Abdul Sajid Mohammed- PhD Computer Science & Technology
- PostDoc Research Scholar at University of the Cumberlands
Abdul Sajid Mohammed
- PhD Computer Science & Technology
- PostDoc Research Scholar at University of the Cumberlands
Research
About
35
Publications
3,651
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135
Citations
Introduction
Hello, I'm Abdul Sajid Mohammed, PhD a research scholar and my passion lies at the intersection of a diverse array of technological domains.
My research interests span a wide spectrum of cutting-edge fields, including Cloud Infrastructure and Automation, Big Data, Information Systems and Data Analytics, AI Machine Learning, Cloud Computing and Security, Kubernetes, Cluster Management, Cloud Networking, Cloud Engineering, Cloud Native, and Distributed Systems.
Current institution
Education
April 2019 - May 2023
Publications
Publications (35)
This paper investigates the effect of heuristic algorithms on cybersecurity hazard detection and mitigation. To be able to do so, the authors analyze the performance of two heuristics-based algorithms-the simple Heuristic set of rules (SHA) and greedy Heuristic set of rules (GHA)-on 4 distinct datasets. The datasets contain malicious and non-malici...
Breast cancer is the most common cancer and the main cause of cancer-related deaths in women around the world. Early detection reduces the number of deaths. Automated breast ultrasound (ABUS) is a new and promising screening method for examining the entire breast. Volumetric ABUS examination is time-consuming, and lesions may be missed during the e...
Using hybrid loss in the UNet++ model. • Presenting a comparison between three common models in classification and segmentation. • Using the three-dimensional structure of the input data. A B S T R A C T Breast cancer is the most common cancer and the main cause of cancer-related deaths in women around the world. Early detection reduces the number...
Artificial Intelligence (AI) has also started to be regularly introduced in cloud-based systems as it can help improve performance and skyrocket efficiency. It is a challenge for such systems to schedule tasks in real time, and computing sources need to be allocated promptly and efficiently. The current scheduling mechanisms being relatively static...
The advent of artificial intelligence (AI) has driven an emergence in applications demanding online responses to immense amounts of data. Typically, the cloud-based deployment of these applications requires resource management to be optimized for high performance while meeting cost efficiency. However, conventional static resource allocation method...
The specification provides a new, resource-efficient method of Dynamic Workload Balancing in AI-driven Real-time Applications over Cloud Infrastructure. The real-time application keeps processing data at high speeds, and it is too difficult to get or make this type of arrangement using our traditional cloud setups as the system employs artificial i...
The integration of machine learning (ML) models into various sectors has revolutionized industries by enabling advanced data analytics, pattern recognition, and decision-making processes. However, the increasing adoption of ML technologies also raises concerns about their vulnerability to cyber-attacks. Adversarial attacks, data poisoning, and mode...
As businesses increasingly depend on these cloud-based services, secure cloud computing is vital. There is no free way with the cloud network, and our information is susceptible to vulnerability. Mitigation strategies to address these network vulnerabilities in cloud computing and analysis of the man-in-the-middle attack and Distributed Denial of S...
Clever healthcare has turned out to be an increasingly more crucial assignment inside the area of healthcare due to its capability to decorate affected person care transport. Using AI-driven diagnostic decision guide systems is one way to permit more green, correct, and regular healthcare decisions. The structures use records from clinical informat...
Medical selection aid structures (CDSSs) are facts systems designed to assist healthcare experts in the decision-making manner. Conventional CDSSs rely upon analytical fashions and understanding-based algorithms to guide choices. However, the latest advances have visible the emergence of AI-pushed CDSSs that utilize artificial intelligence (AI) to...
This abstract provides a state-of-the-art review of secure access control schemes for cloud networks. The rise of cloud computing and the security of the cloud have increased the need for robust access control for securing data and resources. In this paper, role-based, attribute-based, multi-factor authentication access control methods and their st...
Deep learning is a quick-developing subfield of machine studying. It has made big improvements in diverse areas consisting of computer imagination and prescient, natural language processing, and self-reliant driving. Recently, it has additionally been used to improve healthcare strategies, and this has caused greater efficient and price-effective r...
Cellular ad Hoc Networks (MANETs) have become increasingly popular in cell computing packages, inclusive of cellular computing, fitness care, automobile, and army packages. As such, safety protocols used in the transmission of records over MANETs are of essential importance for their hit usage. In this paper, we endorse an evaluation of safety prot...
The increasing use of cloud computing has also resulted in a higher vulnerability to cyber attacks. Network Intrusion Detection Systems (NIDS) play a vital role in protecting cloud computing environments from these attacks. However, the unique characteristics of cloud computing, such as its dynamic and distributed nature, pose challenges for tradit...
https://www.registered-design.service.gov.uk/find/6399110
IncidentoAlert emerges as a groundbreaking solution in the realm of accident detection and alert systems (ADAS), integrating cutting-edge technologies such as AI, financial analytics, cloud computing, IoT, Big Data, and sensor networks. In the event of an accident, IncidentoAlert serves as a pivotal lifeline, rapidly detecting incidents and orchest...
JISAR is published online (https://jisar.org) in connection with the ISCAP (Information Systems and Computing Academic Professionals) Conference, where submissions are also double-blind peer reviewed. Our sister publication, the Proceedings of the ISCAP Conference, features all papers, teaching cases and abstracts from the conference. (https://isca...
This scholarly work presents a research and exploration on the employment of nanorobots in diagnosis of neurochemotherapy, a multifield condition involving nanotechnology, neurology, and oncology. The Nanorobots include self-operated machines having size of 1-100 nanometres, thus can play a significant role in early detection of Brain Tumours and a...
Artificial Intelligence (AI) rapidly transforms healthcare by enhancing patient-centric experiences and enabling personalized care plans. This paper examines how AI technologies like machine learning, deep learning, natural language processing, and computer vision are changing the healthcare industry. These technologies are making diagnostics more...
A face mask vending machine is a device that allows customers to purchase face masks without coming into contact with a store employee. It typically uses a combination of touch less payment technology, facial recognition technology, and an inventory management system. The vending machine allows customers to choose the type of face mask they would l...
Customer Relationship Management (CRM) has evolved into a critical strategy for businesses aiming to sustain competitive advantages in customer retention and personalized engagement. The advent of machine learning (ML) has enabled a proactive CRM approach, wherein predictive models anticipate customer behavior and detect churn patterns before custo...
A disease outbreak is challenging to predict. However, with the recent Covid-19 case, many governments are trying to develop a model and use data to predict its effects. As a result, AI is most widespread in the healthcare field, particularly with the spread of Coronavirus. Indeed, technology influences healthcare across many sectors by automating...
The training of large-scale generative models, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), presents unique challenges due to their computational intensity and memory requirements. These models often require significant hardware resources, distributed frameworks, and scalable environments to manage vast datase...
This study explores the potential of machine learning to uncover revenue opportunities through the integration of enriched datasets and robust validation techniques. Modern businesses often struggle to leverage their data effectively to discover untapped customer segments or optimize their revenue streams. By combining original data with third-part...
