Saddam Hussain Khan- Phd
- Assistant Professor at University of Engineering and Applied Sciences
Saddam Hussain Khan
- Phd
- Assistant Professor at University of Engineering and Applied Sciences
Assistant Professor
About
59
Publications
45,702
Reads
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1,024
Citations
Introduction
Dr. Saddam Hussain Khan is an Assistant Professor in the Department of Computer System Engineering at the University of Engineering and Applied Sciences (UEAS), Swat, Pakistan.
Current institution
University of Engineering and Applied Sciences
Current position
- Assistant Professor
Publications
Publications (59)
Forecasting speculative stock prices is essential for effective investment risk management and requires innovative algorithms. However, the speculative nature, volatility, and complex sequential dependencies within financial markets present inherent challenges that necessitate advanced techniques. In this regard, a novel framework, ACB-XDE (Attenti...
Climate change, along with deforestation as well increasing CO2 emissions, increases the intensity of floods in Pakistan. The common flood forecasting systems do not take into consideration essential environmental features, such as land use alterations and meteorological variables. This study applies machine learning models to develop and improve t...
Recent advancements in detecting tumors using deep learning on breast ultrasound images (BUSI) have demonstrated significant success. Deep CNNs and vision-transformers (ViTs) have demonstrated individually promising initial performance. However, challenges related to model complexity and contrast, texture, and tumor morphology variations introduce...
Deep Convolutional Neural Networks (CNNs) have significantly advanced deep learning, driving breakthroughs in computer vision, natural language processing, medical diagnosis, object detection, and speech recognition. Architectural innovations including 1D, 2D, and 3D convolutional models, dilated and grouped convolutions, depthwise separable convol...
Computed tomography (CT) scans are widely used for diagnosing lung infections, but manual interpretation is laborious. Artificial intelligence has spurred the development of efficient computer-aided diagnostic (CAD) systems, promising faster and more accurate diagnosis. However, many existing CAD systems lack sufficient cross-data analysis and cons...
Monkeypox (MPox) has emerged as a significant global concern, with cases steadily increasing daily. Conventional detection methods, including polymerase chain reaction (PCR) and manual examination, exhibit challenges of low sensitivity, high cost, and substantial workload. Therefore, deep learning offers an automated solution; however, the datasets...
Brain tumor classification is essential for clinical diagnosis and treatment planning. Deep learning models have shown great promise in this task, but they are often challenged by the complex and diverse nature of brain tumors. To address this challenge, we propose a novel deep residual and region-based convolutional neural network (CNN) architectu...
Brain tumor detection in medical image processing presents a formidable challenge due to the complex behavior exhibited by these tumors. Their intricate nature arises from a variety of shapes and textures within brain tumor images, exacerbated by their diverse cellular origins. This complexity is further compounded by the presence of noisy images,...
Alzheimer diseases (ADs) involves cognitive decline and abnormal brain protein accumulation, necessitating timely diagnosis for effective treatment. Therefore, CAD systems leveraging deep learning advancements have demonstrated success in AD detection but pose computational intricacies and the dataset minor contrast, structural, and texture variati...
Medical image segmentation plays a crucial role in various healthcare applications, enabling accurate diagnosis, treatment planning, and disease monitoring. Traditionally, convolutional neural networks (CNNs) dominated this domain, excelling at local feature extraction. However, their limitations in capturing long-range dependencies across image re...
COVID-19, a novel pathogen that emerged in late 2019, has the potential to cause pneumonia with unique variants upon infection. Hence, the development of efficient diagnostic systems is crucial in accurately identifying infected patients and effectively mitigating the spread of the disease. However, the system poses several challenges because of th...
In December 2019, the global pandemic COVID-19 in Wuhan, China, affected human life and the worldwide economy. Therefore, an efficient diagnostic system is required to control its spread. However, the automatic diagnostic system poses challenges with a limited amount of labeled data, minor contrast variation, and high structural similarity between...
