For classifying brain tumors with small datasets, the knowledge-based transfer learning (KBTL) approach has performed very well in attaining an optimized classification model. However, its successful implementation is typically affected by different hyperparameters, specifically rate (LR), batch size (BS), and their joint influence. In general, most of existing research could not achieve desired performance because work addressed only one hyperparameter tuning. This study adopted a Cartesian...

The Industrial Internet of Things (IIoT) comprises a variety systems, smart devices, and an extensive range communication protocols.Hence, these systems face susceptibility to privacy security challenges, making them prime targets for malicious attacks that can result in harm the overall system.Privacy breach issues are notable concern within realm IIoT.Various intrusion detection based on machine learning (ML) deep (DL) have been introduced detect activities networks identify attacks.The...

Globally, coal remains one of the natural resources that provide power to world. Thousands people are involved in collection, processing, and transportation. Particulate dust is produced during these processes, which can crush lung structure workers cause pneumoconiosis. There no automated system for detecting monitoring diseases miners, except specialist radiologists. This paper proposes ensemble learning techniques pneumoconiosis disease chest X-ray radiographs (CXRs) using multiple deep...

Unmanned Aerial Vehicles (UAVs), or drones, provided with camera sensors enable improved situational awareness of several emergency responses and disaster management applications, as they can function from remote complex accessing regions. The UAVs be utilized for application areas which hold sensitive data, necessitates secure processing using image encryption approaches. At the same time, embedded in latest technologies deep learning (DL) models monitoring such floods, collapsed buildings,...

Intrusion detection systems (IDS) are essential in the field of cybersecurity because they protect networks from a wide range online threats.The goal this research is to meet urgent need for small-footprint, highlyadaptable Network Detection Systems (NIDS) that can identify anomalies.The NSL-KDD dataset used study; it sizable collection comprising 43 variables with label's "attack" and "level."It proposes novel approach intrusion based on combination channel attention convolutional neural...

As cyberattacks develop in volume and complexity, machine learning (ML) was extremely implemented for managing several cybersecurity attacks malicious performance. The cyber-physical systems (CPSs) combined the calculation with physical procedures. An embedded computer network monitor control procedure, commonly feedback loops whereas procedures affect calculations conversely, at same time, ML approaches were vulnerable to data pollution attacks. Improving security attaining robustness of...

With the increased advancements of smart industries, cybersecurity has become a vital growth factor in success industrial transformation. The Industrial Internet Things (IIoT) or Industry 4.0 revolutionized concepts manufacturing and production altogether. In industry 4.0, powerful Intrusion Detection Systems (IDS) play significant role ensuring network security. Though various intrusion detection techniques have been developed so far, it is challenging to protect intricate data networks....

This study describes improving network security by implementing and assessing an intrusion detection system (IDS) based on deep neural networks (DNNs). The paper investigates contemporary technical ways for enhancing performance, given the vital relevance of safeguarding computer against harmful activity. DNN-based IDS is trained validated model using NSL-KDD dataset, a popular benchmark research. performs well in both training validation stages, with 91.30% accuracy 94.38% accuracy. Thus,...

Intrusion detection systems, also known as IDSs, are widely regarded one of the most essential components an organization’s network security. This is because IDSs serve first line defense against several cyberattacks and accountable for accurately detecting any possible intrusions. Several implementations accomplish potential threats throughout flow-based traffic analysis. Traditional frequently struggle to provide accurate real-time intrusion while keeping up with changing landscape threat....

The Internet of Things (IoT) has evolved at a revolutionary pace in the last two decades computer science. It is becoming increasingly fashionable for IoT to be rebranded as “Social Things” (SIoT), and this drawing attention scientific community. Smart items ecosystem can locate relevant services based on social ties between neighbors. As result, SIoT displays interplay various problem context ecosystem. Navigating network difficult because number friends complexity ties. By identifying...

Artificial intelligence (AI) techniques play a vital role in the evolving growth and rapid development of smart cities. To develop environment, enhancements to execution, sustainability, security traditional mechanisms become mandatory. Intrusion detection systems (IDS) can be considered an effective solutions achieve environment. This article introduces intrusion using chaotic poor rich optimization with deep learning model (IDCPRO-DLM) for ubiquitous atmospheres. The IDCPRO-DLM follows...

The Internet of Things (IoT) based Wireless Sensor Networks (WSNs) contain interconnected autonomous sensor nodes (SN), which wirelessly communicate with each other and the wider internet structure. Intrusion detection to secure IoT-based WSNs is critical for identifying responding great security attacks threats that can cooperate integrity, availability, privacy network its data. Machine learning (ML) algorithms are deployed detecting difficult patterns subtle anomalies in IoT Artificial...

Extended reality (XR) technologies are an umbrella term for simulated-based learning tools that cover 3-dimensional technologies, including virtual (VR), augmented (AR), and mixed (MR). At King Saud University, first-year pharmacy students required to experience hospital observational training during the Introductory Pharmacy Practice Experience (IPPE). We aimed measure effectiveness satisfaction of VR among IPPE A Quasi-Experimental study was conducted. The experimental arm included PharmD...

With the rising growth of telecommunication industry, customer churn problem has grown in significance as well. One most critical challenges data and voice service industry is retaining customers, thus reducing by increasing satisfaction. Telecom companies have depended on historical to measure churn. However, does not reveal current satisfaction or future likeliness switch between telecom companies. The related research reveals that many studies focused developing churner prediction models...

Over the last few years, unmanned aerial vehicles (UAV), also called drones, have attracted considerable interest in academic field and exploration research of wireless sensor networks (WSN). Furthermore, application drones aided operations related to agriculture industry, smart Internet things (IoT), military support. Now, usage drone-based IoT, (IoD), their techniques design challenges are being investigated by researchers globally. Clustering routing aid maximize throughput, reducing...

Drone developments, especially small-sized drones, usher in novel trends and possibilities various domains. Drones offer navigational inter-location services with the involvement of Internet Things (IoT). On other hand, drone networks are highly prone to privacy security risks owing their strategy flaws. In order achieve desired efficiency, it is essential create a secure network. The purpose current study have an overview problems that recently impacted (IoD). An Intrusion Detection System...

Owing to the development and expansion of energy-aware sensing devices autonomous intelligent systems, Internet Things (IoT) has gained remarkable growth found uses in several day-to-day applications. However, IoT are highly prone botnet attacks. To mitigate this threat, a lightweight anomaly-based detection mechanism that can create profiles for malicious normal actions on networks could be developed. Additionally, massive volume data generated by gadgets analyzed machine learning (ML)...

As users increasingly rely on online social networks for their communication activities, personal location data processing through such poses significant risks to users’ privacy. Location tracks can be mined with other shared information extract rich profiles. To protect privacy, face the challenge of ensuring transparent how are processed, and explicitly obtaining informed consent use this data. In paper, we explore complex nature disclosure problem its We evaluate, an experiment involving...