selecting the most pertinent elements from both pre-existing
and extracted factors. These selected factors include educational level, age, and past experience.
Furthermore, taking into consideration these aforementioned factors, a decision system is constructed
utilizing the Binary classification approach. The logistic regression classifier is utilized in this
investigation. Subsequently, the dataset is partitioned into two distinct subsets, namely the training subset
and the testing subset. The effectiveness of the model is demonstrated by the utilization of various
evaluation metrics, including the confusion matrix, recall, precision, accuracy, and F-measure values

Objective: Examine Ghana’s National Health Insurance Act (Act 650) to identify coverage gaps limiting
artificial intelligence (AI) therapy access and address medical negligence liability issues surrounding
automated healthcare systems.
Methods: Legal and regulatory analysis of Act 650 were conducted, review of academic literature on
global uptake of AI interventions and medical negligence principles were elucidated, examination of case
studies implementing pilot AI therapy programs under insurance schemes were considered.
Results & Conclusions: Act 650 lacks clear provisions for funding innovative AI treatments with proven
efficacy and undefined negligence determination guidelines involving AI systems, contributing to
accessibility and accountability issues. Proposed amendments to reimburse certain AI therapies through
the National Health Insurance Scheme, expand certified provider eligibility, and institute transparent
negligence compensation formulas.
Recommendations: Reform Act 650 to support increased appropriate use of AI healthcare services,
protect patients undergoing automated diagnosis/treatment, and clarify liability rules for medical
negligence incidents relating to AI.
Novelty & Significance: First extensive analysis focused on opportunities for Ghana’s health insurance
framework to catalyze equitable diffusion of advanced AI therapeutics and address emerging legal
challenges and safety risks as automated medicine advances

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreak that is causing
coronavirus disease 2019 is being deemed a pandemic because of its quick spread around the globe.
Because chest X-ray pictures have shown to be beneficial in monitoring a variety of lung disorders, they
have recently been utilized to monitor COVID-19 disease. It takes time to manually analyze a lot of chest
X-ray pictures. Several previous studies have suggested machine-learning (ML)-based techniques for
COVID-19 detection from chest X-ray pictures as a solution to this issue. Though little effort has been
made to use traditional machine learning (ML) methods, the majority of these investigations use deep
learning (DL) based techniques. Conventional ML-based algorithms will be favored for implementation
if they can yield identical outcomes as DL-based methods. In this effort, we constructed four classic MLbased models for COVID-19 identification, driven by the need to close the gap in the literature. The
accuracy rates for the various classification models were as follows, according to the results: 93.4% for
Support Vector Machine (SVM), 93.3% for Random Forest (RF), 90.5% for K-Nearest Neighbors
(KNN), and 87.9% for Decision Tree (DT). The results of the study showed that machine learning-based
algorithms can produce great results for COVID-19 identification by being refined and improved using
several well-known data preparation approaches

Advances in computer vision and machine learning have transformed leaf disease detection by enabling
efficient and accurate identification of subtle disease signs in leaves. Leveraging high-resolution
imaging, pattern recognition algorithms, and deep learning models, researchers and farmers can now
conduct automated detection across various plant species. The development focuses on sophisticated
image processing techniques applied to diverse datasets captured under controlled conditions, ensuring
comprehensive coverage of lighting, time, and weather variations. Expert annotation of infection stages
and types enhances dataset reliability, while pre-processing stages such as resizing and normalization
optimize image consistency for robust model training. Data augmentation techniques enrich dataset
diversity, complemented by feature extraction methods like RGB color analysis, GLCM texture
analysis, and shape descriptors to discern healthy and infected leaves with precision Validation through
K-fold cross-validation ensures model reliability across diverse datasets, culminating in a deployable
application for real-time leaf health monitoring. Results demonstrate significant advancements, with
the proposed model achieving 92% accuracy, surpassing Logistic Regression (87%), Decision Tree
(82%), and Support Vector Machine (79%). Over 10 epochs, the model achieves steady improvements
to 95% training accuracy and 85% validation accuracy, underscoring its effectiveness. Implementing
data augmentation boosts accuracy from 85% to 89%, while analysis of prediction errors refines model
performance for enhanced automated plant health monitoring and precision agriculture applications.
These advancements highlight the transformative impact of technology in safeguarding crop resilience
and optimizing agricultural practices

