International Journal of Emerging Multidisciplinaries: Computer Science & Artificial Intelligence

Performance Analysis of symmetric and asymmetric Encryption Algorithms Based on File, Image and Video

Aisha Atib Tijjani , Abdullahi Isa — Mon, 16 Sep 2024 00:00:00 +0000

The rapid evolution of digital technology has exponentially amplified the generation and sharing of diverse digital data, notably files, images, and videos, over the internet, intensifying the critical need for enhanced security measures to safeguard these prevalent data types. This research presents a comprehensive analysis of various encryption algorithms applied to different data types - files, images, and videos. The study categorizes encryption algorithms into symmetric and asymmetric types, with examples including AES, DES, Triple DES, RSA, Diffie-Hellman, and ECC. The paper further explores specific algorithms used for file, image, and video encryption. The objective is to identify the most efficient encryption algorithm for each data type, thereby enhancing data security in the digital age. The paper emphasizes that while encryption is a crucial tool for data security, it should be used in conjunction with other security measures for comprehensive protection.

Optimizing MRI Preprocessing Techniques for Enhanced Alzheimer’s Disease Detection

Doha Yaqoob Rashid Al Khayari, Hiba Abdallah Nasser Al Shamsi — Fri, 31 May 2024 00:00:00 +0000

Alzheimer’s disease (AD) presents an acute global health challenge, with a sharp rise in cases prompting urgent action from the healthcare community. Recognizing the potential of Artificial Intelligence (AI) in early detection and treatment planning, researchers are focusing on optimizing MRI scans, a key diagnostic tool. However, MRI images often suffer from distortions like motion artifact and intensity fluctuations, compromising AI predictions. This study investigates MRI artifacts and proposes preprocessing solutions such as reorientation, registration, skull stripping, and slicing to enhance image quality. Integration into a user-friendly GUI aims to streamline healthcare operations, empowering medical professionals with accurate AD predictions. This research advances early detection efforts, improving patient outcomes and fostering innovation in medical imaging.

AI-Driven Digital Feature Integration in Large-Scale Technology Conglomerates: Case Studies on AI Utilization in Job Portals in The MENA (Middle East and North Africa) Region.

Omar Daraz , Muhammad Ali — Thu, 28 Nov 2024 00:00:00 +0000

This paper explores the boom of AI-based applications and features in the Middle East/North Africa (MENA) region, especially focusing on how artificial intelligence synthesizes product-led growth and drives the development and integration of innovative features in various product domains. Available evidence represents that the boom of AI-led technology development has both led to the creation of several AI startups and has facilitated the construction of digital platforms that are better at adapting to market trends, personalizing content for consumers, and anticipating consumer behavior. We focus on Bayt.com, the largest job portal in MENA, to demonstrate the increasing integration of AI tools and features in large-scale, complex systems, analyzing how this impacts user perception, user behaviors, and other KPIs, such as retention and growth factors. There is sufficient research around AI applications and the types of features that are being incorporated. However, there exist academic gaps in mapping out how exactly AI features are powering applications in regions with different cultural, social, ethical, and business practices, such as MENA and APAC.

A Gas Cylinder Delivery Service in Oman

Hoor Alfulaiti, Ahed Albarhi, Rawan Alhosni, Aayad Alhajj — Sun, 27 Oct 2024 00:00:00 +0000

Gas is one of the basic requirements that must be available in every home, purchased from gas
stations and street vendors. However, issues may arise in reaching gas stations or finding sellers in certain
areas when gas is needed urgently.
Our project aims to create a desktop application for a gas cylinder delivery service, enhancing the
community's experience by facilitating the timely delivery of suitable gas cylinders.
The application lists gas cylinder owners in Omani society, providing customer data like name, phone
number, city, and governorate. Customers can pay using cash and choose cylinder size and price. The app
also determines delivery time based on location. It facilitates communication between buyers and sellers in
Arabic and English.
The gas cylinder delivery service application requires login information like username, phone number, and
city, then opens a location service home page.
The home page features an "Order Now" button, allowing users to choose from a list of gas cylinder sellers,
specifying size and price. After confirmation, customers can choose payment method, delivery location,
and expected delivery time.
Then the seller receives an order containing the buyer's data and a gas cylinder, and the application confirms
the delivery to the customer after completion.
Oman's technological advancements have made society accustomed to mobile phones, software, and
applications, making the location of the gas cylinder delivery service to people easier than the traditional
method of finding sellers.

