• Khurram Shahzad, Marwan Abu-Zanona, Bassam Mohammad Elzaghmouri, Saad Mamoun AbdelRahman, Ahmed Abdelgader Fadol Osman, Asef Al-Khateeb
USING ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING APPROACHES TO ENHANCE CANCER THERAPY AND DRUG DISCOVERY
This paper looks at how AI and machine learning have been applied over the last ten years to the development of anti-cancer drugs. By speeding up the synthesis of more desirable compounds and the identification of new ones, artificial intelligence (AI) hasdemonstrated substantial contributions to the research and therapy of anti-cancer therapies. Methods: This work is a narrative review that examines numerous uses of AI-based techniques in the development of anti-cancer medications. Results:Future developments in human cancer research and treatment are anticipated to be significantly influenced by AI. Protein-interaction network analysis, drug target prediction, binding site prediction, and virtual screening are examples of innovative techniques. Drug design and screening are enhanced by machine learning, and the use of multitarget drug development approaches has made it possible to develop cancer treatments with fewer side effects. AI does, however, have several drawbacks, such as a heavy reliance on data and a narrow scope of explanation. Interpretable AI models, which combine data and computation in AI-assisted cancer treatment research, will be the new development path in the future.Conclusion:For more than thirty years, computer-aided drug design techniques have been a key component in the advancement of cancer therapies. Artificial intelligence is a new and powerful technology that has the potential to speed up, lower the cost, and improve the efficacy of anti-cancer therapy development.
Journal: Journal of Ayub Medical College, Abbottabad, Pakistan, Volume 36 , Issue 1, Feb 2024.
• Bassam Mohammad Elzaghmouri, yosef Hasan Fayez Jbara, Said Elaiwat, Mohammed Awad Mohammed Ataelfadiel, Ahmed Abdelgader Fadol Osman, Asef Al-Khateeb, Marwan Abu-Zanona, Binod Kumar Pattanayak
IOT Based Model for COVID Detection from CTScan Images Using Deep Learning
The global impact of the COVID-19 pandemic has reached virtually every part of the world, significantly impacting people's health and daily routines. It has disrupted physical activities and necessitates early identification of infected individualsfor proper care. Identifying the disease through radiography and radiology images stands out as one of the quickest approaches. Previous research indicates that COVID-19 patients often exhibit distinct abnormalities in chest radiographs. Radiologists have the ability to detect the existence of COVID-19 by analyzing these images. This study employs a deep learning model that utilizes CT scan images to identify COVID-19 disease in patients. In the beginning, a dataset comprising 746 CT scan images from openly accessible sources is compiled, and then same dataset had been applied for augmentation which creates total 2984 images. Transfer learning is employed to train Convolutional Neural Networks (CNN) using VGG19, enabling the recognition of COVID-19 disease in the examined CT scan images. Additionally, it integrates an IoT-based application and validates the framework. The model undergoes assessment using 521 images for training, 112 images for validation, and the remaining 113 for testing, and then same images created a new dataset using the concept of augmentation and total 2984 images were spitted into 2088 images for training, 449 images for testing and remaining 447 images for validation. The model's efficiency is evaluated by assessing parameters such as precision, recall, FScore, and the confusion matrix. The implementation and validation of the study have been successful.
Journal: Journal of Electrical Systems, Volume 20 , Issue 3, Jan 2024.
