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8. Recent advances in Blockchain Technology: A survey on Applications and Challenges

Author

Hakak, Saqib, Khan, Wazir Zada, Gilkar, Gulshan Amin, Assiri, Basem, Alazab, Mamoun, Bhattacharya, Sweta, and Reddy, G Thippa

Subjects
Computer Science - Computers and Society
Abstract

The rise of blockchain technology within a few years has attracted researchers across the world. The prime reason for worldwide attention is undoubtedly due to its feature of immutability along with the decentralized approach of data protection. As this technology is progressing, lots of developments in terms of identifying new applications, blockchain-based platforms, consensus mechanisms, etc are taking place. Hence, in this article, an attempt has been made to review the recent advancements in blockchain technology. Furthermore, we have also explored the available blockchain platforms, highlighted and explored future research directions and challenges., Comment: The journal we submitted doesn't allow the articles under review to be submitted to arxiv

Published
2020

9. Unmanned Aerial Vehicles in Smart Agriculture: Applications, Requirements and Challenges

Author

Maddikunta, Praveen Kumar Reddy, Hakak, Saqib, Alazab, Mamoun, Bhattacharya, Sweta, Gadekallu, Thippa Reddy, Khan, Wazir Zada, and Pham, Quoc-Viet

Subjects
Electrical Engineering and Systems Science - Signal Processing, Computer Science - Networking and Internet Architecture
Abstract

In the next few years, smart farming will reach each and every nook of the world. The prospects of using unmanned aerial vehicles (UAV) for smart farming are immense. However, the cost and the ease in controlling UAVs for smart farming might play an important role for motivating farmers to use UAVs in farming. Mostly, UAVs are controlled by remote controllers using radio waves. There are several technologies such as WiFi or ZigBee that are also used for controlling UAVs. However, Smart Bluetooth (also referred to as Bluetooth Low Energy) is a wireless technology used to transfer data over short distances. Bluetooth smart is cheaper than other technologies and has the advantage of being available on every smart phone. Farmers can use any smart phone to operate their respective UAVs along with Bluetooth Smart enabled agricultural sensors in the future. However, certain requirements and challenges need to be addressed before UAVs can be operated for smart agriculture-related applications. Hence, in this article, an attempt has been made to explore the types of sensors suitable for smart farming, potential requirements and challenges for operating UAVs in smart agriculture. We have also identified the future applications of using UAVs in smart farming.

Published
2020
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13. Ethical Aspects of Internet of Things from Islamic Perspective

Author

Khan, Wazir Zada, Zahid, Mohammed, Aalsalem, Mohammed Y, Zangoti, Hussein Mohammed, and Arshad, Quratulain

Subjects
Computer Science - Computers and Society
Abstract

The Internet of Things (IoTs) is an evolving new face of technology that provides state of the art services using ubiquitously connected smart objects. These smart objects are capable of sensing, processing, collaborating, communicating the events and provide services. The IoT is a collection of heterogeneous technologies like Sensor, RFID, Communication and nanotechnology. These technologies enable smart objects to identify objects, collect information about their status,communicating the collected information for taking some desired actions. Widespread adaptations of IoT based devices and services raised the ethical challenges for their users. In this paper we highlight ethical challenges raised by IoT and discuss the solutions and methods for encouraging people to properly use these technologies according to Islamic teachings., Comment: 4 pages, 3 figures, 9th IEEE-GCC Conference & Exhibition - 2017

Published
2018

14. Electricity Load Forecasting in Smart Grids Using Support Vector Machine

Author

Ayub, Nasir, Javaid, Nadeem, Mujeeb, Sana, Zahid, Maheen, Khan, Wazir Zada, Khattak, Muhammad Umar, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Barolli, Leonard, editor, Takizawa, Makoto, editor, Xhafa, Fatos, editor, and Enokido, Tomoya, editor

Published
2020
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15. Propagation of Fake News on Social Media: Challenges and Opportunities

Author

Hakak, Saqib, Khan, Wazir Zada, Bhattacharya, Sweta, Reddy, G. Thippa, Choo, Kim-Kwang Raymond, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Chellappan, Sriram, editor, Choo, Kim-Kwang Raymond, editor, and Phan, NhatHai, editor

Published
2020
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19. An Efficient CNN and KNN Data Analytics for Electricity Load Forecasting in the Smart Grid

Author

Aimal, Syeda, Javaid, Nadeem, Islam, Tahir, Khan, Wazir Zada, Aalsalem, Mohammed Y., Sajjad, Hassan, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Barolli, Leonard, editor, Takizawa, Makoto, editor, Xhafa, Fatos, editor, and Enokido, Tomoya, editor