Verified Views: How Blockchain-enabled Digital Identity Verification Can Combat Fake Accounts and Disinformation on Social Media

Emmanuel Anagu, Sharifatu Gago Ja'afaru, Kelvin Inobemhe — Wed, 27 Nov 2024 00:00:00 +0000

The study was conducted to unravel the ways through which digital identity verification offered in form of blockchain technology can help combat fake accounts and disinformation across social media platforms. The researchers relied on the survey research and elicited quantitative data from respondents. The purposive sampling technique was utilised to select respondents and online link to questionnaires shared with them to complete the survey. Findings of the study showed that users have realised the importance of blockchain technology and have accepted its capacity to ensure security across online spaces. Furthermore, the researchers found that watermarking, content hashing, smart contracts, distributed ledger technology, blockchain-based content management systems, public key cryptography consensus mechanism and data encryption are some of the essential strategies and protocols utilised by media organisations to bolster information reliability. Blockchain technology was also found to be essential in curtailing the spread of disinformation and also enhancing confidence in digital content. The researcher concluded that blockchain technology holds the capacity to improve the integrity of digital identities in contemporary digitised world. Among others, the researchers recommended that media organisations and other stakeholders embark on sensitisation to enlighten the public about the strength of blockchain technology.

UTAS Chatbot

Dhiya AlSaqri, Sumaya AlMuqbali, Sarah AlMamari — Tue, 24 Sep 2024 00:00:00 +0000

AI is the foundation of all computer learning and the way that complex decision- making will proceed in the future. The volume of data produced by machines and people combined today surpasses what humans can process, comprehend, and use to make sophisticated decisions. In cases where artificial intelligence is applied to chatbots, a chatbot is, in essence, a computer program that mimics and interprets spoken or written human conversation, enabling users to engage with digital devices in the same way they would with a real person. A chatbot can be as basic as a one-line program that responds to a simple question, or it can be as sophisticated as a digital assistant that learns and adapts over time as it gathers and analyzes data to provide ever-higher levels of personalization. Aartificial Intelligence Chatbots (are the latest development and are still progressing and evolving. The chatbot is specifically targeted the students at The University of Technology and Applied Sciences. This is a link created using artificial intelligence technology and contains information related to students, such as: Score inquiry, subject inquiry, teacher inquiry. For every subject, the technology can reduce congestion in admissions and registrar offices.

A Multi-Lingual Conversational AI Chatbot for Effective Educational Consultations: A Study of ACE-DS, University of Rwanda

Rimamnuskeb Galadima Kefas, Kizito Nkurikiyeyezu, Lawrence Emmanuel — Wed, 18 Sep 2024 00:00:00 +0000

The demand for real-time consultation services from organizations is increasing, leading to prolonged waiting times, primarily due to limited opportunities for face-to-face interactions and language barriers. This study addresses this challenge by leveraging Artificial Intelligence (AI), Natural Language Processing (NLP), and linguistic technologies to develop a multilingual conversational AI Chabot for managing educational consultation services, using the African Center of Excellence in Data Science (ACE-DS), University of Rwanda, as a case study. Information and frequently asked questions (FAQs) about ACE-DS were used to train a Deep Learning Gated Recurrent Units (GRUs) algorithm to power the Chabot. Language detection and translation APIs were integrated to facilitate seamless multilingual conversations. The result of user survey conducted revealed that over 60% of respondents expressed high satisfaction with the Chabot’s performance including grammar, efficiency, language preferences, and response quality. This study showcases the potential of AI particularly NLP in enhancing educational consultation services, providing a framework for efficient information acquisition.

IoT-based Smart Farming System

Yan Sen Tan, Li Wei Chew , Yi Xuen Tan , Sean Zhuang Tan — Tue, 30 Apr 2024 00:00:00 +0000

The integration of Internet of Things (IoT) technologies into agricultural operations, known as smart farming, presents a transformative opportunity to revolutionize traditional farming methodologies and bolster productivity, efficiency, and sustainability within the agricultural sector. This paper investigates the challenges inherent to conventional farming practices, such as inefficient resource utilization and inadequate access to real-time data to inform decision-making. By leveraging an array of IoT sensors and devices are utilized for the purpose of gathering up-to-date information on various aspects such as environment factors and animal natural behaviors, agricultural producers can gain actionable insights, facilitating data-driven decision-making to optimize resource usage and enhance crop yields. The primary objectives of this study encompass enabling automation and precision agriculture to mitigate waste and bolster productivity, while concurrently emphasizing remote monitoring and control capabilities through mobile technologies to augment overall operational efficiency and crop quality. The background underscores the critical importance of integrating IoT technologies into agricultural practices to streamline farm management processes, reduce labour requirements, and increase profitability across all scales of agricultural operations. Through the implementation of IoT-enabled smart farming solutions, this paper endeavors to bridge the divide between advanced technology and practical agricultural needs, offering a cost-effective and user-friendly approach to modernizing farming methodologies.

