Muhammad Attique

Neural Question Generation (NQG) has emerged as a foundation for educational technologies and automated assessments. In this study, we perform a survey of NQG over its emerging era from 2020 to 2026. To assess a pool of 109 highly relevant research articles, we systematically follow a five-phase identification and filtering approach. Using this analysis, the study proposes an exhaustive taxonomy that classifies the recent advancements in NQG based on question generation methodology, generation type, input modality, datasets, application domain, control objectives, and evaluation metrics. The findings indicate that there is a paradigm shift in NQG methodologies from rule-based and syntactic heuristics to highly advanced and popular Transformer-based methodologies, accounting for 58% of the methodologies. The results also indicate that although multiple-choice questions are the most prevalent question type in NQG methodologies, there is an increasing trend towards the generation of cognitively complex questions. Furthermore, we conclude that NQG approaches suffer from a lack of appropriate evaluation measures as well as an inability to generate questions at the higher cognitive order of Bloom’s Taxonomy.

Fish species recognition is essential for ecological studies, fishery management, and marine biology. Accurate detection and categorization are critical for preserving biodiversity, allowing scientists to track species distribution, identify invasive species, and analyze the effects of environmental changes. The fish sector is essential to any country's food and agriculture. Identification of species by the morphology process is both inaccurate and costly. However, the manual process of measuring important details like species identification, length, and quantity is difficult to capture, which shows the need for automation. The merging of automated systems and artificial intelligence has revolutionized this industry. Recent advancements in image detection systems based on machine learning and deep learning have been explored across various domains. Yet, applying state-of-the-art deep model Convolutional Neural Networks (CNNs) to identify the fish species’ complexity of season and location, and limited public datasets pose a challenge for the detection. Machine learning and deep learning use artificial neural networks to simulate how humans think and learn, efficiently automating similar monitoring applications such as species identification on land and in water. You Only Look Once (YOLO) is a state-of-the-art method for object detection based on deep learning. The goal of this study is to develop a deep learning system for recognizing fish species using the YOLO paradigm. The Fish-Pak dataset, which includes information on tropical fish farming in Pakistan, consists of 915 images against 6 targeted classes, freely available at the Mendeley data source. To ensure the suggested YOLO architecture's improved performance on the Fish-Pak data collection, we will conduct an experimental comparison with other versions of YOLO v3 and V4. The total accuracy of fish species identification using the proposed methods is 99%, with an mAP of 99.65%, top performance results as compared to existing literature.

Internet of Things (IoT) applications are switching from general to precise in different industries, e.g., healthcare, automation, military, maritime, smart cities, transportation, logistics, and many more. In the healthcare domain, these applications have demonstrated an incredible improvement in patient assessment, monitoring, prescription, etc., with ease of access through the Internet. Despite its benefits, this technology also offers several security challenges for the research community and healthcare stakeholders, because of its wireless communication and open-area deployment. To explore, patient wearable devices and other networking entities follow unstructured communication format to share their accumulated data in the network, which makes them susceptible to manifold security threats. Considering the significance of these applications, data acquisition, processing, storage, and assessment on client and remote sides need a high standard of secure communication infrastructure. Therefore, security of these applications is one of the major obstacles that prevent their widespread use in different healthcare domains. To discuss different security constraints, in this article, we present a comprehensive survey of the theoretical literature from 2015 to 2023 to highlight the unresolved security problems of this emerging technology. Based on the evaluated literature pros and cons, we determine the security requirements and challenges of Healthcare-IoT (HC-IoT) applications. Following this, we demonstrate future research directions that could be useful for the researchers and industry stakeholders working in this domain. To demonstrate the uniqueness of this work and claim its contribution, we compare our work section-wise with previously published papers to answer the question of reviewers, editors, students, and readers, why this review article is required in the presence of already published review articles.

The proliferation of current and next-generation mobile and sensing devices has increased at an alarming rate. With these state-of-the-art devices, the global positioning system ( GPS ) has made remote sensing and location tracking more viable. One such query is the All Nearest Neighbor ( ANN ) query, which extracts and returns all data objects that are in close vicinity to all query objects. An ANN is a combination of k-nearest neighbors ( kNN ), and join queries. Hence, ANN has useful for applications in different domains such as transportation optimization, locating safe zones, and ride-sharing. An example of its applications is, “ find the nearest gas station for each car parking lot ”. Because these applications are responsible for generating a massive number of query requests, a large amount of computation is required to return these query requests. As a single machine cannot meet this demand in this study, we propose a distributed query processing framework to process ANN queries using the Apache Spark framework. In an empirical study, our proposed framework achieved superior query efficiency and scalability compared to other methods and design alternatives.

Infectious diseases are always alarming for the survival of human life and are a key concern in the public health domain. Therefore, early diagnosis of these infectious diseases is a high demand for modern‐era healthcare systems. Novel general infectious diseases such as coronavirus are infectious diseases that cause millions of human deaths across the globe in 2020. Therefore, early, robust recognition of general infectious diseases is the desirable requirement of modern intelligent healthcare systems. This systematic study is designed under Kitchenham guidelines and sets different RQs (research questions) for robust recognition of general infectious diseases. From 2018 to 2021, four electronic databases, IEEE, ACM, Springer, and ScienceDirect, are used for the extraction of research work. These extracted studies delivered different schemes for the accurate recognition of general infectious diseases through different machine learning techniques with the inclusion of deep learning and federated learning models. A framework is also introduced to share the process of detection of infectious diseases by using machine learning models. After the filtration process, 21 studies are extracted and mapped to defined RQs. In the future, early diagnosis of infectious diseases will be possible through wearable health monitoring cages. Moreover, these gages will help to reduce the time and death rate by detection of severe diseases at starting stage.

