Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder characterized by difficulties in social communication and repetitive behaviors. The exact causes of ASD remain elusive and likely involve a combination of genetic, environmental, and neurobiological factors. Doctors often face challenges in accurately identifying ASD early due to its complex and diverse presentation. Early detection and intervention are crucial for improving outcomes for individuals with ASD. Early diagnosis allows for timely access to appropriate interventions, leading to better social and communication skills development. Artificial intelligence techniques, particularly facial feature extraction using machine learning algorithms, display promise in aiding the early detection of ASD. By analyzing facial expressions and subtle cues, AI models identify patterns associated with ASD features. This study developed various hybrid systems to diagnose facial feature images for an ASD dataset by combining convolutional neural network (CNN) features. The first approach utilized pre-trained VGG16, ResNet101, and MobileNet models. The second approach employed a hybrid technique that combined CNN models (VGG16, ResNet101, and MobileNet) with XGBoost and RF algorithms. The third strategy involved diagnosing ASD using XGBoost and an RF based on features of VGG-16-ResNet101, ResNet101-MobileNet, and VGG16-MobileNet models. Notably, the hybrid RF algorithm that utilized features from the VGG16-MobileNet models demonstrated superior performance, reached an AUC of 99.25%, an accuracy of 98.8%, a precision of 98.9%, a sensitivity of 99%, and a specificity of 99.1%.

In the ever-evolving technological landscape, ensuring high system availability has become a paramount concern. This research paper focuses on cloud computing, a domain witnessing exponential growth and emerging as a critical use case for high-availability systems. To fulfil the criteria, many services in cloud infrastructures should be combined, relying on the user’s demands. Central to this study is load balancing, an integral element in harnessing the full potential of heterogeneous computing systems. In cloud environments, dynamic management of load balancing is crucial. This study explores how virtual machines can effectively remap resources in response to fluctuating loads dynamically, optimizing overall network performance. The core of this research involves an in-depth analysis of several metaheuristic algorithms applied to load balancing in cloud computing. These include Genetic Algorithm, Particle Swarm Optimization, Ant Colony Optimization, Artificial Bee Colony, and Grey Wolf Optimization. Utilizing CloudAnalyst, the study conducts a comparative analysis of these techniques, focusing on key performance metrics such as Total Response Time (TRT) and Data Center Processing Time (DCPT). The findings of this research offer insights into the varying behaviors of these algorithms under different cloud configurations and user retention levels. The ultimate aim is to pave the way for developing innovative load-balancing strategies in cloud computing. By providing a comprehensive evaluation of existing metaheuristic methods, this paper contributes to advancing high-availability systems, underscoring the importance of tailored solutions in the dynamic realm of cloud technology.

In the ever-expanding digital landscape, the abundance of user-generated content on consumer platforms such as Booking and TripAdvisor offers a rich source of information for both travellers and hoteliers. Sentiment analysis, a fundamental research task of natural language processing (NLP) is used for mining sentiments and opinions within this vast reservoir of text reviews. A more specific type of sentiment analysis, i.e., aspect-based sentiment analysis (ABSA), is used when processing customer reviews is required. In ABSA, we aim to capture aspect-level sentiments and intricate relationships between various aspects within reviews. This article proposes a novel approach to ABSA by introducing a novel technique of word sense disambiguation (WSD) and integrating it with the Transformer architecture bidirectional encoder representations from Transformers (BERT) and graph convolutional networks (GCNs). The proposed approach resolves the intriguing ambiguities of the words and represents the review data as a complex graph structure, facilitating the modeling of intricate relationships between different aspects. The combination of bidirectional long short-term memory (BiLSTM) and GCN proves effective in capturing inter-dependencies among various aspects, providing a nuanced understanding of customer sentiments. The experiments are conducted on the RABSA dataset (an enhanced and richer hotel review data collection), and results demonstrate that our approach outperforms previous baselines, showcasing the effectiveness of integrating WSD in ABSA. Furthermore, an ablation study confirms the significant contribution of the WSD module to the overall performance. Moreover, we explore different similarity measures and find that cosine similarity yields the best results when identifying the real sense of a word in a given sentence using WordNet. The findings of our work and future work related to our work create lots of interest for people in the tourism and hospitality industry. This research gives another boost to the concept of the potential of NLP techniques in sentiment analysis. It emphasizes that if we combine the potential of NLP techniques along with state-of-the-art machine learning frameworks, we can shape the future of this field.

As EVs reshape the automotive industry, eco-driving and route optimization become essential in reducing energy inefficiencies, especially in urban settings where frequent acceleration and deceleration degrade battery performance. This study proposes a mathematical model for EV energy consumption and applies Multi-Objective Optimization (MOO) with NSGA-II (Non-dominated Sorting Genetic Algorithm II) to minimize energy loss while extending driving range through route selection, traffic avoidance, and eco-driving strategies. The framework integrates real-time traffic and weather data from the TomTom Application Programming Interface (API) along with historical traffic records from NYC OpenData. Vehicle-specific data from the Volkswagen eGolf further trains the model under real driving conditions, producing the energy consumption dataset E_Total. Using the E_Total dataset, a predictive algorithm improves energy forecasting accuracy by incorporating traffic flow, road topology, and vehicle dynamics. Numerical simulation results confirm the model’s effectiveness in balancing energy use and travel time through Pareto-optimal decision-making. The AI model generated 100 predictive route options between Times Square and the Holland Tunnel. Among these, route 2 (5.17 km) was identified as the most efficient, requiring only 1.03 kWh of energy E_Total. These findings demonstrate the potential of AI in eco-routing to extend EV range and enhance driver-assistance and autonomous vehicle systems.

The detection of hate speech on social media has become a pressing challenge, particularly in multilingual and low-resource language settings such as Roman Urdu, where informal grammar, code-switching, and inconsistent orthography hinder accurate classification. Despite progress in hate speech detection for high-resource languages, limited research exists for Roman Urdu content. This study addresses this gap by proposing a computationally efficient deep learning framework based on a hybrid convolutional neural network and bidirectional long short-term memory (CNN-BiLSTM) architecture. The model leverages FastText pre-trained embeddings to capture subword-level semantics and combines convolutional layers for local feature extraction with BiLSTM for global context modeling. We evaluate our approach on a labeled Roman Urdu dataset and compare it with traditional machine learning models and deep learning baselines. Our proposed CNN-BiLSTM model achieves the highest performance with an accuracy of 80.67% and an F1-score of 81.47%, outperforming competitive baselines. These findings demonstrate the effectiveness and practicality of our lightweight architecture in detecting hate speech in Roman Urdu, offering a scalable solution for multilingual and resource-constrained environments.

Presents corrections to the paper, (Corrections to “A Comparative Analysis of Metaheuristic Techniques for High Availability Systems”).