Assessing the impact of Machine Learning Algorithms on Portfolio Optimization and Risk Management in the Era of Sustainable Investing

Fahad Masood — Mon, 05 Aug 2024 00:00:00 +0000

The contemporary financial landscape is rapidly evolving, with sustainable investing emerging as a pivotal dimension. Against this backdrop, this research aimed to assess the impact of machine learning algorithms on portfolio optimization and risk management in the era of sustainable investing. Utilizing data from 90 FTSE100 companies that possess an ESG score, multiple machine learning models were employed to understand their efficacy. The Lasso regression model emerged as the top performer, underscoring the potential of machine learning in this domain. A conspicuous gap was identified in the literature, highlighting a limited implementation of machine learning techniques in the financial sector despite their proven capabilities. In an era characterized by the proliferation of big data, the rise of sustainable investing, and unparalleled computational power, relying solely on traditional methods becomes impractical. As manual calculations become increasingly untenable, the seamless integration of machine learning becomes imperative. However, while the preliminary findings are promising, further studies are essential to navigate potential pitfalls and to refine the application of these algorithms in real-world financial settings.

Integrating Smart Technologies and Mobile Applications to Enhance Shopping Mall Parking Experience

Luwe Li Long, Juhwan Lee, Chan Shin Yee, Eng Ee Shen, Bryan Lok Yi-Jie — Sat, 30 Mar 2024 00:00:00 +0000

Several studies found that Malaysian shopping malls have reported that traffic issues happen when entering malls. We have found out that the traditional car parking system is not fully utilised with advanced technologies. Here, we come out with a smart parking system called Smart Park. Smart Park is the evolution of the car parking system by integrating traditional parking systems, IoT devices and mobile applications together. Smart Park is important as it can overcome the traffic issue happening in car parks in Malaysian shopping malls. The goal of this study is to find a solution to save customers ‘time and enhance customers’ parking experience before entering the mall. We aim to solve public issues of wasting time in finding car parking in shopping malls and using advanced technologies to overcome the issues.

Porter Information System Based On Oman.

Fatma Khater Al Badi , Fatma Alzahra Al-Mamari — Sat, 12 Oct 2024 00:00:00 +0000

The Online Porter Service functions as a virtual platform catering to users' needs. Its primary objective is to highlight individuals requiring assistance in transporting their belongings from one location to another. Within this framework, the user inputs specific details regarding the dimensions and pick-up as well as drop-off locations of the shipment. This information is subsequently forwarded to the closest available driver within the vicinity, who then proceeds with the delivery. Our intention is to maintain the driver's presence in the designated area for a certain duration, ensuring their readiness in the event of additional service requests. This approach streamlines and expedites the process for both clients and drivers, facilitating the smooth execution of their tasks.

An Intelligent Analysis and Prediction of Employee Attrition Rate in Healthcare Using Machine Learning Techniques

Fri, 20 Dec 2024 00:00:00 +0000

In the increasingly competitive business environment of the twenty-first century, organizations try to retain their most valuable employees. In the healthcare sector especially, Attrition rates among employees are being affected by a new situation that has arisen as a result of the COVID-19 pandemic and remote working scenarios. Attrition refers to the process by which employees depart from a business for any reason, including their own choice of leave. The application of artificial intelligence (AI) in the past for business expansion and advancement has started influencing human resource management. Improvements to models that predict employee attrition are a constant goal of machine learning (ML) researchers. These models can predict the results of employee turnover, but they have one major drawback: they are mostly used on datasets on attrition in non-healthcare settings. In this study, we offer an exploratory study of using machine learning methodologies to predict employee attrition in healthcare, using the SVM, KNN, XGBoost, RF, and SMOTETOMEK resampling techniques to make healthcare attrition prediction more accurate using the Kaggle dataset of healthcare employees. This gave the healthcare sector a chance to change the narration of cause’s employees leaving the system. The effectiveness of the model was assessed using the area under the curve (ROC), accuracy, recall, and precision metrics. The findings indicate that the SMOTETOMEK Random Forest model exhibits a 98.0% accuracy rate, outperforming the other classification models.

Ghaslah: Car wash application

Qusai alshihi, Alhassan Ali Almutawa , Shaher Khalfan Alshibli — Sat, 09 Nov 2024 00:00:00 +0000

It is known in Oman and many countries that technological devices and artificial intelligence have become a part of daily life. People use them to complete daily tasks and communicate among themselves, as well as facilitating some tasks that require time and effort. Therefore, there are many phone applications that have been developed to serve people, and they were created and developed by Omanis. These applications facilitate the promotion of the tourism, economic, and commercial sectors. Some of them support tourism by showing tourist places and introducing them to tourists, and some show important events in the Sultanate. Which tourists love to go to and supports the economy through the financial returns that result from this trade through these applications. In this project, we proposed creating a mobile application titled Ghaslah. It is an application concerned with car washes. It supports the customer and the owners of car wash companies. It enables the customer to choose the appropriate car wash company and choose the best services that the customer needs. It also supports segments of society, including women, the elderly, and people with special needs in particular. It supports car wash companies by displaying the services provided by the companies at the lowest costs in terms of advertising and other things, enabling companies to provide their services such as reservations and home services. This application will enhance the economic aspect in Oman and the owners of car wash companies and satisfy the customers with services that suit their needs.