Wireless sensor networks and Internet of Things devices are revolutionizing the smart agriculture
industry by increasing production, sustainability, and profitability as connectivity becomes increasingly
ubiquitous. However, the industry has become a popular target for cyberattacks. This survey investigates
the role of artificial intelligence (AI) in improving cybersecurity in smart agriculture (SA). The relevant
literature for the study was gathered from Nature, Wiley Online Library, MDPI, ScienceDirect, Frontiers,
IEEE Xplore Digital Library, IGI Global, Springer, Taylor & Francis, and Google Scholar. Of the 320
publications that fit the search criteria, 180 research papers were ultimately chosen for this investigation.
The review described advancements from conventional agriculture to modern SA, including architecture
and emerging technology. It digs into SA’s numerous uses, emphasizing its potential to transform
farming efficiency, production, and sustainability. The growing reliance on SA introduces new cyber
threats that endanger its integrity and dependability and provide a complete analysis of their possible
consequences. Still, the research examined the essential role of AI in combating these threats, focusing
on its applications in threat identification, risk management, and real-time response mechanisms. The
survey also discusses ethical concerns such as data privacy, the requirement for high-quality information,
and the complexities of AI implementation in SA. This study, therefore, intends to provide researchers
and practitioners with insights into AI’s capabilities and future directions in the security of smart
agricultural infrastructures. This study hopes to assist researchers, policymakers, and practitioners in
harnessing AI for robust cybersecurity in SA, assuring a safe and sustainable agricultural future by
comprehensively evaluating the existing environment and future trends.

The objective of this paper is to clarify dynamic clustering, the divisive and agglomerative dynamic
clustering techniques. It fundamentally centers on the concept of the divisive different leveled shapes as
well known as the top-down approach by creating a workflow appear, dendrograms, clustered data table
which accumulated the clusters based the chosen property, and appear the isolated between each cluster
with the assistance of an data mining device called Python. The DIANA dynamic approach utilized data
tests of the list of laborers in a Data Advancement firm to induce clusters from the position column inside
the data test table. In this work, we in addition executed genuine infers by creating bar chart that shows
up the ages of the chosen agent sets plotted against the positions which are the Engineers, Assistants,
Workers and Troughs

Wi-Fi 7, commonly referred to as IEEE 802.11be, is the most recent development in wireless
communication technology. It provides significant improvements in terms of speed, capacity, and
efficiency. The purpose of this study is to investigate Wi-Fi 7, a standard for wireless communication
technology, with a particular focus on the technological advancements and security issues associated
with it. In addition, it offers historical perspectives and investigates the institution's present capabilities
as well as its potential for the future. The purpose of this study is to provide a complete examination of
the development of Wi-Fi technology and its influence on a wide range of industries. This will allow
researchers to look forward to and anticipate future advancements and breakthroughs. In addition, it made
it possible to integrate Wi-Fi 7 in smart homes, intelligent transportation systems, healthcare services,
and industrial automation by providing high-speed connectivity and lowering the amount of latency that
occurs during interactions. Both positive and negative aspects, as well as future trends and developments,
as well as economic consequences for a wide range of sectors, are investigated simultaneously. The
findings of this study shed light on the possible uses of Wi-Fi 7 and its ability to transform a number of
different industries, such as training and essential infrastructure. This review aims to enlighten developers
and decision-makers about the capabilities and consequences of Wi-Fi 7. The objective is to provide a
comprehensive understanding of the technology and promote its appropriate adoption in the current era
of digital technology.

Integrating Machine Learning (ML) techniques into Vehicular Adhoc Networks (VANETs) provides
promising features in autonomous driving and ITS applications. In this paper, DSRC data is used to
evaluate the effectiveness of different ML models, including Naive Bayes, Random Forest, KNN, and
Gradient Boosting, in normal and adversarial scenarios. Since the dataset is relatively imbalanced, the
Synthetic Minority Over-sampling Technique (SMOTE) is employed for sampling, and defensive
distillation for improving model resilience to adversarial perturbations. From the results, it is clear that
models such as Gradient Boosting and Random Forest show high accuracy in both cases, thus showing
the potential of using Machine Learning to improve VANET security and reliability when new threats
appear. Through this research, the significance of the application of ML in the protection of vehicular
communication in order to enhance both traffic safety and flow has been articulated

