Brief Biography
Dr ADNAN NADEEM ALHASSAN received the Ph.D. degree from the Institute for Communication Systems, University of Surrey, United Kingdom., in 2011. He is a Full Professor & the Head of the Department of Computer Science in the Faculty of Computer and Information System (FCIS), Islamic University in Madinah (IUM), Saudi Arabia. He is a Senior Member of IEEE. He has published over 95 papers in international conferences and journals, including a registered Patent & a Book chapter. He has more than 2143 citations for his work. The cumulative Impact Factor of his publication is 88.05 to date. My Researchgate Score is higher than 96% of RG members. He has completed eight funded research projects since 2015 worth SAR 250,000 and applied for 0.1 million SAR research funds as the PI and CO-PI. He is leading Sensing, Imaging & Security Research Group. He received awards related to research, teaching, academic advising, and contribution towards ABET accreditation in his current tenure at Islamic University. He has also contributed to designing and establishing the MS Cyber Security program at Islamic University.
During his pedagogical journey before joining Islamic University, he has won several awards and achievements, including the Best Researcher, Best Academic Advisor & Best Initiative for International Collaboration Award from Islamic University of Madinah. The Foreign Ph.D. Scholarship, the Associate Fellowship of Higher Education Academy (AFHEA), U.K.,in 2009, the 5th HEC Outstanding Research Award 2013/14, and the Best Paper and Best Track Paper Award in the ICICTT 2013 and ICEET 2016 conferences, respectively. He also received the Best Academic Advisor and the Best Researcher Award from FCIS, IUM, in 2018. He received the Nishane-Imtiaz for his outstanding research from FUUAST Pakistan, in 2016.
He has nineteen years of teaching experience at the university level. He joined FUUAST as a lecturer in 2005, and got promoted to Assistant professor in 2010. He served as Head of Postgraduate Programs for two years and the Head of Department of Computer Science for 1.5 years. He has taught a number of courses at BS, Masters, and Ph.D. levels. He has supervised six Masters and four Ph.D. students as Principle and Co-Supervisor including one Ph.D. student in progress. He received his bachelor’s degree BSc and master’s degree MCS in computer science from the Faculty of Science, the University of Karachi with first division.
Dr Adnan’s principal research interest includes IoT, AI, wireless sensors sensor networks applied to healthcare, agriculture, smart cities, crowd management & their security. He has proposed various solutions for network layer security attacks by IDS, IPS, QoS, and reliability issues of Mobile Ad Hoc and sensors Networks. Currently, he is focusing on investigating & providing solutions for real community problems through modeling and assistive technology. He also works on projects on emerging technologies such as Blockchain & IoT challenges and applications. He has presented his research and tutorials in several international conferences and seminars both as the guest speaker and author including IEEE WCNC, IEEE ICUMT, IEEE INMIC, ICON etc. He is a Senior member of the IEEE and Communication Society. He is also serving as an editorial member of international journals, conference session chair, and organizing international conferences.
Internet of Things (IoT), AI, advanced imaging applied to Smart Cities, Agriculture,Healthcare; Security; Intrusion Detection & Response Systems; Machine Learning; BlockChain Technology; Wireless and Body Area Sensor Networks; QoS; Reliability; Security of Mobile Ad Hoc Networks Applications; Modelling & Assistive Technology Applications;
A positive attitude causes a chain reaction of positive thoughts, events and outcomes. It is a catalyst and it sparks extraordinary results
People grow through experience if they meet life honestly and courageously. This is how character is built.
International Islamic University of Madinah, Kingdom of Saudi Arabia.
Federal Urdu University of Arts, Science and Technology, Karachi, Pakistan.Federal Urdu University of Arts, Science and Technology, Karachi, Pakistan.Responsibilities:Conducted theory and lab classes at undergraduate and graduate level related to Computer Networks, Data Structure, Software Engineering, Programming Languages, Network Security, Algorithm Analysis etc.
Institute of Business Administration Sukkur, Sindh, Pakistan.
Usman Insitute of Technology UIT, Hamdard University,Karachi, Pakistan.