Published
2019
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22. A Low Cost Vision Based Hybrid Fiducial Mark Tracking Technique for Mobile Industrial Robots

Author

Aalsalem, Mohammed Y, Khan, Wazir Zada, and Arshad, Quratul Ain

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Robotics
Abstract

The field of robotic vision is developing rapidly. Robots can react intelligently and provide assistance to user activities through sentient computing. Since industrial applications pose complex requirements that cannot be handled by humans, an efficient low cost and robust technique is required for the tracking of mobile industrial robots. The existing sensor based techniques for mobile robot tracking are expensive and complex to deploy, configure and maintain. Also some of them demand dedicated and often expensive hardware. This paper presents a low cost vision based technique called Hybrid Fiducial Mark Tracking (HFMT) technique for tracking mobile industrial robot. HFMT technique requires off-the-shelf hardware (CCD cameras) and printable 2-D circular marks used as fiducials for tracking a mobile industrial robot on a pre-defined path. This proposed technique allows the robot to track on a predefined path by using fiducials for the detection of Right and Left turns on the path and White Strip for tracking the path. The HFMT technique is implemented and tested on an indoor mobile robot at our laboratory. Experimental results from robot navigating in real environments have confirmed that our approach is simple and robust and can be adopted in any hostile industrial environment where humans are unable to work., Comment: 6 pages, 7 figures

Published
2012

23. A Graphical Password Based System for Small Mobile Devices

Author

Khan, Wazir Zada, Aalsalem, Mohammed Y., and Xiang, Yang

Subjects
Computer Science - Cryptography and Security
Abstract

Passwords provide security mechanism for authentication and protection services against unwanted access to resources. A graphical based password is one promising alternatives of textual passwords. According to human psychology, humans are able to remember pictures easily. In this paper, we have proposed a new hybrid graphical password based system, which is a combination of recognition and recall based techniques that offers many advantages over the existing systems and may be more convenient for the user. Our scheme is resistant to shoulder surfing attack and many other attacks on graphical passwords. This scheme is proposed for smart mobile devices (like smart phones i.e. ipod, iphone, PDAs etc) which are more handy and convenient to use than traditional desktop computer systems., Comment: 10 Pages, 7 figures, 1 Table

Published
2011

24. The Aware Cricket Ground

Author

Khan, Wazir Zada, Aalsalem, Mohammed Y., and Arshad, Quratul Ain

Subjects
Computer Science - Other Computer Science
Abstract

The most profound technologies are those that disappear. They weave themselves into fabrics of everyday life until they are indistinguishable from it [1]. This research work is a mere effort for automated decision making during sports of most common interest leveraging ubiquitous computing. Primarily cricket has been selected for the first implementation of the idea. A positioning system is used for locating the objects moving in the field. Main objectives of the research are to help achieve the following goals. 1) Make Decisions where human eye can make error due to human limitations. 2) Simulate the Match activity during and after the game in a 3D computerized Graphics system. 3) Make various types of game and performance analysis of a certain team or a player., Comment: 5 pages, 5 figure

Published
2011

32. A Hybrid Graphical Password Based System

Author

Khan, Wazir Zada, Xiang, Yang, Aalsalem, Mohammed Y., Arshad, Quratulain, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Xiang, Yang, editor, Cuzzocrea, Alfredo, editor, Hobbs, Michael, editor, and Zhou, Wanlei, editor

Published
2011
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37. Anomalous Situations Recognition in Surveillance Images Using Deep Learning.

Author

Arshad, Qurat-ul-Ain, Raza, Mudassar, Khan, Wazir Zada, Siddiqa, Ayesha, Muiz, Abdul, Khan, Muhammad Attique, Tariq, Usman, Taerang Kim, and Jae-Hyuk Cha

Subjects
DEEP learning, IMAGE recognition (Computer vision), FEATURE extraction, HUMAN activity recognition, ANOMALY detection (Computer security), SUPPORT vector machines, K-nearest neighbor classification, AUTOMOBILE license plates
Abstract