COVID-19 is a new pathogen that first appeared in the human population at the end of 2019, and it can lead to novel variants of pneumonia after infection. COVID-19 is a rapidly spreading infectious disease that infects humans faster. Therefore, efficient diagnostic systems may accurately identify infected patients and thus help control their spread...
Security issues are threatened in various types of networks, especially in the Internet of Things (IoT) environment that requires early detection. IoT is the network of real-time devices like home automation systems and can be controlled by open-source android devices, which can be an open ground for attackers. Attackers can access the network, ini...
Brain tumor classification is crucial for clinical analysis and an effective treatment plan to cure patients. Deep learning models help radiologists to accurately and efficiently analyze tumors without manual intervention. However, brain tumor analysis is challenging because of its complex structure, texture, size, location, and appearance. Therefo...
Malaria is a potentially fatal plasmodium parasite injected by female anopheles mosquitoes that infect red blood cells and millions worldwide yearly. However, specialists' manual screening in clinical practice is laborious and prone to error. Therefore, a novel Deep Boosted and Ensemble Learning (DBEL) framework, comprising the stacking of new Boos...
Malaria is a potentially fatal plasmodium parasite injected by female anopheles mosquitoes that infect red blood cells and millions worldwide yearly. However, specialists' manual screening in clinical practice is laborious and prone to error. Therefore, a novel Deep Boosted and Ensemble Learning (DBEL) framework, comprising the stacking of new Boos...
Malaria is a potentially fatal plasmodium parasite injected by female anopheles mosquitoes that infect red blood cells and millions worldwide yearly. However, specialists' manual screening in clinical practice is laborious and prone to error. Therefore, a novel Deep Boosted and Ensemble Learning (DBEL) framework, comprising the stacking of new Boos...
Brain tumor classification is crucial for clinical analysis and an effective treatment plan to cure patients. Deep learning models help radiologists to accurately and efficiently analyze tumors without manual intervention. However, brain tumor analysis is challenging because of its complex structure, texture, size, location, and appearance. Therefo...
Implementing an accurate and reliable passenger detection and counting system is important for the correct distribution of the available transport system over the different paths. The research aims to develop an accurate computer vision-based system alternative to the sensor or contact-based mechanisms to detect, track and count passengers. The pro...
Automatic segmentation of shoulder muscle MRI is challenging due to the high variation in muscle size, shape, texture, and spatial position of tears. Manual segmentation of tear and muscle portion is hard, time-consuming, and subjective to pathological expertise. This work proposes a new Region and Edge-based Deep Auto-Encoder (RE-DAE) for shoulder...
Tumor-infiltrating lymphocytes are specialized lymphocytes that can detect and kill cancerous cells. Their detection poses many challenges due to significant morphological variations, overlapping occurrence, artifact regions and high-class resemblance between clustered areas and artifacts. In this regard, a Lymphocyte Analysis Framework based on De...
The usage of social media applications such as Youtube, Facebook, and other applications is rapidly increasing with each passing day. These applications are used for uploading informational content such as images, videos, and voices, which results in exponential traffic overhead. Due to these overheads (high bandwidth consumption), the service prov...
In December 2019, the global pandemic COVID-19 in Wuhan, China, affected human life and the worldwide economy. Therefore, an efficient diagnostic system is required to control its spread. However, the automatic diagnostic system poses challenges with a limited amount of labeled data, minor contrast variation, and high structural similarity between...
Ransomware attacks pose a serious threat to Internet resources due to their far-reaching effects. It’s Zero-day variants are even more hazardous, as less is known about them. In this regard, when used for ransomware attack detection, conventional machine learning approaches may become data-dependent, insensitive to error cost, and thus may not tack...
Interaction between devices, people, and the Internet has given birth to a new digital communication model, the internet of things (IoT). The integration of smart devices to constitute a network introduces many security challenges. These connected devices have created a security blind spot, where cybercriminals can easily launch attacks to compromi...