Quantum Variational Autoencoders for Predictive Analytics in High Frequency Trading Enhancing Market Anomaly Detection

Jamshaid Basit, Danish Hanif, Madiha Arshad — Wed, 09 Oct 2024 00:00:00 +0000

High-frequency trading (HFT) markets, characterized by high and frequent price fluctuations, necessitate the use of anomaly detection mechanisms to monitor the market and ensure the efficacy of the trading system. This paper aims to discuss the possibility of improving predictive analytics in HFT using quantum computing with the help of the Quantum Variational Autoencoder (QL-VAE). As a result, we propose a new direction for further research on quantum VAEs in HFT that involves their direct comparison with classical VAEs. The application of quantum models for mastering the intensive data flow of HFT is conditioned by the advantages of quantum computation in comparison to classical ones, which are more suitable for handling multidimensional data arrangements and intricate topologies. Our detailed study methodology involved examining various aspects of HFT data, such as order book features and stock price characteristics. We normalized all the data and reduced some of its dimensions. We established quantum VAEs using Pennylane, and configured the classical VAEs using TensorFlow. When it comes to market anomalies, the results of the comparative analysis showed higher accuracy, recall, and F1 rate in quantum VAEs compared to classical models when it comes to the analysis of market anomalies. Therefore, the quantum model's ability to handle high-dimensional data makes it a better fit for HFT than classical methods. These studies suggest that quantum VAEs could significantly improve anomaly detection in the financial market.

Telehealth Monitoring System for Chronic Disease Management

Tue, 30 Apr 2024 00:00:00 +0000

This project introduces a carefully crafted remote health monitoring solution to support chronic illness management in response to the changing healthcare environment. The use of cutting-edge microcontroller technology, the foundation of this all-inclusive solution, is at the core of this breakthrough. Because of the system's ability to operate beyond regional boundaries, users can manage chronic illnesses proactively. The approach promises real-time data capture and communication by seamlessly integrating microcontrollers into the monitoring process, enabling patients and healthcare practitioners alike. This project is a paradigm shift in healthcare, utilizing technology to provide continuous, tailored, and accessible monitoring for improved health and better chronic illness management.

الجريمة الإلكترونية في الواقع العراقي ، دراسة وتحليل

Osamah Nadhim Saadoon Al_Ibadi, Tariq M. Saeed, Subhi Hamadi Hamdoon — Sat, 10 Aug 2024 00:00:00 +0000

The Iraqi Ministry of Interior announced the number of cybercrimes and found that there is a need to reduce them while developing a special law for them because the Iraqi judiciary is still dealing with cybercrime perpetrators in accordance with the Iraqi Penal Code No. 111 of 1969. The enormous and continuous scientific progress in the field of communications in the world requires the legislator to continuously develop The Penal Code was designed to accommodate cases that are in line with technological development, including those related to social media networks. The research was based on a field study directed exclusively at university students (study sample). Students' opinions were surveyed on cybercrime topics, which were divided into personal crimes (insult, slander, fraud, and blackmail), societal crime (targeting a group, not an individual), and international crime (Iraqi security). At the end of the paper, several recommendations were included according to the analysis of the questionnaire.

الخلاصة

صرحت وزارة الداخلية العراقية بعدد الجرائم الإلكترونية وتبين هناك حاجة للحد منها مع وضع تشريع قانون خاص بها لأن القضاء العراقي لازال يتعامل مع مرتكبي الجريمة الإلكترونية وفق قانون العقوبات العراقي رقم 111 لسنة 1969. إن التقدم العلمي الهائل والمستمر بمجال الاتصالات في العالم يفرض على المشرع العراقي التطوير المستمر لقانون العقوبات ليستوعب الحالات التي تتماشى مع التطور التكنولوجي ومنها تلك المتعلقة بشبكات التواصل الإجتماعي.

محاور الجريمة الإلكترونية التي وزعت الى جريمة شخصية (السب أو الشتم والإحتيال والإبتزاز) والجريمة المجتمعية (تستهدف فئة وليس فرد) وجريمة دولية (أمن العراق ), تم بعد ذلك عرض نتائج الإستبيان وتحليل الواقع ورفع التوصيات وفق نتائج إستبيان البحث.