Anomalous situations in surveillance videos or images that may result in security issues, such as disasters, accidents, crime, violence, or terrorism, can be identified through video anomaly detection. However, differentiating anomalous situations from normal can be challenging due to variations in human activity in complex environments such as train stations, busy sporting fields, airports, shopping areas, military bases, care centers, etc. Deep learning models’ learning capability is leveraged to identify abnormal situations with improved accuracy. This work proposes a deep learning architecture called Anomalous Situation Recognition Network (ASRNet) for deep feature extraction to improve the detection accuracy of various anomalous image situations. The proposed framework has five steps. In the first step, pretraining of the proposed architecture is performed on the CIFAR-100 dataset. In the second step, the proposed pre-trained model and Inception V3 architecture are used for feature extraction by utilizing the suspicious activity recognition dataset. In the third step, serial feature fusion is performed, and then the Dragonfly algorithm is utilized for feature optimization in the fourth step. Finally, using optimized features, various Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) based classification models are utilized to detect anomalous situations. The proposed framework is validated on the suspicious activity dataset by varying the number of optimized features from 100 to 1000. The results show that the proposed method is effective in detecting anomalous situations and achieves the highest accuracy of 99.24% using cubic SVM. [ABSTRACT FROM AUTHOR]

Published
2023
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46. Students' Perception and Acceptance of e-Learning and e-Evaluation in Higher Education.

Author

Ahmed, Shakeel, Noor, Ahmad Shukri Mohd, Khan, Wazir Zada, Mehmood, Asim, Shaheen, Reema, and Fatima, Toheed

Subjects
PSYCHOLOGY of students, DIGITAL learning, HIGHER education, TROPICAL medicine, ONLINE education
Abstract

The COVID-19 pandemic has led to increased use of online resources in educational institutions, making e-learning a necessity. This study aimed to investigate how students from four colleges at Jazan University in Saudi Arabia perceived and accepted e-learning and e-evaluation. Methods: 236 students participated in a cross-sectional study conducted in October 2021. The students completed a well-constructed questionnaire with 22 closed-ended questions divided into five domains. The students rated their answers on a 5-point Likert scale from 1 to 5. To analyze the collected data, the researchers utilized SPSS (v26). Result: Out of the total 236 students who participated in the study, most (30.5%) from the College of Public Health and Tropical Medicine (59.8%) used laptops, while 78% of students had a favorable impression of e-learning. Students' perceptions were focused on whether e-learning and e-evaluation methods helped them understand the study material smoothly and clearly. Among the students, 27% strongly agreed, 25% agreed, 16% were neutral, 11% disagreed, and 11% strongly disagreed. Additionally, 28% of the students strongly agreed, and 34% agreed that exam questions during e-evaluation were appropriate and comprehensive. Although online learning may result in less social contact, a lack of social presence, and difficulties in communication harmonization, e-learning still has some positive effects on students. It is considered a powerful platform, especially during emergencies or for those unable to attend in-person classes to complete their studies. The study has significant implications for higher education institutions, especially during emergencies, where online learning is necessary. Future research can further explore the factors that affect student perceptions and acceptance of elearning and e-evaluation and how to improve them. [ABSTRACT FROM AUTHOR]

Published
2023
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50. Antlion re-sampling based deep neural network model for classification of imbalanced multimodal stroke dataset

Author

G, Thippa Reddy, Bhattacharya, Sweta, Maddikunta, Praveen Kumar Reddy, Hakak, Saqib, Khan, Wazir Zada, Bashir, Ali Kashif, Jolfaei, Alireza, Tariq, Usman, G, Thippa Reddy, Bhattacharya, Sweta, Maddikunta, Praveen Kumar Reddy, Hakak, Saqib, Khan, Wazir Zada, Bashir, Ali Kashif, Jolfaei, Alireza, and Tariq, Usman

Abstract

Stroke is enlisted as one of the leading causes of death and serious disability affecting millions of human lives across the world with high possibilities of becoming an epidemic in the next few decades. Timely detection and prompt decision making pertinent to this disease, plays a major role which can reduce chances of brain death, paralysis and other resultant outcomes. Machine learning algorithms have been a popular choice for the diagnosis, analysis and predication of this disease but there exists issues related to data quality as they are collected cross-institutional resources. The present study focuses on improving the quality of stroke data implementing a rigorous pre-processing technique. The present study uses a multimodal stroke dataset available in the publicly available Kaggle repository. The missing values in this dataset are replaced with attribute means and LabelEncoder technique is applied to achieve homogeneity. However, the dataset considered was observed to be imbalanced which reflect that the results may not represent the actual accuracy and would be biased. In order to overcome this imbalance, resampling technique was used. In case of oversampling, some data points in the minority class are replicated to increase the cardinality value and rebalance the dataset. transformed and oversampled data is further normalized using Standardscalar technique. Antlion optimization (ALO) algorithm is implemented on the deep neural network (DNN) model to select optimal hyperparameters in minimal time consumption. The proposed model consumed only 38.13% of the training time which was also a positive aspect. The experimental results proved the superiority of proposed model.

Published
2020

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