Modern technologies are bridging the communication and data analysis gap to connect devices for intelligent operations. Internet of things (IoT) is one such technology that is contributing towards making systems used in our daily life smart. A large-scale prevalence of mobile devices and the ever-increasing popularity of mobile applications and ser...
Malaria is a life-threatening infection that infects the red blood cells and gradually grows throughout the body. The plasmodium parasite is transmitted by a female Anopheles mosquito bite and severely affects numerous individuals within the world every year. Therefore, early detection tests are required to identify parasite-infected cells. The pro...
Medical image segmentation systems play a significant role in assisting radiologists in disease severity, clinical evaluation, and deciding the optimal treatment plan. With the advancements in medical imaging techniques and large volumes of data, deep learning is gaining popularity in medical image segmentation. However, the medical segmentation sy...
Brain tumor analysis is essential to the timely diagnosis and effective treatment of patients. Tumor analysis is challenging because of tumor morphology factors like size, location, texture, and heteromorphic appearance in medical images. In this regard, a novel two-phase deep learning-based framework is proposed to detect and categorize brain tumo...
Autonomous modeling of artificial swarms is necessary because manual creation is a time intensive and complicated procedure which makes it impractical. An autonomous approach employing deep reinforcement learning is presented in this study for swarm navigation. In this approach, complex 3D environments with static and dynamic obstacles and resistiv...
The Coronavirus (COVID-19) outbreak in December 2019 has become an ongoing threat to humans worldwide, creating a health crisis that infected millions of lives, as well as devastating the global economy. Deep learning (DL) techniques have proved helpful in analysis and delineation of infectious regions in radiological images in a timely manner. Thi...
Interaction between devices, people, and the Internet has given birth to a new digital communication model, the Internet of Things (IoT). The seamless network of these smart devices is the core of this IoT model. However, on the other hand, integrating smart devices to constitute a network introduces many security challenges. These connected device...
COVID-19 is a respiratory illness that has affected a large population worldwide and continues to have devastating consequences. It is imperative to detect COVID-19 at the earliest opportunity to limit the span of infection. In this work, we developed a new CNN architecture STM-RENet to interpret the radiographic patterns from X-ray images. The pro...
Brain tumors analysis is important in timely diagnosis and effective treatment to cure patients. Tumor analysis is challenging because of tumor morphology like size, location, texture, and heteromorphic appearance in the medical images. In this regard, a novel two-phase deep learning-based framework is proposed to detect and categorize brain tumors...
Swarm intelligence has been applied to replicate numerous natural processes and relatively simple species to achieve excellent performance in a variety of disciplines. An autonomous approach employing deep reinforcement learning is presented in this study for swarm navigation. In this approach, complex 3D environments with static and dynamic obstac...
Drones are unmanned aerial vehicles (UAV) utilized for a broad range of functions, including delivery, aerial surveillance, traffic monitoring, architecture monitoring, and even War-field. Drones confront significant obstacles while navigating independently in complex and highly dynamic environments. Moreover, the targeted objects within a dynamic...
Drones are unmanned aerial vehicles utilized for a broad range of functions, including delivery, aerial surveillance, traffic monitoring, architecture monitoring and even in War field. Indeed, drones confront significant obstacles while navigating independently in unstable and highly dynamic environments. In comparison with the standard "map-locali...
Background
: Immuno-score, a prognostic measure for cancer, employed in determining tumour grade and type, is generated by counting the number of Tumour-Infiltrating Lymphocytes (TILs) in CD3 and CD8 stained histopathological tissue samples. Significant stain variations and heterogeneity in lymphocytes’ spatial distribution and density make automat...
Malware is a key component of cyber-crime, and its analysis is the first line of defence against cyber-attack. This study proposes two new malware classification frameworks: Deep Feature Space-based Malware classification (DFS-MC) and Deep Boosted Feature Space-based Malware classification (DBFS-MC). In the proposed DFS-MC framework, deep features...
