The incorporation of Internet of Things (IoT) applications into 5G networks marks a significant step towards realizing the full potential of connected systems. 5G networks, with their ultra-low latency, high data speeds, and huge interconnection, provide a perfect foundation for IoT ecosystems to thrive. This connectivity offers a diverse set of applications, including smart cities, self-driving cars, industrial automation, healthcare monitoring, and agricultural solutions. IoT devices can improve their reliability, real-time communication, and scalability by exploiting 5G’s advanced capabilities such as network slicing, edge computing, and enhanced mobile broadband. Furthermore, the convergence of IoT with 5G fosters interoperability, allowing for smooth communication across diverse devices and networks. This study examines the fundamental technical applications, obstacles, and future perspectives for integrating IoT applications with 5G networks, emphasizing the potential benefits while also addressing essential concerns such as security, energy efficiency, and network management. The results of this review and analysis will act as a valuable resource for researchers, industry experts, and policymakers involved in the progression of 5G technologies and their incorporation with IT solutions.

Background: Deep learning (DL)-based medical image classification is becoming increasingly reliable, enabling physicians to make faster and more accurate decisions in diagnosis and treatment. A plethora of algorithms have been developed to classify and analyze various types of medical images. Among them, Convolutional Neural Networks (CNNs) have proven highly effective, particularly in medical image analysis and disease detection. Methods: To further enhance these capabilities, this research introduces MediVision, a hybrid DL-based model that integrates a vision backbone based on CNNs for feature extraction, capturing detailed patterns and structures essential for precise classification. These features are then processed through Long Short-Term Memory (LSTM), which identifies sequential dependencies to better recognize disease progression. An attention mechanism is then incorporated that selectively focuses on salient features detected by the LSTM, improving the model’s ability to highlight critical abnormalities. Additionally, MediVision utilizes a skip connection, merging attention outputs with LSTM outputs along with Grad-CAM heatmap to visualize the most important regions of the analyzed medical image and further enhance feature representation and classification accuracy. Results: Tested on ten diverse medical image datasets (including, Alzheimer’s disease, breast ultrasound, blood cell, chest X-ray, chest CT scans, diabetic retinopathy, kidney diseases, bone fracture multi-region, retinal OCT, and brain tumor), MediVision consistently achieved classification accuracies above 95%, with a peak of 98%. Conclusions: The proposed MediVision model offers a robust and effective framework for medical image classification, improving interpretability, reliability, and automated disease diagnosis. To support research reproducibility, the codes and datasets used in this study have been publicly made available through an open-access repository.

Early and interpretable detection of foliar diseases in date palms is essential for sustainable agriculture in arid regions, yet conventional deep-learning models treat symptoms as static visual patterns without considering their biological progression along the rachis. This study introduces PalmRachis-BiLSTM-Attn, a bio-inspired and explainable deep-learning framework that models palm-leaf pathology as a spatial–temporal progression process guided by the anatomical ordering of the rachis. The method begins with a CNN backbone that extracts textural cues from leaf images, which are then reorganized into rachis-aligned sequences and processed through a bidirectional LSTM with temporal attention to capture both forward (disease spread) and backward (regression) vascular dynamics. An integrated explainability module combines Grad-CAM spatial maps with temporal-attention timelines, generating biologically meaningful “where–when” visualizations of disease development. Evaluated on an eight-class date-palm leaf dataset, the framework achieves 98.1% classification accuracy and outperforms static CNNs and non-anatomical sequence baselines across all metrics (precision = 0.988, recall = 0.984, F $1=0.984$ ). By aligning model behavior with real infection pathways, PalmRachis-BiLSTM-Attn advances progression-aware, transparent, and biologically grounded disease monitoring, offering a deployment-oriented foundation for IoT-enabled crop-health systems and future smart-agriculture.