Segment Anything (SA) is a state-of-the art method for universal object segmentation, which does not
need task-specific training. Herein, we emphasize that SA can overcome the limitations of traditional
segmentation frameworks based on requiring extensive manually annotated datasets and predefined
architectures, as extensively documented in this review. SB supercharges performance and reduces cost
by combining Mutual Information learning with an Efficient Transformer architecture, benefiting from
a substantially larger pool of in-the-wild data. In this paper we review SA and its specific key innovations
generality, resource boundedness, and scalability to large datasets. We also face obstacles such as data
biases, computational complexity, real-world application issues and consider security as well as privacy
in federated learning scenarios. It discusses areas for future research, such as increasing precision and
robustness, incorporating the federated learning aspect and concerns regarding its ethical use in high risk
domains of application. In this review, we highlight the transformative capacity that SA may bring to
volume-wise object segmentation and urge the community to leverage on top of these new venues for a
breakthrough in AI-vision systems.

Potato crop is one of the prominent consumed foods by human beings. When potato crops are infected
by diseases it affects farmers negatively and to run in a loss. Therefore, early detection of the potato crop
disease can play a vital role in minimizing the loss of the farmers. Nowadays, artificial intelligence
technologies, more specifically deep learning techniques, provide solutions to many crops disease-related
problems. However, training deep learning models requires a high computational power and huge
amount of data as they are data hungry models. Also, designing a custom CNN models a difficult task
and there are some variations to be considered. To avoid these difficulties, we adopted two pretrained
models of DenseNet121 and VGG19 through transfer learning approaches. The achieved accuracy for
DenseNet121 and VGG19 models are 82.6% and 98.56% respectively. DenseNet121 model obtained the
average precision, sensitivity, and F1-score of 88.19%, 82.53, and 82.04%, respectively. Whereas
VGG19 yields 98.39% of precision, 98.39% of sensitivity, and 97.26% of F1-score in ternary 3-class
classification (early blight vs healthy vs late blight).

This paper outlines a methodology for motion detection in video surveillance systems, leveraging
advanced algorithms and TCP/IP networks for real-time data acquisition and analysis. The primary focus
is on the implementation of the Three-Frame Difference Algorithm, which detects moving targets by
analyzing the differences between three consecutive video frames. This method significantly reduces
redundant data transmission and storage, addressing the challenges posed by limited wireless network
capabilities. The surveillance model, designed in MATLAB using SIMULINK, integrates computer
vision systems with embedded coders to facilitate effective communication and processing of video data.
The results demonstrate the system's capability to detect motion accurately under varying lighting
conditions, thereby showcasing its potential applications in diverse fields such as security, robotics, and
human-computer interaction.

Credit card fraud detection (FD) protects consumers and financial institutions by identifying suspicious
or unauthorized transactions. To improve security and reduce false positives, fraud detection systems
can analyze transaction data patterns in real time using advanced machine learning (ML) and deep
learning (DL). This paper exploits DL models to detects transactional data which includes anomalies
through Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent
Unit (GRU) to verify data and mitigate fraud. The models used precision, recall, F1-score, and AUC on
a balanced shared 559856-record Kaggle repository dataset. The RNN model detected anomalies with
99.39% accuracy, 0.9939 F1-score, and excellent recall. RNN shows promise as a real-time anomaly
detection method with high performance and low computational cost.

Liver disease (LD) is a world health concern that requires accurate diagnostic methods. This study
proposes an optimized machine learning model (ML) based on BPSO for LD classification using a
shared public dataset from kaggle Indicates to liver patients from India. The paper used six ML models
such as Random Forest (RF), Support Vector Machine (SVM), Dummy Classifier (DC), Extra Trees
Classifier (ET), K-Nearest Neighbors (KNN), and Logistic Regression (LT) to evaluate the
performance. Through observations we detected that ET achieved an accuracy of 79.82%. The BPSO
hyperparameter optimization optimized ET to enhance accuracy to reach 85%. The paper used metrics
such as accuracy, precision, recall, F1 score, and AUC. The results indicate that ML techniques with
optimization have the potential to develop reliable diagnostic tools for LD.