Tanweer Alam
@iu.edu.sa
Full Professor, Department of Computer Science, Faculty of Computer and Information Systems, Islamic University of Madinah, Madinah
islamic university of madinah
RESEARCH, TEACHING, or OTHER INTERESTS
Computer Science, Computer Networks and Communications, Computer Engineering, Computer Science Applications
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Arif Ullah, Saman Yasin, and Tanweer Alam
Springer Science and Business Media LLC
Ruchi Gupta, Anupama Sharma, and Tanweer Alam
Springer Nature Singapore
Ruchi Gupta and Tanweer Alam
Springer Science and Business Media LLC
Danalakshmi Durairaj, Thiruppathy Kesavan Venkatasamy, Abolfazl Mehbodniya, Syed Umar, and Tanweer Alam
Informa UK Limited
Arif Ullah, Hanane Aznaoui, Dorsaf Sebai, Laith Abualigah, Tanweer Alam, and Aziza Chakir
Springer Science and Business Media LLC
Tanweer Alam
Springer Science and Business Media LLC
Tanweer Alam, Arif Ullah, and Mohamed Benaida
Springer Science and Business Media LLC
Ruchi Gupta, Pushpa Singh, Tanweer Alam, and Shivani Agarwal
Springer Science and Business Media LLC
Mohammed Shakeel, Chintala Lakshmana Rao, T Shyam Prasad, Tanweer Alam, Navneet Rawat, and R Kavitha
IEEE
Each and every nation in the globe has very crucial infrastructures that offer crucial services like internet activity, energy, banking and finance, crucial public services, transportation, and water management. For vital infrastructure based on sectors, each nation has a unique strategy. As IoT-based solutions proliferate, these crucial infrastructures now have network and Internet connectivity. Consequently, these crucial systems that are a part of information networks are equally vulnerable to online attacks. It is crucial to recognise the potential cyber-attack types, devise defence strategies, and implement various preventative measures. In the present day, it is critical to protect potential cyberattacks on these vital infrastructures. The most common attacks on crucial infrastructures are looked at in this study, especially those that have recently happened. Security precautions are also discussed with a view to reducing or preventing IP-based intrusions. The Internet of Things is the next step in machine-to-machine connectivity. IoT has made it possible for anything to be online nowadays. IoT also refers to the networking of uniquely identified, pervasive computing devices that have the ability to transmit data over a network without necessarily requiring or machine-to-human interaction.
N. Nasurudeen Ahamed, R. Vignesh, and Tanweer Alam
Springer Science and Business Media LLC
Ruchi Gupta and Tanweer Alam
Springer Science and Business Media LLC
R. Jayanthi, S. Sundararajan, Tanweer Alam, Neha Garg, Rupesh Roshan Singh, and M. Udhayamoorthi
IEEE
For all parties involved in a logistics system, precise data and smart business operations are essential. However, a hurdle to implementation could be posed by a lack of shared trust. Supply chains experience difficulties as a result of a lack of confidence in data sharing, according to several studies. The degree to which each stakeholder has faith in the data they acquire can have a significant impact on management choices. The processing of bitcoin transactions has made extensive use of blockchain technology. It has recently been shown to be successful in building confidence within the Network of Things.By building trust between entities who would otherwise have been distrustful of each other’s data, blockchain technology permits more seamless and secure data sharing. However, businesses may encounter severe delays and the requirement for significant processing resources if the network is not IoT-optimized. Additionally, Traditional consensus algorithms still have severe limitations on the nodes’ ability to agree. Trust is one of the major concerns in blockchain technology and stands as a severe challenge presently. Confidentiality, privacy, data integrity, and scalability analysis can be identified as the main challenges presently.
Neha Anand, Lalit Mohan Pant, Tanweer Alam, Sumit Pundir, Lims Thomas, and U.R. Rakshith
IEEE
As AI offers a suitable response to various challenges associated with this disease, it plays a crucial role in mental health. A fundamental concept of the AI-based mental health remedy and its impact on directly affecting social-emotional learning has also been examined in this study with the appropriate material and references. The many types of AI that may unquestionably aid in general mental health education have already been specifically outlined in this research using all the data from existing books and publications. In addition to this, an AI-based chatbot platform has been built within the software system to identify various health factors that seem to be directly linked to mental health. The main challenges in this regard remained consistent with other AI-related healthcare systems including concerns for privacy and confidentiality apart from data integrity.
