Phishing attacks continue to pose a significant threat to cybersecurity, employing increasingly sophisticated techniques to deceive victims into revealing sensitive information or downloading malware. This paper presents a comprehensive study on the application of Machine Learning (ML) techniques for identifying phishing websites, with a focus on enhancing detection accuracy and efficiency. We propose an approach that integrates the CfsSubsetEval attribute evaluator with the K-Means Clustering algorithm to improve phishing detection capabilities. Our method was evaluated using datasets of varying sizes (2000, 7000, and 10,000 samples) from a publicly available repository. Simulation results demonstrate that our approach achieves an accuracy of 89.2% on the 2000-sample dataset, outperforming the traditional kernel K-Means algorithm, which achieved an accuracy of 51.5%. Further analysis using precision, recall, and F1-score metrics corroborates the effectiveness of our method. We also discuss the scalability and real-world applicability of our approach, addressing limitations and proposing future research directions. This study contributes to the ongoing efforts to develop robust, efficient, and adaptable phishing detection systems in the face of evolving cyber threats.

Unmanned Aerial Vehicles (UAVs) are increasingly employed across various domains, including communication, military, and delivery operations. Their reliance on the Global Positioning System (GPS) renders them vulnerable to GPS spoofing attacks, in which adversaries transmit false signals to manipulate UAVs’ navigation, potentially leading to severe security risks. This paper presents an enhanced integration of Long Short-Term Memory (LSTM) networks with a Genetic Algorithm (GA) for GPS spoofing detection. Although GA–neural network combinations have existed for decades, our method expands the GA’s search space to optimize a wider range of hyperparameters, thereby improving adaptability in dynamic operational scenarios. The framework is evaluated using a real-world GPS spoofing dataset that includes authentic and malicious signals under multiple attack conditions. While we discuss strategies for mitigating CPU resource demands and computational overhead, we acknowledge that direct measurements of energy consumption or inference latency are not included in the present work. Experimental results show that the proposed LSTM–GA approach achieved a notable increase in classification accuracy (from 88.42% to 93.12%) and the F1 score (from 87.63% to 93.39%). These findings highlight the system’s potential to strengthen UAV security against GPS spoofing attacks, provided that hardware constraints and other limitations are carefully managed in real deployments.

The integration of Generative Artificial Intelligence (GenAI) in academic learning has gained substantial traction across disciplines, necessitating a systematic analysis of its impact. This study explored ChatGPT's transformative role in higher education from 2022 onwards, synthesizing empirical findings across twelve distinct academic fields spanning STEM, social sciences, and healthcare. Relevant empirical case studies were identified through a systematic Scopus database search, applying discipline-specific keywords and filtering out surveys, literature reviews, and theoretical papers. Multi-stage screening identified 60 full-text articles, ultimately selecting twelve high-quality studies for rigorous cross-disciplinary analysis. The findings revealed pronounced disciplinary variations in ChatGPT adoption and impact. Quantitative analysis demonstrated that STEM disciplines report significantly higher accuracy concerns (mean = 1.57 on a 0–2 scale) compared to other fields, while healthcare disciplines showed the highest privacy concerns (mean = 2.0). Moderate positive correlation (r = 0.68) exists between academic integrity concerns and usage intensity, with computer science and social science reporting the highest levels for both metrics. Female representation, documented in 50% of studies, appears to influence adoption patterns. Sample sizes varied considerably (n = 12 to n = 430), with computer science (n = 430) and medical education (n = 265) providing robust empirical bases. Cross-disciplinary analysis revealed that ChatGPT enhances academic performance in structured problem-solving contexts, with health sciences reporting the highest positive impact scores (mean = 1.67), whilst potentially undermining critical thinking. Disciplines with text-based assessments face greater academic integrity challenges (r = 0.72 correlation).

The next generation wireless network is expected to be a heterogeneous. The Radio Access Technology (RAT) selection algorithm, including initial RAT selection and vertical handover, is one of the key research areas in Common Radio Resource Management (CRRM). A suitable RAT selection algorithm can maximize the system performance and Quality of Service (QoS) by allocating users to the most suitable RAT in the case of two or more RATs co-located in the same coverage area. This paper evaluates the performance of three RAT selection algorithms, load balancing based using maximum resource consumption, load balancing based using minimum resource consumption and service based algorithms, in terms of traffic distribution, blocking probability, throughput and throughput fairness for a co-located GERAN/UTRAN/WLAN network. Simulation results show that in terms of blocking probability, the service based algorithm is the worst one when the traffic load is high. In terms of data throughput, the load balancing based using maximum resource consumption algorithm performs better than the other two when the traffic load is low. However, the service based algorithm outperforms the other two when the traffic load is high. In terms of throughput fairness, the service based algorithm achieves the best performance.

Parkinson’s disease (PD) is associated with alterations in gait symmetry and plantar loading that can be examined using vertical ground reaction force (VGRF) recordings. This study presents a confidence-stratified, physiology-anchored multiple-instance learning framework with concept-bottleneck-inspired pathways (implementation identifier: DRO-PAS-MIL-CBM; hereafter, PAS-MIL) for retrospective session-level PD-versus-control classification. Each gait session is represented as a bag of temporal windows. Eight predefined bilateral signal descriptors are combined with eight learned latent temporal dimensions, aggregated through attention-based pooling, and processed by concept-guided, prototype, anchor-only, and static-feature expert pathways. The evaluation used five-fold person-grouped cross-validation on 306 sessions from 165 participants in the PhysioNet Gait in Parkinson’s Disease database.Inner person-grouped out-of-fold ExtraTrees probabilities were used to construct the confidence strata and distillation targets. PAS-MIL achieved a pooled session-level area under the receiver operating characteristic curve of 0.771, average precision of 0.890, and a mean fold AUC of $0.826\pm 0.041$. Relevance analysis identified C05 (asymmetry variability) and C08 (bilateral change mismatch) as the highest-weighted predefined physiological anchor descriptors. Protocol-stratified sensitivity analysis showed variation across the three source sub-studies, with AUCs ranging from 0.740 to 0.790. Probability calibration remained suboptimal after temperature scaling (mean per-fold ECE, $0.291\pm 0.042$). The results demonstrate the feasibility of integrating physiology-informed descriptors, temporal representation learning, and session-level aggregation. The study is a retrospective proof of concept and does not establish external robustness or clinical deployment readiness.