Randa Allafi

Digital pathology involves the digitisation of histology slides, which is vital in modern healthcare, especially for cancer detection and diagnosis. Still, the manual analysis of these digital slides by expert pathologists is time-consuming and prone to inconsistencies. This workload often results in subjective grading and a lack of agreement between different pathologists (inter-operator variability) and even with the same pathologist over time (intra-operator variability). With the advent of whole slide imaging (WSI) and high computational ability, the application of deep learning (DL), particularly convolutional neural networks, has developed rapidly in the past decades in the domain of digital pathology. Therefore, this study introduces a Hybrid Temporal Deep Learning Network for Automated Malignant Cell Classification in Cytology Slides (HTDL-MCCCS) model. The objective is to improve diagnostic consistency, reduce workload, and enable scalable screening in resource-limited clinical settings. The HTDL-MCCCS model consists of four integrated stages: First, slide pre-processing is performed using stain normalisation, artefact removal, patch extraction, and nuclei segmentation to enhance morphological clarity. Second, the pyramid vision transformer (PVT) is utilised to learn robust cell-level features from unlabeled slide patches. Third, the bidirectional temporal recurrent unit (BiTRU) is employed for the automated classification of malignant and benign cells. Lastly, a detailed analysis using Grad-CAM is included to support clinical interpretability and trust. A wide-ranging simulation analysis of the HTDL-MCCCS methodology portrayed superior outcomes of 98.16% and 96.42% under the SIPaKMeD and Herlev datasets.

Intelligent transportation systems (ITS) have experienced an important development in the past decade because of developments in communication, control, and information technology deployed to roads, vehicles, and traffic controller systems. Vehicle form classification plays an essential role in applying ITS owing to its capability for collecting valuable traffic information, providing further development of transport structures, and improving human convenience. Nevertheless, the present service structure implements artificial intelligence (AI) methods with universal patterns for every vehicle. Still, the computational efficiency and needs of deep learning (DL) methods pose difficulties for real-time applications. DL is a useful device for classifying vehicle categories because it can take composite traffic data features and learns from larger data amounts. This manuscript develops a Secure Elliptic Galois Cryptography Framework for Vehicle Image Classification in Intelligent Transportation Systems (SEGCF-VICITS) method. The primary aim of the SEGCF-VICITS method is to ensure secure data transmission and intelligent decision-making in ITS environments. Initially, the SEGCF-VICITS model employs the elliptic galois cryptography (EGC) model to provide strong encryption for sensitive vehicular data, utilizing a key that is subsequently used for data decryption. Besides, the SE-DenseNet model is utilized for feature extraction. Additionally, the convolutional sparse autoencoder (CSAE) method is used for vehicle classification. The experimental validation of the SEGCF-VICITS method portrayed a superior accuracy value of 95.48% over existing models under the vehicle image classification dataset.

In Unmanned Aerial Vehicle (UAV) networks, multi-class aerial image classification (AIC) is crucial in various applications, from environmental monitoring to infrastructure inspection. Deep Learning (DL), a powerful tool in artificial intelligence (AI), proves significant in this context, enabling the model to analyze and classify complex aerial images effectually. By utilizing advanced neural network architectures, such as convolutional neural networks (CNN), DL models outperform at identifying complex features and patterns within the aerial imagery. These models can extract spectral and spatial information from the captured data, classifying diverse terrains, structures, and objects precisely. Furthermore, the integration of Snake Optimization algorithms assists in fine-tuning the classification process, improving accuracy. As UAV networks continue to expand, DL-powered multi-class AIC significantly enhances the performance of surveillance, reconnaissance, and remote sensing tasks, contributing to the advancement of autonomous aerial systems. This study proposes a Snake Optimization Algorithm with Deep Learning for Multi-Class Aerial Image Classification (SOADL-MCAIC) methodology on UAV Networks. The main purpose of SOADL-MCAIC methodology is to recognize the presence of multiple classes of aerial images on the UAV networks. To accomplish this, the SOADL-MCAIC technique utilizes Gaussian filtering (GF) for pre-processing. In addition, the SOADL-MCAIC technique employs the Efficient DenseNet model to learn difficult and intrinsic features in the image. The SOA-based hyperparameter tuning process is used to enhance the performance of the Efficient DenseNet technique. Finally, the kernel extreme learning machine (KELM)-based classification algorithm is implemented to identify and classify the presence of various classes in aerial images. The simulation outcomes of the SOADL-MCAIC method are examined under the UCM land use dataset. The experimental analysis of the SOADL-MCAIC method portrayed a superior accuracy value of 99.75% over existing models.

