Dr. Mudassir Khan
Dr. Mudassir Khan is currently working as Assistant Professor in the department of Computer Science at College of Science & Arts Tanumah, King Khalid University Abha Saudi Arabia.He has completed his Ph.D. in Computer Science from Noida International University Gautam Budh Nagar (NIU) India. He has completed his Graduation from Aligarh Muslim University, Aligarh and Masters from Gautam Budh Technical University, India.He has more than 12 years of Teaching Experience at the King Khalid University of Saudi Arabia.He has published more than 36 papers in International Journals(SCIE, ESCI,Web of Science, Scopus, Springier and IGI) and conferences( IEEE, Springer Series). He has also published 2 books and 1 patent. He is the Member of various technical/ professional societies such as IEEE, UACEE, Internet Society,IAENG and CSTA.His research interest includes Big data, IoT, deep learning, Computer Security, Cyber Security and Cloud Computing.
Supervisors: Dr. Aadarsh Malviya
Phone: +966503921479 || +917060422120
Address: Assistant Professor || Researcher || Reviewer || Educator || Editor || Academician || Article Writer || BigData || DataScience || Python || Former Administrator(Head) || Department of Computer Science || College of Science & Arts Tanumah || King Khalid University || Saudi Arabia.
Supervisors: Dr. Aadarsh Malviya
Phone: +966503921479 || +917060422120
Address: Assistant Professor || Researcher || Reviewer || Educator || Editor || Academician || Article Writer || BigData || DataScience || Python || Former Administrator(Head) || Department of Computer Science || College of Science & Arts Tanumah || King Khalid University || Saudi Arabia.
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Papers by Dr. Mudassir Khan
concern. Despite the advancement of screening programs, patient selection
and risk stratification pose significant challenges. This study addresses the
pressing need for early detection through a novel diagnostic approach that
leverages innovative image processing techniques. The urgency of early lung
cancer detection is emphasized by its alarming growth worldwide. While
computed tomography (CT) surpasses traditional X-ray methods, a
comprehensive diagnosis requires a combination of imaging techniques. This
research introduces an advanced diagnostic tool implemented through image
processing methodologies. The methodology commences with histogram
equalization, a crucial step in artifact removal from CT images sourced from a
medical database. Accurate lung CT image segmentation, which is vital for
cancer diagnosis, follows. The Otsu thresholding method and optimization,
employing Colliding Bodies Optimization (CBO), enhance the precision of the
segmentation process. A local binary pattern (LBP) is deployed for feature
extraction, enabling the identification of nodule sizes and precise locations.
The resulting image underwent classification using the densely connected
CNN (DenseNet) deep learning algorithm, which effectively distinguished
between benign and malignant tumors. The proposed CBO+DenseNet CNN
exhibits remarkable performance improvements over traditional methods.
Notable enhancements in accuracy (98.17%), specificity (97.32%), precision
(97.46%), and recall (97.89%) are observed, as evidenced by the results from
the fractional randomized voting model (FRVM). These findings highlight the
potential of the proposed model as an advanced diagnostic tool. Its improved
metrics promise heightened accuracy in tumor classification and localization.
The proposed model uniquely combines Colliding Bodies Optimization (CBO) with DenseNet CNN, enhancing segmentation and classification accuracy for
lung cancer detection, setting it apart from traditional methods with superior
performance metrics.
end examination. For this, teachers make lesson plans for year/semester according
to number of working days with a goal to complete syllabus prior to final examination. The lesson plans are made without knowledge of the class attendance for any
particular day, since it is hard for a teacher to make a correct guess. Therefore, when
class strength is unexpectedly low on a given day, the teacher can either postpone
the lecture to next day or continue and let the absent students be at loss. Postponing
the lecture will not complete the syllabus on expected time and letting students be
at loss is also not a solution. This paper will discuss the solution to this problem by
using a Machine Learning Model which is trained with past records of attendance
of students to find a pattern of class attendance and predict accurate class strength
for any future date according to which the lesson plans can be made or modified.
Teachers having prior knowledge of class strength will help them to act accordingly
to achieve their goals.
amount of growth in population constructing usage of social media in their everyday life, the social media statistics are scattered in various disciplines. The footprints of social media data analytics system growth into four different steps, data analysis, data formation, data discovery and data collection. The main objective of this article is to analyze the status and evaluation of social networks on big data. The challenges and difficulties faced during distinct data analysis methods, the distinct stages of research’s data discovery, collection and preparation hardly
exist. The challenge not only to conquer big data although in scrutiny and providing valuable data, which can be used for decision-making. In this article, we review the major challenges faced by researchers to process social media data. The outcomes are frequently used to enlarge an existing framework on social media. This article represents a comprehensive argue of the different studies linked with big data in
social media.