University of Surrey, Guildford, United Kingdom.Time passes faster and faster, but with every project I always want to find the next challenge and the next challenge is just as exciting as the previous one
International Academics:
Dr Michael Howarth (University of Surrey, UK), Prof George Pavlou (UCL, UK), Dr Ning Wang (University of Surrey, UK), Dr Qammar Abbasi (James Watt School of Engineering, University of Glasgow, UK), Dr HAfiz Farooq , (King Faisal University, KSA), Prof Khurram Khan (King Saud Univeristy, KSA), Dr Yue Cao (Wuhan University, china), Dr Nadeem Mehmood (University of Karachi, Pakistan), Dr Amjad Pervaiz (SARC, Pakistan), Dr Kamran Arshad (University of Greenwich, UK), Dr. Soamsiri Chantaraskul (Mongkut's University of Technology , Thailand ), Dr Kamran Ahsan (FUUAST, Pakistan), Dr Kashif Rizwan (FUUAST, Pakistan)
International Organizations:
Department of Lost & Found, Masjid e Nabvi ( Madinah Pollice,KSA), Department of Traffic (KSA), Ministry of Interior, KSA, Sindh Agricultural University, Pakistan, University of Karachi, Edhi Foundation, Darul Sukun (Elderly Home), Pakistan, PARC, Agriculture Research Sindh Pakistan, Artificial Blockchain Automations, UK.
Creativity is putting your imagination to work, and it's produced the most extraordinary results in human culture
Azfar S., A.Nadeem , Basit A., “Pest detection and control techniques using wireless sensor networks : A review”, published in Journal of Entomology and Zoology studies, Vol.3, No.2, April 2015, pp 92-99.
T. A. Syed, M. S. Siddique, A. Nadeem, A. Alzahrani, S. Jan and M. A. K. Khattak, "A Novel Blockchain-Based Framework for Vehicle Life Cycle Tracking: An End-to-End Solution," in IEEE Access, vol. 8, pp. 111042-111063, 2020, doi: 10.1109/ACCESS.2020.3002170.
K. Rizwan, A. Nadeem, N.Mehmood ,K.Ahsan “A Case Study of remote monitoring and its integration with emergency services”, accept for publication in 1st International Conference on Emerging Trends and Innovation in Commputing & Technology, 2016.
A.Nadeem. Howarth M., “Protection of MANETs from a range of attacks using an intrusion detection and prevention system”, published in Springer Special issue on Mobile Computing technologies of Telecommunication System Journal, Vol. 52, No.4, 2012, pp 20147-2058,
A.Nadeem and M.Howarth, “A Generalized Intrusion Detection & Prevention Mechanism for Securing MANETs”, Proceeding of IEEE 5th International Conference on Ultra Modern Telecommunications & workshops (ICUMT 09), St Petersburg Russia, Pages 1 -6, October 2009
A.Nadeem. Howarth M., An Intrusion Detection & Adaptive Response Mechanism for MANETs”, published in Elsevier Ad Hoc Networks Journal, Vol .13, 2013, pp 368-380.
A.Nadeem, Howarth M., “A Survey of MANET Intrusion Detection & Prevention Approaches for Network Layer Attacks", published in IEEE Communication Society Journal of Communication Survey and Tutorial, Vol .15, No.4 , pp 2027-2045.
The whole purpose of education is to turn mirrors into windows
Wireless Networks and Security
MCS Computer Science DepartmentComputer Science, Maths, Statistics
Associate Fellow of Higher Education Academy, United Kingdom (FHEA) I hope I raised more questions than I have given answers
Do the thing you're good at. Not many people are lucky enough to be so good at something
Research is creating new knowledge
Higher Education Commission, Islamabad, Pakistan. 
Center for Communication Systems Research (CCSR), University of Surrey Guildford, United Kingdom. 
Department of Computer Science FUUAST , Karachi, Pakistan.I have completed multiple research projects, including, Adaptive Intrusion Detection and Response Mechanism for Mobile Ad Hoc Applications, remote vital sign monitoring & assistance of patients using BASN, Provision of Security, reliability and QoS in BASN applied to healthcare applications. He has proposed various solutions for network layer security attacks by IDS, IPS, QoS and reliability issues of Mobile Ad Hoc and sensors Networks. Currently he is focusing on investigating & providing solutions for real community problems through modeling and assistive technology. He also working on project on emerging technologies such as Blockchain challenges and applications. He has published over 80 peer review papers in international journal and conferences including a patent submission. Total Impact Factor of his publications is 80.15 . He has also presented his research and tutorials in several international conference and seminars including IEEE WCNC, IEEE ICUMT, IEEE INMIC etc. My Researchgate Score is higher than 95% of RG members: (https://www.researchgate.net/profile/Adnan-Nadeem-Al-Hassan/stats ). My research work has been cited more than 1566 times see google scholar: (https://scholar.google.com.pk/citations?user=9fXG_OkAAAAJ&hl=en ). APatent accepted and sealed by the Government of Pakistan, in 2019.