Human activity recognition (HAR) employs a broad range of sensors that generate massive volumes of data. Traditional server-based and cloud computing methods require all sensor data to be sent to servers or clouds for processing, which leads to high latency and bandwidth costs. The long-term data transfer between servers and sensors maximizes the cost of latency and bandwidth. Real-time processing is, nevertheless, highly required for human action identification. By bringing processing and quick data storage to the sensors instead of depending on a central database, edge computing is rapidly emerging as a solution to this issue. Artificial intelligence is responsible for most HAR, which demands a lot of processing power and calculation. Artificial intelligence (AI) needs more computation which is not allowed by edge computing. So Edge intelligence, which allows AI to operate at the network edge for actual-time applications, has been made possible by the advent of binarized neural networks. To provide less latency and less memory for human activity identification at the edge network, we construct a hybrid deep learning-based binarized neural network (HDL-Binary Dilated DenseNet) in this research. Fractal HAR optimization algorithms could be applied to these algorithms. For example, fractal-HAR optimization techniques might be used to provide less latency and less memory human activity identification at the edge network. Using three sensors-based human activity detection datasets such as Radar HAR dataset, UCI HAR dataset and UniMib-SHAR dataset, we implemented the Hybrid Binary Dilated Dense Net. It is then assessed using four criteria. Comparatively, the Hybrid Binary Dilated DenseNet performs better with 99.6% radar HAR dataset which is highest than other models like CNN-BiLSTM and GoogLeNet.

The internet is the most effective means of communication in the modern world. Therefore, cyber-attacks are becoming more frequent, and their consequences are becoming increasingly severe. Distributed denial of service (DDoS) is one of the five most effective and costly cyberattacks. DDoS attacks are the most prevalent and expensive in today's evolving cybersecurity landscape. However, their ability to disrupt network services causes significant financial losses and has become an effective means of DDoS detection and prevention, both of which are essential for organisations. Network monitoring and control systems have found it challenging to recognise the numerous classes of denial of service (DoS) and DDoS attacks, as they all work exclusively. Therefore, an effective model is needed for attack detection. A previous study has established that shallow and deep learning (DL) methods are vital for identifying DDoS threats; however, there is a lack of research on time-based features and classification across numerous DDoS threat categories. This manuscript introduces an ensemble learning model integrated with two-tier heuristic optimisation techniques for effective cyber defence (ELMT2HO-ECD) methodology. The primary purpose of the ELMT2HO-ECD methodology is to provide a robust solution for detecting and mitigating DDoS attacks in real time. Initially, the ELMT2HO-ECD approach applies mean normalisation to the data to measure the feature within a specified range. Furthermore, the mountain gazelle optimiser (MGO) approach is utilised for feature extraction. For DDoS attack detection, ensemble DL models, namely convolutional long short‐term memory (ConvLSTM), Wasserstein autoencoder (WAE), and temporal convolutional networks (TCN), are employed. To further enhance the performance of the three ensemble models, hyperparameter tuning is performed using the improved pufferfish optimisation algorithm (IPOA), which optimises the models' parameters to achieve higher accuracy. The ELMT2HO-ECD model is evaluated on the CICIDS2017, CICIDS2018, and CICIDS2019 datasets. Validation of the performance of the ELMT2HO-ECD model demonstrated superior accuracy of 98.93%, 98.43%, and 99.23% compared with existing techniques.