In the era of smart healthcare, Internet of Medical Things (IoMT) based Cyber-Physical Systems (CPS) plays an important role, while accessing, monitoring, assessing, and prescribing patients ubiquitously. Efficient authentication and secure data transmission are the influential impediments of these networks to be addressed to maintain credence amidst clients, healthcare specialists, pharmacologists, and other associated entities. To address the authentication and data privacy issues in smart healthcare, in this paper, we proposed a hybrid lightweight authentication scheme leveraging supervised machine learning (SML) technique followed by Cryptographic Parameter Based Encryption and Decryption (CPBE&D) scheme to ensure the validation of legal patient wearable devices with secure transmission over the wireless communication channel. To achieve better results, we enabled the decentralized authentication of legitimate patient wearable devices to minimize computation cost, authentication time, and communication overhead with the help of SML technique to predicate and forward authentication attributes of patient wearable devices to the next concerned trusted authority, when it is shifted from region to another region. Simulation upshots of the SML and CPBE&D scheme exhibit extraordinary security features with cost-effective validation of legal patient wearable devices accompanied by worthwhile communication functionalities compared with the predecessor work.

With the current advancement in the Internet, there has been a growing demand for building intelligent and smart systems that can efficiently address the detection of health-related problems on social media, such as the detection of depression and anxiety. These types of systems, which are mainly dependent on machine learning techniques, must be able to deal with obtaining the semantic and syntactic meaning of texts posted by users on social media. The data generated by users on social media contains unstructured and unpredictable content. Several systems based on machine learning and social media platforms have recently been introduced to identify health-related problems. However, the text representation and deep learning techniques employed provide only limited information and knowledge about the different texts posted by users. This is owing to a lack of long-term dependencies between each word in the entire text and a lack of proper exploitation of recent deep learning schemes. In this paper, we propose a novel framework to efficiently and effectively identify depression and anxiety-related posts while maintaining the contextual and semantic meaning of the words used in the whole corpus when applying bidirectional encoder representations from transformers (BERT). In addition, we propose a knowledge distillation technique, which is a recent technique for transferring knowledge from a large pretrained model (BERT) to a smaller model to boost performance and accuracy. We also devised our own data collection framework from Reddit and Twitter, which are the most common social media sites. Finally, we employed word2vec and BERT with Bi-LSTM to effectively analyze and detect depression and anxiety signs from social media posts. Our system surpasses other state-of-the-art methods and achieves an accuracy of 98% using the knowledge distillation technique.

Five decades have passed since the first bit was transmitted over the internet. Although the internet has improved our lives and led to the digital economy, currently only 51% of the world’s population have access to it. Currently, consumers mostly access the internet via mobile broadband, 2G, 3G, and 4G services. Regulatory bodies such as the Federal Communications Commission (FCC) of the US are responsible for ensuring that consumers receive an adequate service from Mobile Network Operators (MNOs). Usually, regulators evaluate the performance of each MNO in terms of service quality yearly and publish a report. To evaluate performance, metrics such as coverage, download/upload speed, and the number of subscribers can be used. However, the evaluation process and the metrics used by each regulatory body are inconsistent, and this makes it hard to determine which nations are providing adequate services to their citizens. Furthermore, it is not clear as to which performance evaluation is the right path. In this case study, we analyzed the reports released from eight nations (United States of America, United Kingdom, France, South Korea, Japan, Singapore, and Australia) as of the year 2020. We then point out the advantages and the drawbacks of the current evaluation process and metrics. Furthermore, a discussion on why the current methods are not sufficient to evaluate 5G services is presented. Our findings indicate that there is a great need for a unified metric and that this process becomes more complex with the rollout of 5G.

In this article, we present a robust channel categorization scheme to fix data privacy and preservation problems in an Industrial Healthcare Internet of Things (IHC-IoT) network. The proposed model categorizes the transmission bandwidth into four independent channels for each device by defining triggering rules with respect to time for reception and transmission of data. Besides, our developed prototype, which is known as high optimal path channel triggering protocol (HOPCTP) ensures data privacy and preservation utilizing minimal network resources in an IHC-IoT network. Furthermore, the HOPCTP prototype enables client-side devices to transmit and receive data with four different independent communication channels following the triggering mechanism to avoid adversary device anticipation in the network. The categorized channels are triggered with a defined time period to change their transmission and reception functionality, which triggers the transmitted data between different channels. Same data transmission via four different channels ensures the confidentiality and integrity of data because if an attacker captures one channel of data, he will not be able to understand the full message. The convalescent communication infrastructure is developed among patient wearable devices followed by cluster heads, micro base station, and macro base station to ensure data privacy and preservation with better communication metrics. In addition, the objective of the HOPCTP prototype is to resolve the data privacy issues in delay-sensitive applications, i.e., IHC-IoT networks. To achieve this, the HOPCTP prototype promotes data preservation and communication in terms of authenticity, congestion, throughput, communication cost, and packet loss ratio. The result statistics of the proposed scheme demonstrate remarkable improvement over the existing schemes for aforementioned comparative metrics.