• Nisreen Innab, Ahmed Abdelgader Fadol Osman, Mohammed Awad Mohammed Ataelfadiel, Marwan Abu-Zanona , Bassam Mohammad Elzaghmouri, Farah H. Zawaideh, Mouiad Fadeil Alawneh
Phishing Attacks Detection Using Ensemble Machine Learning Algorithms
Phishing, an Internet fraudwhere individuals are deceived into revealing critical personal and account information,
poses a significant risk to both consumers and web-based institutions. Data indicates a persistent rise in phishing
attacks. Moreover, these fraudulent schemes are progressively becoming more intricate, thereby rendering them
more challenging to identify.Hence, it is imperative to utilize sophisticated algorithms to address this issue.Machine
learning is a highly effective approach for identifying and uncovering these harmful behaviors. Machine learning
(ML) approaches can identify common characteristics in most phishing assaults. In this paper, we propose an
ensemble approach and compare it with six machine learning techniques to determine the type of website and
whether it is normal or not based on two phishing datasets. After that, we used the normalization technique
on the dataset to transform the range of all the features into the same range. The findings of this paper for all
algorithms are as follows in the first dataset based on accuracy, precision, recall, and F1-score, respectively:Decision
Tree (DT) (0.964, 0.961, 0.976, 0.968), Random Forest (RF) (0.970, 0.964, 0.984, 0.974), Gradient Boosting (GB)
(0.960, 0.959, 0.971, 0.965), XGBoost (XGB) (0.973, 0.976, 0.976, 0.976), AdaBoost (0.934, 0.934, 0.950, 0.942),
Multi Layer Perceptron (MLP) (0.970, 0.971, 0.976, 0.974) and Voting (0.978, 0.975, 0.987, 0.981). So, the Voting
classifier gave the best results.While inthe seconddataset, all the algorithms gave the same results in four evaluation
metrics, which indicates that each of them can effectively accomplish the prediction process. Also, this approach
outperformed the previous work in detecting phishing websites with high accuracy, a lower false negative rate, a
shorter prediction time, and a lower false positive rate.
Securing Industrial IoT Environments through Machine Learning-Based Anomaly Detection in the Age of Pervasive Connectivity
In an era characterized by the relentless evolution of Internet of Things (IoT) technologies, marked by the pervasive adoption of smart devices and the ever-expanding realm of Internet connectivity, the IoT has seamlessly integrated itself into our daily lives. This integration has ushered in a new era for manufacturing companies, enabling them to conduct real-time monitoring of their machinery, supervise product quality, and closely monitor environmental variables within their facilities. In addition to the immediate benefits of risk mitigation and loss prevention, this multifaceted approach has provided decision-makers with a comprehensive perspective for making informed decisions. People are now more dependent than ever in IoT devices and services. However, anomalies within IoT networks pose a critical concern despite the IoT's immense potential. These anomalies can pose significant security and safety risks if they go undetected. Identifying and alerting users of these anomalies on time has become crucial for preventing potential damages and losses. In response to this imperative, our research endeavors to utilize the power of Machine Learning and Deep Learning techniques to detect anomalies in IoT networks. We undertake exhaustive experiments with the IoT-23 dataset to validate our methodology empirically. Our research examines an exhaustive comparison of numerous models, assessing their performance and time efficiency to determine the optimal algorithm for achieving high detection accuracy under strict time constraints. This research represents an important step towards enhancing the security of Industrial IoT environments, thereby protecting vital infrastructure and ensuring the integrity of industrial operations in our increasingly interconnected world.
Journal: International Journal of Intelligent Systems and Applications in Engineering (IJISAE), ISSN 1247-6799, Volume 12, Issue 2, Jan 2024
• Ahamd Khader Habboush , Bassam Mohammed Elzaghmouri, Binod Kumar Pattanayak , Saumendra Pattnaik, Rami Ahmad Haboush
An End-to-End Security Scheme for Protection from Cyber Attacks on Internet of Things (IoT) Environment
The Internet of Things (IoT)
technology has recently emerged as a
potential global communication medium that
efficiently facilitates human-to-human,
human-to-machine, and machine-tomachine
communications. Most importantly,
unlike the traditional Internet, it supports
machine-to-machine communication
without human intervention. However,
billions of devices connected to the IoT
environment are mostly wireless, small,
hand-held, and resourced-constrained
devices with limited storage capacities. Such
devices are highly prone to external attacks.
These days, cybercriminals often attempt to
launch attacks on these devices, which
imposes the major challenge of efficiently
implementing communications across the
IoT environment. In this paper, the issue of
cyber-attacks in the IoT environment is
addressed. An end-to-end encryption scheme
was proposed to protect IoT devices from
cyber-attacks.