Besides the developing threat of cyberattacks, cybersecurity is one of the most significant Internet of Things (IoT) regions. While the IoT has formed a novel model where a network of devices and machines is efficient in collaborating and communicating with each other, it is a novel process invention in enterprises. The role of cybersecurity is to mitigate risks for institutions and users by safeguarding data confidentiality across networks. The growing tools and technologies for cybersecurity improve safety in IoT systems. AI is helpful in improving cybersecurity by providing real-time information for faster threat detection, rapid responses, and smarter decisions. Moreover, integrating blockchain (BC) with IoT illustrates promise but encounters threats like performance issues, security vulnerabilities, and scalability limits. Still, BC plays a key part in protecting low-energy IoT devices. In this study, I proposed a novel approach using an Attention Mechanism-Based Recurrent Neural Network and Dimensionality Reduction for Cyber Threat Detection (AMRNN-DRCTD) model. The main goal of the proposed AMRNN-DRCTD model was to enhance the detection system for cyberattacks in IoT networks. I considered possible security breaches in BC and their influence on network processes. At the initial stage, the data normalization applied zero-mean normalization to alter data into a consistent setup. The feature selection process employed the chaotic and terminal strategy-based butterfly optimization algorithm (CTBOA). Furthermore, the proposed AMRNN-DRCTD model utilized the hybrid convolutional neural network and bi-directional long short-term memory with an attention mechanism (CNN-BiLSTM-AM) technique for the classification process. Finally, the Honey Badger Algorithm (HBA)-based hyperparameter selection range was accomplished to optimize the detection outcomes of the CNN-BiLSTM-AM technique. The experimental evaluation of the AMRNN-DRCTD methodology was examined under the BoT-IoT dataset. The performance validation of the AMRNN-DRCTD methodology highlighted a superior accuracy output of 99.28% over existing approaches.

Posting sarcastic comments on social media has become popular in the modern era. Sarcasm is a linguistic expression that typically conveys the contrary meaning of what has already been said, making it challenging for machines to find the literal meaning. It depends mainly on context, making it a tedious process for computational analysis. It is well known for its modulation with spoken words and an irony undertone. In addition, sarcasm conveys negative sentiment using positive words, which easily confuses sentiment analysis (SA) models. Sarcasm detection is a natural language processing (NLP) process and is prevalent in SA, human–machine dialogue, and other NLP applications due to sarcasm’s ambiguities and complex nature. Concurrently, the advancement of machine learning (ML) techniques makes it easier to develop robust sarcasm detection methods. This paper presents an automated sarcasm recognition using applied linguistics-driven deep learning with a large language model (ASR-ALDL3M) technique. The purpose of the ASR-ALDL3M technique is to focus on recognizing the sarcastic data using the DL model. In the ASR-ALDL3M technique, the initial data preprocessing phase is utilized, and glove word embedding is applied. Next, the sarcasm recognition procedure is applied using the long short-term memory (LSTM) model. Moreover, the hyperparameter selection of the LSTM model is performed using the fractals monarch butterfly optimization (MBO) technique. At last, a large language model (LLM) is utilized to enhance the sarcastic recognition process. A comprehensive result analysis is made to validate the outcomes of the ASR-ALDL3M technique. The performance evaluation outcomes stated that the ASR-ALDL3M method gains better performance over other models.

Peak-to-average power ratio (PAPR) in orthogonal time frequency space (OTFS) is vital for optimizing signal efficiency, minimizing distortion, and enhancing the performance of a beyond fifth-generation (B5G) system. The paper focuses on lowering the PAPR while retaining the bit error rate (BER) and power spectral density (PSD) for 64 and 256 sub-carriers. The PAPR is minimized by using a fractal optimization process known as selective mapping (SLM) and partial transmission sequence (PTS) at the transmitting block of the OTFS framework. The combination of SLM and PTS reduces peak power by selectively modifying the phase of transmitted symbols. SLM generates multiple versions of the signal, while PTS allocates power to these versions optimally, collectively mitigating amplitude peaks and minimizing PAPR. According to the simulation results, the suggested SLM+PTS works better than the traditional SLM and PTS, achieving low spectrum leakage and PAPR improvements of 5.5 dB and 4.9[ ]dB as well as SNR gains of 2.4[ ]dB and 2.1[ ]dB. In the future, SLM+PTS may work on lowering PAPR by making computers faster, researching adaptive algorithms, and combining machine learning methods for real-time optimization in various communication settings. Complex modeling, including fractal-based SLM and PTS approaches, provides a detailed understanding of waveform behavior and interference patterns, enabling more accurate and effective hybrid PAPR reduction techniques. This combination enhances the ability to manage to mitigate non-linear distortion across various sub-carriers, crucial for the performance and efficiency of 6G OTFS modulation.