Chhaya Nayak, Chintala Lakshmana Rao, Tanweer Alam, Shalini Singh, Shaziya Islam, and Umme Habiba MaginmanI
IEEE
Artificial intelligence (AI) technologies have been recently developed, and their advantages can be observed in a wide range of domains, from image processing to face detection. AI-based approaches can assist opponents to enhance their attack strategies while also improving cyber protection technologies. The efficiency of AI techniques against cyber security issues are evaluated. Here, the primary data and a quantitative study design approach is used. The information is then obtained from software industry professionals. The sample size for this study is 549 people, and it included confirmatory factor analysis, discriminant validity testing, model fundamental analysis, and hypothesis evaluation. All variables' P-values were found to be statistical, with the exception of intelligent agents, which had no meaningful relationship to AI or cyber security. The main issues were availability, geographical locations, study population, and other related variables.
Tanweer Alam
MDPI AG
Advances in technology always had an impact on our lives. Several emerging technologies, most notably the Internet of Things (IoT) and blockchain, present transformative opportunities. The blockchain is a decentralized, transparent ledger for storing transaction data. By effectively establishing trust between nodes, it has the remarkable potential to design unique architectures for most enterprise applications. When it first appeared as a platform for anonymous cryptocurrency trading, such as Bitcoin, on a public network platform, blockchain piqued the interest of researchers. The chain is completed when each block connects to the previous block. The Internet of Things (IoT) is a network of networked devices that can exchange data and be managed and controlled via unique identifiers. Automation, wireless sensor networks, embedded systems, and control systems are just a few of the well-known technologies that power the IoT. Converging advancements in real-time analytics, machine learning, commodity sensors, and embedded systems demonstrate the rapid expansion of the IoT paradigm. The Internet of Things refers to the global networking of millions of networked smart gadgets that gather and exchange data. Integrating the IoT and blockchain technology would be a significant step toward developing a reliable, secure, and comprehensive method of storing data collected by smart devices. Internet-enabled devices in the IoT can send data to private blockchain networks, creating immutable records of all transaction history. As a result, these networks produce unchangeable logs of all transactions. This research looks at how blockchain technology and the Internet of Things interact to understand better how devices can communicate with one another. The blockchain-enabled Internet of Things architecture proposed in this article is a useful framework for integrating blockchain technology and the Internet of Things using the most cutting-edge tools and methods currently available. This article discusses the principles of blockchain-based IoT, consensus methods, reviews, difficulties, prospects, applications, trends, and communication between IoT nodes in an integrated framework.
Tanweer Alam
Springer Science and Business Media LLC
Tanweer Alam and Mohamed Benaida
Slovenian Association Informatika
Shamimul Qamar, Abdul Azeem, Tanweer Alam, and Izhar Ahmad
Springer Science and Business Media LLC
Tanweer Alam and Ruchi Gupta
MDPI AG
Federated learning (FL) is a cutting-edge artificial intelligence approach. It is a decentralized problem-solving technique that allows users to train using massive data. Unprocessed information is stored in advanced technology by a secret confidentiality service, which incorporates machine learning (ML) training while removing data connections. As researchers in the field promote ML configurations containing a large amount of private data, systems and infrastructure must be developed to improve the effectiveness of advanced learning systems. This study examines FL in-depth, focusing on application and system platforms, mechanisms, real-world applications, and process contexts. FL creates robust classifiers without requiring information disclosure, resulting in highly secure privacy policies and access control privileges. The article begins with an overview of FL. Then, we examine technical data in FL, enabling innovation, contracts, and software. Compared with other review articles, our goal is to provide a more comprehensive explanation of the best procedure systems and authentic FL software to enable scientists to create the best privacy preservation solutions for IoT devices. We also provide an overview of similar scientific papers and a detailed analysis of the significant difficulties encountered in recent publications. Furthermore, we investigate the benefits and drawbacks of FL and highlight comprehensive distribution scenarios to demonstrate how specific FL models could be implemented to achieve the desired results.
Tanweer Alam
IGI Global
Exploring the unique blockchain-internet of things (IoT) framework may be an attractive structure for enhancing communications efficiency in the 5G networks. The wireless communication would have been the largest research area that allows users to communicate with each other. Nowadays, high-speed, smart, efficient with many technologies, such as low power consumption, and so on, appear to be available to communicate with each other in today's globe. Throughout this framework, the expansion of fog features is enabled for physical objects within IoT. Several of the challenging issues in the field of wireless communications would be to build a new blockchain-based virtualization system across the IoT architecture. The main purpose of this framework is to connect blockchain technology to the IoT and fogging or maintains the IoT cryptography secured when transactions occurred. This strengthens blockchain and fog to build an effective IoT communication system. The recommended method is an important estimation of the extensive work.
