In an era characterized by the relentless evolution of Internet of Things (IoT) technologies, marked by the pervasive adoption of smart devices and the ever-expanding realm of Internet connectivity, the IoT has seamlessly integrated itself into our daily lives. This integration has ushered in a new era for manufacturing companies, enabling them to conduct real-time monitoring of their machinery, supervise product quality, and closely monitor environmental variables within their facilities. In addition to the immediate benefits of risk mitigation and loss prevention, this multifaceted approach has provided decision-makers with a comprehensive perspective for making informed decisions. People are now more dependent than ever in IoT devices and services. However, anomalies within IoT networks pose a critical concern despite the IoT's immense potential. These anomalies can pose significant security and safety risks if they go undetected. Identifying and alerting users of these anomalies on time has become crucial for preventing potential damages and losses. In response to this imperative, our research endeavors to utilize the power of Machine Learning and Deep Learning techniques to detect anomalies in IoT networks. We undertake exhaustive experiments with the IoT-23 dataset to validate our methodology empirically. Our research examines an exhaustive comparison of numerous models, assessing their performance and time efficiency to determine the optimal algorithm for achieving high detection accuracy under strict time constraints. This research represents an important step towards enhancing the security of Industrial IoT environments, thereby protecting vital infrastructure and ensuring the integrity of industrial operations in our increasingly interconnected world.

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Journal: International Journal of Intelligent Systems and Applications in Engineering (IJISAE), ISSN 1247-6799, Volume 12, Issue 2, Jan 2024

The Internet of Things (IoT) technology has recently emerged as a potential global communication medium that efficiently facilitates human-to-human, human-to-machine, and machine-tomachine communications. Most importantly, unlike the traditional Internet, it supports machine-to-machine communication without human intervention. However, billions of devices connected to the IoT environment are mostly wireless, small, hand-held, and resourced-constrained devices with limited storage capacities. Such devices are highly prone to external attacks. These days, cybercriminals often attempt to launch attacks on these devices, which imposes the major challenge of efficiently implementing communications across the IoT environment. In this paper, the issue of cyber-attacks in the IoT environment is addressed. An end-to-end encryption scheme was proposed to protect IoT devices from cyber-attacks.

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Journal: Tikrit Journal of Engineering Sciences, ISSN 2312-7589, Volume 30, NO. 4, Dec 2023

In normal pharmacological effect screening protocols, natural substances that were thoroughly diluted and without their active components separated are employed. Over the last two decades, strong active isomeric compounds have been identified and isolated. The notion of multitarget treatment was novel in the mid-2000s, but it will be one of the most significant advancements in drug development by 2021. Instead, then relying on organically generated mixtures, researchers are looking at target-based drug development based on precisely specified fragments for effective organic anticancer medicines. This study emphasizes the breakdown of structures utilizing computer aids or fragments, as well as a process for applying natural anticancer medications. The use of computer-assisted drug development (CADD) is becoming more frequent. The major areas of this study were the development of computer-aided pharmaceuticals and anticancer agents. The discovery of effective all-natural cancer treatments will be accelerated. Multitarget drug development methodologies have enabled the development of cancer medicines with fewer negative side effects. Cutting-edge analytical and bioinformatics approaches, particularly machine learning, will be employed to uncover natural anticancer therapies

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Journal: “,Journal of Ayub Medical College, Abbottabad, Pakistan, ISSN 1025-9589 , Volume 36 , Issue 1, Feb 2024

Mobile learning applications utilise the advantages of mobile technologies to increase learning opportunities, mainly on an anytime, anywhere basis. The advancement of mobile technology has facilitated the development of numerous applications to improve students’ learning experience and performance. Successful implementation of m-learning is highly dependent on learning context and environment awareness. This work presents a multiphase exploration of early phases responsible for defining and validating the m-learning context, and later phases based on context validation results achieved from the previous phases, involving the development and evaluation of a new m-learning context prototype. This new prototype proved to provide context-aware and ubiquitous learning services fulfilling several diverse user interaction levels and requirements.

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Journal: International Journal of Computer applications in Technology. 58 (4), 296- 307.

This paper delves into the capacity of enhanced Big Bang-Big Crunch (EBB-BC) metaheuristic to handle data clustering problems. BB-BC is a product of an evolution theory of the universe in physics and astronomy. Two main phases of BB-BC are big bang and big crunch. The big bang phase involves a creation of a population of random initial solutions, while in the big crunch phase these solutions are shrunk into one elite solution exhibited by a mass center. This study looks into enhancing the BB-BC’s effectiveness in clustering data. Where, the inclusion of an elite pool alongside implicit solution recombination and local search method, contribute to such enhancement. Such strategies resulted in a balanced search of good quality population that is also diverse. The proposed elite pool-based BB-BC was compared with the original BB-BC and other identical metaheuristics. Fourteen different clustering datasets were used to test BB-BC and the elite pool-based BB-BC showed better performance compared to the original BB-BC. BB-BC was impacted more by the incorporated strategies. The experiments outcomes demonstrate the high quality solutions generated by elite pool-based BB-BC. Its performance in fact supersedes that of identical metaheuristics such as swarm intelligence and evolutionary algorithms.

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Journal: Journal of Computer Science. 14 (12), 1611-1626.

Journal: 20th International Congress on Modelling and Simulation, Adelaide, Australia

Journal: 19th International Congress on Modelling and Simulation, Perth, Australia

Journal: International Journal of Data Mining, Modelling and Management

Journal: International Journal of Advanced Computer Science and Applications,

Journal: International Journal of Advanced Computer Science and Applications, Vol. 11, No. 5, 2020