Automatic segmentation of shoulder muscle MRI is challenging due to the high variation in muscle size, shape, texture, and spatial position of tears. Manual segmentation of tear and muscle portion is hard, time-consuming, and subjective to pathological expertise. This work proposes a new Region and Edge-based Deep Auto-Encoder (RE-DAE) for shoulder...
Background: The recent emergence of a highly infectious and contagious respiratory viral disease known as COVID-19 has vastly impacted human lives and overloaded the health care system. Therefore, it is crucial to develop a fast and accurate diagnostic system for the timely identification of COVID-19 infected patients and thus to control its spread...
The new emerging COVID-19, declared a pandemic disease, has affected millions of human lives and caused a massive burden on healthcare centers. Therefore, a quick, accurate, and low-cost computer-based tool is required to timely detect and treat COVID-19 patients. In this work, two new deep learning frameworks: Deep Hybrid Learning (DHL) and Deep B...
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Malicious activities in cyberspace have gone further than simply hacking machines and spreading viruses. It has become a challenge for a nations survival and hence has evolved to cyber warfare. Malware is a key component of cyber-crime, and its analysis is the first line of defence against attack. This work proposes a novel deep boosted hybrid lear...
The presentation of our two works; Covid-19 Analysis Using Chest X-ray and CT Lung images
COVID-19 is a global health problem. Consequently, early detection and analysis of the infection patterns are crucial for controlling infection spread as well as devising a treatment plan. This work proposes a two-stage deep Convolutional Neural Networks (CNNs) based framework for delineation of COVID-19 infected regions in Lung CT images. In the f...
Purpose:
The novel coronavirus (COVID-19) is quickly spreading throughout the world, but facilities in the hospitals are limited. Therefore, diagnostic tests are required to timely identify COVID-19 infected patients, and thus reduce the spread of COVID-19.
Methods:
The proposed method exploits the learning capability of the convolutional neural ne...
Implementing accurate and reliable passenger detection and counting system is an important task for the correct distribution of available transport system. The aim of this paper is to develop an accurate computer vision-based system to track and count passengers. The proposed passenger detection system incorporates the ideas of well-established det...
A churn prediction system guides telecom service providers to reduce revenue loss. Development of a churn prediction system for a telecom industry is a challenging task, mainly due to size of the data, high dimensional features, and imbalanced distribution of the data. In this paper, we focus on a novel solution to the inherent problems of churn pr...
This paper introduces a new application of co PSXWHUYLVLRQ�7RWKHEHVWRIWKHDXWKRU¶VNQRZOHGJH�LWLVWKHILUVWattempt to incorporate computer vision techniques into room interior designing. The computer vision based interior designing is achieved in two steps: object identification and color ass ignment. The image segmentation approach is used for the ide...
Questions
Questions (25)
Dear Author,
Warm Greetings from Academic 2023!
Congratulations, based on the quality of your recent publication, you are provisionally selected for the Research Award and recommended by our scientific committee. In this regard, we welcome you to nominate your short research profile through an online submission system of the International event.
Selected Category: Best Researcher Award
Online Nomination:
Note:
Submit your updated profile under the selected category.
Submission is peer-reviewed by editorial members.
Stanford University has recently published an update of the list of the top 2% most widely cited scientists in 2022, the World’s Top 2% Scientists.
Fields
1. Artificial Intelligence
2. Cyber security
3. Medical Diagnosis
4. Internet of Things
What is the main difference between the survey paper and the review paper? Which one has the more impact? Please, explain.
Elsevier BV, Stanford University, Top Pakistani Researchers Ranked, 2021 (careers)
Stanford University List Released, Top Researcher Ranks, 2021(Career) (Copied)
Elsevier BV, Stanford University, Top Researcher Ranks, 2021 (Year-wise)
TOP 400 Computer Science and Electrical Engineering JCR Journal List
COVID, getting and causing severe infection due to
1. Smoking
2. Polluted Environment
Reasons:
1. Smoker
2. Drinker
3. Polluted environment