Sentiment analysis (SA) is the popular natural language processing (NLP) problem utilized for analyzing texts, including uploaded reviews and user posts on e-commerce portals, forums, and social media platforms, regarding their opinions about the event or person, product, and service. The SA task comprises analyzing text to determine whether the sentiment expressed is negative, positive, or neutral, aiming for precise subjective data analysis. The deep learning (DL) technique enables various architectures to model SA tasks and has surpassed other machine learning (ML) techniques as the first method to perform SA tasks. Recent advancements in the DL model depart from the growing dominance of transformer language algorithms and convolutional neural network (CNN) and recurrent neural network (RNN) models. Utilizing pre-trained transformer language models to transfer knowledge to downstream tasks has emerged as a cutting-edge model in NLP. Therefore, the study designs a fractal walrus optimizer with self-attention DL-based SA in applied linguistics (WOSADL-SAAL) method. The WOSADL-SAAL method aims to recognize and classify the presence of sentiments in social media content. In the WOSADL-SAAL technique, data preprocessing is initially performed to transform the input dataset into a meaningful format. In addition, the WOSADL-SAAL technique uses the bag of words (BoW) model for extracting features. The self-attention bidirectional long short-term memory (SA-BiLSTM) network is applied to classify sentiment. The walrus optimizer (WO) model performs the hyperparameter tuning model to boost the detection outcomes of the SA-BiLSTM network. The performance evaluation of the WOSADL-SAAL method takes place under the benchmark SA dataset. The experimental analysis of the WOSADL-SAAL method exhibited a superior accuracy value of 99.07% and 99.24% under TUSA and IMDB datasets over existing approaches.

Cybersecurity in the Internet of Things (IoT) is the practice of implementing measures to secure networks and connected devices from data breaches, cyber threats, and unauthorized access. It is essential owing to the increasing interconnectivity of devices, ranging from smart home appliances to industrial sensors. The potential attack surface expands, necessitating strong cybersecurity measures to protect sensitive data, ensure privacy, and prevent disruptions to critical services with these increasing number of IoT devices. Artificial intelligence (AI) technologies, particularly deep learning (DL) and machine learning (ML) approaches, hold the potential to mitigate and identify cyberattacks on IoT networks. DL demonstrates promise for effectively preventing and detecting security threats within IoT devices. Despite the importance of Intrusion Detection Systems (IDS) in maintaining confidentiality by detecting suspicious activities, classical IDS solutions might face difficulties in the IoT platform. Therefore, this study presents an Artificial Orca Algorithm with Ensemble Learning cyberattack detection and classification (AOAEL-CDC) methodology in an environment of IoT. The presented AOAEL-CDC technique exploited the feature selection (FS) approach with an ensemble learning approach for cyberattack recognition and identification in the IoT atmosphere. In the developed AOAEL-CDC model, the feature selection takes place using the AOA technique. For the cyberattack detection process, the ensemble learning process is carried out by the use of three models such as bidirectional long short-term memory (BiLSTM), gated recurrent unit (GRU), and extreme learning machine (ELM). Finally, the hyperparameter range of the DL techniques takes place using the marine predator’s algorithm (MPA). To examine the performance analysis of the AOAEL-CDC methodology, a series of simulations take place using a benchmark dataset. An extensive comparative study reported that the BCODL-SDSC technique reaches an effective performance with other models with a maximum accuracy of 99.31%.