examined above social media. The manuscript focus the topic of requirement of
average value organizes proceed towards that might be employed for every
social networking websites. It is owing to assortment of design of big data
suitable on top of these websites. Matter disclose an confront not merely in
confine of big information but also in examination along with give up of
important information, which involve executive This manuscript analyzes a
catalogued papers set in the area of big information and social media. The article
gives an outline recognizing the issues of value study of big data on social
media, investigating recent skills utilized via social media firm to confine,
examine big data, depicting social media websites, suitable merger of big
information confine and examination techniques with the information value
organize necessities.
over the traditional data, its applications, categorization of big data, its technologies, and analytics techniques.
concern. Despite the advancement of screening programs, patient selection
and risk stratification pose significant challenges. This study addresses the
pressing need for early detection through a novel diagnostic approach that
leverages innovative image processing techniques. The urgency of early lung
cancer detection is emphasized by its alarming growth worldwide. While
computed tomography (CT) surpasses traditional X-ray methods, a
comprehensive diagnosis requires a combination of imaging techniques. This
research introduces an advanced diagnostic tool implemented through image
processing methodologies. The methodology commences with histogram
equalization, a crucial step in artifact removal from CT images sourced from a
medical database. Accurate lung CT image segmentation, which is vital for
cancer diagnosis, follows. The Otsu thresholding method and optimization,
employing Colliding Bodies Optimization (CBO), enhance the precision of the
segmentation process. A local binary pattern (LBP) is deployed for feature
extraction, enabling the identification of nodule sizes and precise locations.
The resulting image underwent classification using the densely connected
CNN (DenseNet) deep learning algorithm, which effectively distinguished
between benign and malignant tumors. The proposed CBO+DenseNet CNN
exhibits remarkable performance improvements over traditional methods.
Notable enhancements in accuracy (98.17%), specificity (97.32%), precision
(97.46%), and recall (97.89%) are observed, as evidenced by the results from
the fractional randomized voting model (FRVM). These findings highlight the
potential of the proposed model as an advanced diagnostic tool. Its improved
metrics promise heightened accuracy in tumor classification and localization.
The proposed model uniquely combines Colliding Bodies Optimization (CBO) with DenseNet CNN, enhancing segmentation and classification accuracy for
lung cancer detection, setting it apart from traditional methods with superior
performance metrics.
end examination. For this, teachers make lesson plans for year/semester according
to number of working days with a goal to complete syllabus prior to final examination. The lesson plans are made without knowledge of the class attendance for any
particular day, since it is hard for a teacher to make a correct guess. Therefore, when
class strength is unexpectedly low on a given day, the teacher can either postpone
the lecture to next day or continue and let the absent students be at loss. Postponing
the lecture will not complete the syllabus on expected time and letting students be
at loss is also not a solution. This paper will discuss the solution to this problem by
using a Machine Learning Model which is trained with past records of attendance
of students to find a pattern of class attendance and predict accurate class strength
for any future date according to which the lesson plans can be made or modified.
Teachers having prior knowledge of class strength will help them to act accordingly
to achieve their goals.
amount of growth in population constructing usage of social media in their everyday life, the social media statistics are scattered in various disciplines. The footprints of social media data analytics system growth into four different steps, data analysis, data formation, data discovery and data collection. The main objective of this article is to analyze the status and evaluation of social networks on big data. The challenges and difficulties faced during distinct data analysis methods, the distinct stages of research’s data discovery, collection and preparation hardly
exist. The challenge not only to conquer big data although in scrutiny and providing valuable data, which can be used for decision-making. In this article, we review the major challenges faced by researchers to process social media data. The outcomes are frequently used to enlarge an existing framework on social media. This article represents a comprehensive argue of the different studies linked with big data in
social media.
examined above social media. The manuscript focus the topic of requirement of
average value organizes proceed towards that might be employed for every
social networking websites. It is owing to assortment of design of big data
suitable on top of these websites. Matter disclose an confront not merely in
confine of big information but also in examination along with give up of
important information, which involve executive This manuscript analyzes a
catalogued papers set in the area of big information and social media. The article
gives an outline recognizing the issues of value study of big data on social
media, investigating recent skills utilized via social media firm to confine,
examine big data, depicting social media websites, suitable merger of big
information confine and examination techniques with the information value
organize necessities.
over the traditional data, its applications, categorization of big data, its technologies, and analytics techniques.
hardware and software solutions, engineers and entrepreneurs. Infosec
professionals, for example forensic researchers, malware analysts and
other cyber-security professionals are included in this group, which are
using, building and testing new technologies for their regular tasks.
Some will have experience in programming, others will have working
knowledge of different security instruments (EnCase for forensics,
Wire shark for network analysis, IDA Pro for reverse engineering,
etc.).
All these disciplines are subject to the scientific method. Cybersecurity can be applied to daily issues including testing for bugs in a new smartphone, endorse company security choices for a limited budget, persuade people that your additional security packaging is better than the competition and balance precision and productivity with intrusion detection. Most people today know more than they did last
year about cyber-security