Improving energy management is essential for sustainability since building energy use is rising.
This study is a trailblazer in using multi-criteria decision-making (MCDM) methodologies for the
real-time operational optimization of building energy systems. Data collection and pre-processing,
feature extraction, feature selection, classification, trust authentication, encryption, and decryption
are among the techniques used in this approach. Pre-processing procedures for the raw data include
feature encoding, dimension reduction, and normalization approaches. The Hybrid Grey Level Cooccurrence
Matrix Fast Fourier Transform (HGLCM-FFT) method is used for feature extraction.
Filter-based methods are used for feature selection, including IG, CS, symmetric uncertainty, and
gain ratio. The Hierarchical Gradient Boosted Isolation Forest (HGB-IF) technique is used for the
classification. Distributed Adaptive Trust-Based Authentication (DAT-BA), a security architecture
in distributed cloud environments, uses trust authentication. The Particle Swarm Optimized
Symmetrical Blowfish (PSOSB) method is used for encryption and decryption. The suggested work
is implemented using OS Python – 3.9.6; the performance of the proposed model is Attack
Detection Rate, False alarm rate, True positive rate, Network usage, CPU usage, Encryption time,
encryption time and Throughput.

Cancel culture has emerged as a significant societal phenomenon, particularly in the context of consumer-brand relationships, where consumers leverage social media platforms to publicly sever ties with brands. This study explores cancel culture through a multi-method approach, integrating insights from survey data, qualitative research, and social media analysis. Leveraging advanced natural language processing (NLP) models, including transformer-based architectures, we analyze sentiment, emotional dynamics, and linguistic patterns underlying cancel behaviors. Our findings highlight the emotional distinction between canceling and boycotting, with sadness predominantly associated with canceling and anger driving boycotting behaviors. By employing state-of-the-art AI-driven tools to quantify and examine these behaviors, this research provides actionable insights into the mechanisms of consumer-brand disengagement. The study underscores the role of NLP in advancing business intelligence and offers new perspectives on digital-era consumer behavior and its impact on brand reputation and strategy.

Evaluating large artery atherosclerosis is critical for predicting and preventing ischemic strokes. Ultrasonographic assessment of the carotid arteries is the preferred first-line examination due to its ease of use, noninvasive, and absence of radiation exposure. This study proposed an automated classification model for the common carotid artery (CCA), carotid bulb, internal carotid artery (ICA), and external carotid artery (ECA) to enhance the quantification of carotid artery examinations. A total of 2,943 B-mode ultrasound images (CCA: 1,563; bulb: 611; ICA: 476; ECA: 293) from 288 patients were collected. Three distinct sets of embedding features were extracted from artificial intelligence networks including pre-trained DenseNet201, vision Transformer (ViT), and echo contrastive language-image pre-training (EchoCLIP) models using deep learning architectures for pattern recognition. These features were then combined in a support vector machine (SVM) classifier to interpret the anatomical structures in B-mode images. After ten-fold cross-validation, the models achieved accuracies between 76.1%-81.8%, which was significantly better than using individual feature sets, with a p-value of <0.001. The proposed model could make carotid artery examinations more accurate and consistent with the achieved classification accuracy.

Our study investigates sustainability and environmental disclosure in Saudi Arabian accounting, focusing on stakeholder accountability. Employing content analysis and close reading, we examine disclosures in the annual reports of 18 companies from 2008 to 2018. Findings reveal limited disclosures, influenced by interpretations of Islam and government emphasis. Despite sector-specific attention to environmental issues, disclosures lack depth. The study offers insights into managerial motivations, highlighting divergent stakeholder priorities and moral obligations. Policy implications suggest mandatory regulations could enhance accountability and encourage comprehensive disclosures. The research underscores the significance of contextual understanding in shaping accounting practices, particularly in regions like Saudi Arabia. Regulatory changes could improve corporate reporting on environmental and sustainability matters, addressing societal needs more effectively.