Ruchi Gupta and Tanweer Alam
Springer Science and Business Media LLC
Arif Ullah, Shakeel Ahmad Khan, Tanweer Alam, Shkurte Luma-Osmani, and Mahanz Sadie
Institute of Advanced Engineering and Science
<span>In the health sector, the computer-aided diagnosis (CAD) system is a rapidly growing technology because medical diagnostic systems make a huge change as compared to the traditional system. Now a day huge availability of medical data and it needs a proper system to extract them into useful knowledge. Heart disease accounts to be the leading cause of death worldwide. Heuristic algorithms have been exposed to be operative in supporting making decisions and classification from the large quantity of data produced by the healthcare sector. Classification is a prevailing heuristic approach which is commonly used for classification purpose some heuristic algorithm predicts accurate result according to the marks whereas some others exhibit limited accuracy. This paper is used to categorize the attendance of heart disease with a compact number of aspects. Original, 13 attributes are involved in classifying heart disease. A reasonable analysis of these techniques was done to conclude how the cooperative techniques can be applied for improving prediction accuracy in heart disease. Four main classifiers used to construct heart disease prediction based on the experimental results demonstrate that support vector machine, artificial bee colony (ABC), Bat algorithm, and memory-based learner (MBL) provide efficient results. The accuracy differs between 13 features and 8 features in the training dataset is 1.9% and in the validation, dataset is 0.92% of vector machine which is the most accurate heuristic algorithm. </span>
Abhishek Bhatt, Tanweer Alam, Kantilal Pitambar Rane, Rainu Nandal, Meenakshi Malik, Rahul Neware, and Samiksha Goel
World Scientific Pub Co Pte Ltd
In recent years, the increasing human–computer interaction has spurred the interest of researchers towards facial expression recognition to determine the expressive changes in human beings. The detection of relevant features that describe the expressions of different individuals is vital to describe human expressions accurately. The present work has employed the integrated concept of Local Binary Pattern and Histogram of Gradient for facial feature extraction. The major contribution of the paper is the optimization of the extracted features using quantum-inspired meta-heuristic algorithms of QGA (Quantum-Inspired Genetic Algorithm), QGSA (Quantum-Inspired Gravitational Search Algorithm), QPSO (Quantum-Inspired Particle Swarm Optimization), and QFA (Quantum-Inspired Firefly Algorithm). These quantum-inspired meta-heuristic algorithms utilize the attributes of quantum computing that ensure the adequate control of facial feature diversity with quantum measures and Q-bit superstition states. The optimized features are fed to the deep learning (DL) variant deep convolutional neural network added with residual blocks (DCNN-R) for the classification of expressions. The facial expressions are detected for the KDEF and RaFD datasets under varying yaw angles of –90∘, –45∘, 0∘, 45∘, and 90∘. The detection of facial expressions with varying angles is also a crucial contribution, as the features decrease with the increasing yaw angle movement of the face. The experimental evaluations demonstrate the superior performance of the QFA than other algorithms for feature optimization and hence the better classification of facial expressions.
Tanweer Alam, Abdirahman Ahmed Hadi, and Rayyan Qari Shahabuddin Najam
Springer Science and Business Media LLC
Neelam Singh, Prashant Rohila, Kashif Anwar, Tanweer Alam, and Vandana Rawat
IEEE
Designing a safe electronic voting system offering secrecy with impartial approach while being transparent and flexible is a challenging task. Full Stack decentralized applications are smart contract decentralized application that runs on a Decentralized Blockhain Network. Problems constructing a vote casting software at the Central Web Server are: the votes at the database will be modified and will be counted extra or eliminated entirely, also the supply code at the net server can also be modified at any time. Rather than having a separate central server database we can deploy Blockchain. Blockchain is a network and database which handles all the transaction. Blocks on the network are added if the transaction is valid and the Blocks also ensure that the data distributed across the Blockchain network are valid. In this paper, we will elaborate the design and development of a Decentralized Approach for Election Voting System using Solidity language to make our smart contract. This Decentralized Approach will have the option for the voter to cast a vote in an election by their Ethereum account address which has public and private key address. This approach aims to provide a secure and trusted voting transaction.