This paper presents a hybrid adaptive approach based on machine learning (ML) for classifying incoming traffic, feature selection and thresholding, aimed at enhancing downgrade attack detection in Wi-Fi Protected Access 3 (WPA3) networks. The fast proliferation of WPA3 is regarded critical for securing modern Wi-Fi systems, which have become integral to 5G and Beyond (5G&B) Radio Access Networks (RAN) architecture. However, the wireless communication channel remains inherently susceptible to downgrade attacks, where adversaries intentionally cause networks to revert from WPA3 to WPA2, with the malicious intent of exploiting known security flaws. Traditional Intrusion Detection Systems (IDS), which rely on fixed-threshold statistical methods, often fail to adapt to changing network environments and new, sophisticated attack strategies. To address this limitation, we introduce a novel ML-based Feature Selection and Thresholding for Downgrade Attacks Detection (MFST-DAD) approach, which comprises three stages: traffic data preprocessing, baseline adaptive feature selection, and real-time detection and prevention using ML algorithms. Experimental results on a specially generated dataset demonstrate that the proposed approach detects downgrade attacks in WPA3 networks, achieving 99.8% accuracy with a Naive Bayes classifier in both WPA3 personal and enterprise transition modes. These findings confirm the effectiveness of our proposed approach in securing next-generation Wi-Fi systems.

Effective intervention and detection of developmental disabilities (DDs) should be carried out early and accurately. This paper discusses a novel AI-based approach for detecting DDs precisely, by incorporating both speech and behavioral data in a unique manner. The new architecture comprises several key stages, with innovations attributed to the system: Enhanced Gaussian-Based Noise Filtering (EGNF) is used to enhance speech; Conv-BiLSTM and Transformer Embeddings for performing strong feature extraction from speech and behavior; Harmony-ReliefF Optimization (HRO) for feature selection, and BioNeuroFusionNet (BNFN) as a novel classification network integrating IndRNN and MnasNet, with GNA selected as the class superiors. The Harmony-ReliefF Optimization (HRO) strategy combines the exploration capabilities of the Harmony Search Algorithm (HSA) with the feature ranking ability of ReliefF for optimizing the feature space. The features selected and optimally extracted are then fed into the novel BioNeuroFusionNet (BNFN) intended for final classification. The BNFN is another significant contribution of this work, where Independently Recurrent Neural Networks (IndRNN) are incorporated for fast processing of sequential data set and MnasNet (MN) for extracting high-level abstract features. The experimental result indicates that this integrated architecture works effectively with high accuracy (0.98942 for voice; 0.99632 for behavior) and very high scores in precision, recall, specificity, and other relevant metrics. Indeed, these results indicate that the system proposed in this work represents a considerable improvement in the area of early diagnosis and intervention of developmental disorders.

Renewable energy sources (RESs) are increasingly being recognized as sustainable and accessible alternatives for the energy future. However, their intermittent nature poses significant challenges to system reliability and stability, necessitating the integration of energy storage systems (ESSs) to ensure sustainability and dependability. This study examines various ESS alternatives, evaluating their suitability for different applications using a multi-criteria decision-making (MCDM) approach. The methodology accommodates diverse criteria types, including qualitative and quantitative factors, represented as linguistic terms, interval values, and crisp numerical data. A techno-socio-economic framework for ESS selection is proposed and applied to Jordan’s unique energy landscape. This framework integrates technical performance, economic feasibility, and social considerations to identify suitable ESS solutions aligned with the country’s renewable energy goals. The study ranks twelve energy storage systems (ESSs) based on key performance criteria. Pumped hydro storage (PHS), thermal energy storage (TES), supercapacitors (SCs), and lithium-ion batteries (Li-ion BESS) lead the ranking. These systems showed the best performance in terms of scalability, efficiency, and integration with grid-scale applications in Jordan. Key applications analyzed include renewable energy integration, grid stability, load shifting, peak load regulation, frequency regulation, and seasonal energy storage. Results indicate that Li-ion batteries are most suitable for renewable energy integration, while flywheels excel in grid stability and frequency regulation. PHS was found to be the preferred solution for load shifting, peak load regulation, and seasonal storage, with hydrogen storage emerging as a promising option for long-duration needs. These findings provide critical insights to guide policy and infrastructure planning, offering a robust model for comprehensive ESS assessment in energy transition planning for countries facing similar challenges.