Journal: Tikrit Journal of Engineering Sciences, ISSN 2312-7589, Volume 30, NO. 4, Dec 2023
• Khurram Shahzad, Marwan Abu-Zanona, Bassam Mohammad Elzaghmouri, Saad Mamoun AbdelRahman, Ahmed Abdelgader Fadol Osman, Asef Al-Khateeb
Enhancing cancer treatment and drug discovery through the implementation of artificial intelligence and machine learning techniques
In normal pharmacological effect screening protocols, natural substances that were
thoroughly diluted and without their active components separated are employed. Over the last two
decades, strong active isomeric compounds have been identified and isolated. The notion of multitarget
treatment was novel in the mid-2000s, but it will be one of the most significant advancements
in drug development by 2021. Instead, then relying on organically generated mixtures, researchers
are looking at target-based drug development based on precisely specified fragments for effective
organic anticancer medicines. This study emphasizes the breakdown of structures utilizing computer
aids or fragments, as well as a process for applying natural anticancer medications. The use of
computer-assisted drug development (CADD) is becoming more frequent. The major areas of this
study were the development of computer-aided pharmaceuticals and anticancer agents. The
discovery of effective all-natural cancer treatments will be accelerated. Multitarget drug
development methodologies have enabled the development of cancer medicines with fewer negative
side effects. Cutting-edge analytical and bioinformatics approaches, particularly machine learning,
will be employed to uncover natural anticancer therapies
Journal: “,Journal of Ayub Medical College, Abbottabad, Pakistan, ISSN 1025-9589 , Volume 36 , Issue 1, Feb 2024
مع تطور العصر الرقمي وتنامي المساعي لبناء مجتمع المعرفة، ظهرت الحاجة
إلى وضع آليات قانونية لمعالجة األخطار الناجمة عن إساءة استخدام تكنولوجيا
المعلومات واالتصاالت وتطبيقاتها المختلفة في الحياة االقتصادية واالجتماعية
والثقافية.
وقد قام المشرع األردني بإصدار قانون الجرائم االلكترونية رقـم )27 )لسنة
2015م، لمعالجة النقص التشريعي في التصدي للجرائم المستحدثة التي تستهدف
أنظمة المعلومات او الشبكة المعلوماتية. سنحاول في هذا البحث التعرف على مدى
كفاية الحماية الجزائية للحق في الحياة الخاصة لألفراد في قانون الجرائم
االلكترونية األردني.
COMBATING THE CRIME OF MONEY LAUNDERING TO FINANCE TERRORISM STUDY IN INTERNATIONAL LAW
The world is facing a huge wave of global terrorism which had destroyed many
countries, therefore it is clear that terrorism could not be that strong political, media
and financial support. One of the most important factors that led to the emergence of
terrorism in the international community is the financial funding that terrorists receive
from various sources. The international community has recognized this funding and has
worked to combat the sources of terrorist financing. However, terrorism funding
sources have tended to send money through banks under the guise of humanitarian,
cultural, religious or charitable aid, the purpose of which is to finance terrorism. This is
called money laundering to finance terrorism. The research includes a brief idea of
terrorism and money laundering and then international legal means to combat this
money laundering, The research was concluded with conclusions and
recommendations
Many Arabic countries are suffering from big waves of International terrorism, to a point that it requires from all
countries to unify their efforts in compacting the international terrorism; Jordan in other hand had played a great
role in compacting it, and as a result many laws had been issued in Jordan criminalizing such terrorist acts.
Not only that, but Jordan had joined the international coalition to compact the International terrorism effectively;
as a result, the Jordanian forces are participating effectively in targeting many terrorist locations around the
world, on the other hand it was targeted by many terrorist attacks resulted in many deaths and casualties among
Jordanian citizens.
In this research I addressed the International terrorism concept and the laws issued in Jordan to compact
terrorism, in addition pinpointing the acts that are considered terrorist acts.
the research ended with a conclusion that include the most important recommendations from the restate point of
view.
THE CULTURAL CHALLENGE IS FACING THE WORLD TRADE ORGANIZATION
The World Trade Organization (WTO) works to unify the cultures among the peoples
of the world. American culture is applied through this organization. This means the
abolition of the cultural specificity of each of the peoples of the world. Many countries
reject this principle and believe that it is a dissolution of their national and historical
identity. These include France and China. The use of the global market as a tool for
breaching the balance of nation-countries in their social protection systems and
programs. This paper deals with the identification of damage caused by the WTO to
the specificity of national peoples. The research concluded with a conclusion and
recommendations.