PhD Supervision
| S.No | Name | Thesis title | Status |
| 1 | Dr Amir Mehmood | Self-organized applications of Body Area Sensor in healthcare | Completed in 2021 |
| 2 | Dr Azhar Hussain | Assisting Disabled & Elder Person by Exploiting Mobile Technology & Ubiquitous Computing | Completed in 2021 |
| 3 | Dr Sarwat Iqbal | Context Aware Assistive Technology for Elderly using Mobile Technology | Completed in 2020 |
| 4 | Mr Saeed Azfar | Monitoring & Detection of Cotton Crop Insects and related Disease using WSN & IoT | In Progress |
MS Supervision:
| S.No | Name | Thesis title | University |
| 5 | George Marinos | enial of Service Attack detection in DSR routing protocol in MANETs | MSc (University of Surrey, United Kingdom) |
| 6 | Yu Li | Analysis of Routing protocols for Delay Tolerant Networks | MSc (University of Surrey, United Kingdom) |
| 7 | Abdul Salam | A Class based Quality of Services Model for Body Area Sensor Networks | MS (FUUAST, Pakistan) |
| 8 | Obaid Khan | RPRP: Routing Protocol to Ensure the Reliability in Health Care System | MS (FUUAST, Pakistan) |
| 9 | Anis Ahmed | Sybil Attack Detection in Mobile Ad Hoc Networks (In complete) | MS (FUUAST, Pakistan) |
| 10 | Mr Basil AlHazmi | Securing Face Recognition system using Image Watermarking scheme | FCIS IUM (in Progress) |
Life is 10% what happens to you and 90% how you react to it. Read more at:
SMotionDataSet: Published by Harvard Dataverse (Download -> Dataset:)
Abstract: The dataset is devised to benchmark techniques dealing with human behavior analysis based on multimodal inertial measurement wearable shimmer sensors.
| Data Set Characteristics: | Multivariate, Time-Series | Number of Instances: | 114 | Area: | Computer |
| Attribute Characteristics: | Real | Number of Attributes: | 14 | Date updated | 2019-04-20 |
| Associated Tasks: | Classification
Pattern analysis |
Source:
Dar ul Sukoon, Elderly Care Home, Karachi
EDHI Foundation Old age home, Karachi
Department of Special Education, University of Karachi
Department of Computer Science ,Federal Urdu University of science Art and Technology
Email to whom correspondence should be addressed: adnan.nadeem@iu.edu.sa
Data Set Information:
The dataset comprises body motion recordings for several volunteers of diverse profile while performing certain physical activities. Sensors placed on the subject's waist is used to measure the motion experienced by diverse body parts, namely, acceleration and rate of turn. Data is divided into five age and weight groups categories.
| S. No. | Age Groups | Male | Female | Total |
| 1 | 41 – 50 yrs. | 3 | 3 | 6 |
| 2 | 51 – 60 yrs. | 33 | 30 | 63 |
| 3 | 61 – 70 yrs. | 13 | 6 | 19 |
| 4 | 71 – 80 yrs. | 13 | 3 | 16 |
| 5 | >80 yrs. | 5 | 5 | 10 |
| Total | 67 | 47 | 114 | |
See the db file with groups
Activities: 3
Sensor devices: 1
Subjects: varies
The collected dataset comprises body motion and vital signs recordings for several volunteers of diverse profile while performing 3physical activities (Table 1). Shimmer3 wearable sensors were used for the recordings. The sensors were respectively placed on the subject's waist attached by using elastic straps (as shown in the figure in attachment). The use of multiple sensors permits us to measure the motion experienced by diverse body parts, namely, the acceleration, the rate of turn, thus better capturing the body dynamics. All sensing modalities are recorded at a sampling rate of 50 Hz(normal range), which is considered sufficient for capturing human activity. few session was recorded using a video camera. The activities were collected in an out-of-lab environment with no constraints on the way these must be executed, with the exception that the subject should try their best when executing them.
The activity set is listed in the following:
L1: Standing still (5sec)
L4: Walking (1 min)
L11: Stand to Sitting (3 steps) (time varies)
NOTE: In brackets are the number of repetitions (Nx) or the duration of the exercises (min).
A complete and illustrated description (including table of activities, sensor setup, etc.) of the dataset is provided in the papers presented in the presentation section.
The data collected for each subject is stored in a different log file: 'shimmer XXX.xls'(XXX will be number between 001 to 999). Each file contains the samples (by rows) recorded for all sensors (by columns). The labels used to identify the activities are similar to the abovementioned (e.g., the label for walking is '4').
The meaning of each column is detailed next:
Column 1: Time stamp raw
Column 2: Time stamp in millisecond
Column 3: acceleration raw (X axis)
Column 4: Acceleration cal (X axis)
Column 5: acceleration raw (Y axis)
Column 6: Acceleration cal (Y axis)
Column 7: : acceleration raw (Z axis)
Column 8: Acceleration cal (Z axis)
Column 9: gyro raw (X axis)
Column 10: gyro cal (X axis)
Column 11: gyro raw (Y axis)
Column 12: gyro cal (Y axis)
Column 13: gyro raw (Z axis)
Column 14: gyro cal (Z axis)
*Units: Acceleration (m/s^2), gyroscope (deg/s).
Use of this dataset in publications must be acknowledged by referencing the following :
A.Nadeem, K. Rizwan and N.Mehmood, "SMotion dataset', developed under the project of fall detection system for elderly, available at http://adnan-nadeem.com/data-repository/.
We would appreciate if you send us an email (adnan.nadeem at fuuast dot edu dot pk) to inform us of any publication using this dataset.
SMotionDataSet: Published by Harvard Dataverse (Download -> Dataset:)
Abstract: The dataset is devised to benchmark techniques dealing with human behavior analysis based on multimodal inertial measurement wearable shimmer sensors.
| Data Set Characteristics: | Multivariate, Time-Series | Number of Instances: | 114 | Area: | Computer |
| Attribute Characteristics: | Real | Number of Attributes: | 14 | Date updated | 2019-04-20 |
| Associated Tasks: | Classification
Pattern analysis |
Source:
Dar ul Sukoon, Elderly Care Home, Karachi
EDHI Foundation Old age home, Karachi
Department of Special Education, University of Karachi
Department of Computer Science ,Federal Urdu University of science Art and Technology
Email to whom correspondence should be addressed: adnan.nadeem@iu.edu.sa
Data Set Information:
The dataset comprises body motion recordings for several volunteers of diverse profile while performing certain physical activities. Sensors placed on the subject's waist is used to measure the motion experienced by diverse body parts, namely, acceleration and rate of turn. Data is divided into five age and weight groups categories.
| S. No. | Age Groups | Male | Female | Total |
| 1 | 41 – 50 yrs. | 3 | 3 | 6 |
| 2 | 51 – 60 yrs. | 33 | 30 | 63 |
| 3 | 61 – 70 yrs. | 13 | 6 | 19 |
| 4 | 71 – 80 yrs. | 13 | 3 | 16 |
| 5 | >80 yrs. | 5 | 5 | 10 |
| Total | 67 | 47 | 114 | |
See the db file with groups
Activities: 3
Sensor devices: 1
Subjects: varies
The collected dataset comprises body motion and vital signs recordings for several volunteers of diverse profile while performing 3physical activities (Table 1). Shimmer3 wearable sensors were used for the recordings. The sensors were respectively placed on the subject's waist attached by using elastic straps (as shown in the figure in attachment). The use of multiple sensors permits us to measure the motion experienced by diverse body parts, namely, the acceleration, the rate of turn, thus better capturing the body dynamics. All sensing modalities are recorded at a sampling rate of 50 Hz(normal range), which is considered sufficient for capturing human activity. few session was recorded using a video camera. The activities were collected in an out-of-lab environment with no constraints on the way these must be executed, with the exception that the subject should try their best when executing them.
The activity set is listed in the following:
L1: Standing still (5sec)
L4: Walking (1 min)
L11: Stand to Sitting (3 steps) (time varies)
NOTE: In brackets are the number of repetitions (Nx) or the duration of the exercises (min).
A complete and illustrated description (including table of activities, sensor setup, etc.) of the dataset is provided in the papers presented in the presentation section.
The data collected for each subject is stored in a different log file: 'shimmer XXX.xls'(XXX will be number between 001 to 999). Each file contains the samples (by rows) recorded for all sensors (by columns). The labels used to identify the activities are similar to the abovementioned (e.g., the label for walking is '4').
The meaning of each column is detailed next:
Column 1: Time stamp raw
Column 2: Time stamp in millisecond
Column 3: acceleration raw (X axis)
Column 4: Acceleration cal (X axis)
Column 5: acceleration raw (Y axis)
Column 6: Acceleration cal (Y axis)
Column 7: : acceleration raw (Z axis)
Column 8: Acceleration cal (Z axis)
Column 9: gyro raw (X axis)
Column 10: gyro cal (X axis)
Column 11: gyro raw (Y axis)
Column 12: gyro cal (Y axis)
Column 13: gyro raw (Z axis)
Column 14: gyro cal (Z axis)
*Units: Acceleration (m/s^2), gyroscope (deg/s).
Use of this dataset in publications must be acknowledged by referencing the following :
A.Nadeem, K. Rizwan and N.Mehmood, "SMotion dataset', developed under the project of fall detection system for elderly, available at http://adnan-nadeem.com/data-repository/.
We would appreciate if you send us an email (adnan.nadeem at fuuast dot edu dot pk) to inform us of any publication using this dataset.
Email to whom correspondence should be addressed: adnan.nadeem@iu.edu.sa
This dataset consists of raw and processed images reflecting a highly challenging and unconstraint environment.
This large gathering dataset of images extracted from publicly filmed videos by 24 cameras installed on the premises of Masjid Al-Nabvi, Madinah, Saudi Arabia.
| Item | Description |
| Subject Area or Application area | Computer Vision, Face Recognition, Pattern Recognition |
| Specific application area | Face detection, Personnel identification, tracking of missing persons, Crowd counting in Large Gatherings |
| Type of Data | Videos sequences (Frames), Processed Face images, Annotations |
| How data were acquired | Data was collected from publicly available videos captured from multiple cameras installed in Masjid Al-Nabvi, Madinah, Saudi Arabia |
| Data format | PNG (Portable Network Graphic) files |
| Experimental factors | Indoor/outdoor scenes of large gatherings, variable illumination, various object types, variable crowd density
The number of cameras in consideration is 24 |
| Experimental features | The total number of Frames is4613
The total number of clips is 34 Variable video clip lengths |
| Description of data collection sample | The dataset contains 8 profile images of each of 250 personnel which makes a total of 2000 face images including children, youngsters, and elderlies. |
| Data source location | Masjid Al-Nabvi, Madinah, Kingdom of Saudi Arabia |
| Data accessibility | Dataset mentioned in this article is uploaded by authors and available at: |
| Source Code | The code is uploaded by authors and available at: |
| Related research articles | The research work based on this dataset is available at:
https://doi.org/10.3390/s22031153 and |
The methodology for building the dataset consists of four core phases; that include acquisition of videos, extraction of frames, localization of face regions, and cropping and resizing of detected face regions. The raw images in the dataset consist of a total of 4613 frames obtained from video sequences. The processed images in the dataset consist of the face regions of 250 persons extracted from raw data images to ensure the authenticity of the presented data. The dataset further consists of 8 images corresponding to each of the 250 subjects (persons) for a total of 2000 images. It portrays a highly unconstrained and challenging environment with human faces of varying sizes and pixel quality (resolution). Since the face regions in video sequences are severely degraded due to various unavoidable factors, it can be used as a benchmark to test and evaluate face detection and recognition algorithms for research purposes. We have also gathered and displayed records of the presence of subjects who appear in presented frames; in a temporal context. This can also be used as a temporal benchmark for tracking, finding persons, activity monitoring, and crowd counting in large crowd scenarios.
There are five core phases of the methodology employed to build this large gathering dataset
In the first phase of methodology, we used publicly filmed videos from twenty-four cameras on the premises of Masjid Al-Nabvi, Madinah, KSA.
In the second phase, the data set is prepared by extracting every 10th frame from those captured videos and this extraction was made through the publicly available freeware "Free studio”. The dataset consists of a video sequence of 4613 frames, recorded by the cameras installed on the premises of the Masjid Al-Nabvi.
In the third phase, we applied the viola-jones Local Binary Pattern (LBP) cascade face detection algorithm on these frames to detect and separate face regions.
In the last two phases, the face regions of 250 personnel have been extracted, cropped, and resized to 50 x 50 resolution.
| Parameter | Measurement |
| Total Subjects | 250 |
| Facial Images Resolution | 50 x 50 |
| Number of cropped faced | 250 x 8= 2000 |
| Label Method | Manual |
The organization of the presented dataset is depicted in Fig. 4. The main folder is named ‘Large Gathering Dataset’, which includes the following sub-folders.
video_summary File
| Parameter | Measurement |
| Frame size | 973 X 489 |
| Number of frames | 4613 |
| Number of clips in the video sequence | 34 |
| Number of Cameras | 14 |
| Tracking sequences | Yes |
| Multi-shot | Yes |
| Full frames availability | Yes |
Use of this dataset in publications must be acknowledged by referencing the following :
We would appreciate if you send us an email (adnan.nadeem at Iu dot edu dot SA) to inform us of any publication using this dataset.
