This paper presents a systematic literature review of energy management models for smart homes, conducted between 2018 and 2024, using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol. Smart homes leverage advanced technologies to optimize energy consumption and enhance sustainability through interconnected devices and sophisticated algorithms. The review covers energy optimization techniques, predictive management, renewable energy integration, demand-side management, user behavior, and data protection. It examines the effectiveness of various models, identifies key tends, and addresses challenges such as integrating diverse energy sources, managing consumption variability, and ensuring data privacy. The findings reveal significant advancements in energy optimization, home automation, and grid stability. However, areas like demand-side management and artificial intelligence (AI) and machine learning (ML) driven algorithms for energy management remain underexplored and require further research. Recommendations are provided to improve energy management systems and guide future research for increased efficiency and sustainability in smart homes. This review offers valuable insights into the current state of energy management models and lays the groundwork for future developments in smart home energy systems.

Virtual assistants are involved in the daily activities of humans such as managing calendars, making appointments, and providing wake-up calls. They provide a conversational service to customers around-the-clock and make their daily life manageable. With this emerging trend, many well-known companies launched their own virtual assistants that manage the daily routine activities of customers. In the healthcare sector, virtual medical assistants also provide a list of relevant diseases linked to a specific symptom. Due to low accuracy and uncertainty, these generated recommendations are untrusted and may lead to hypochondriasis. In this study, we proposed a Medical Instructed Real-time Assistant (MIRA) that listens to the user’s chief complaint and predicts a specific disease. Instead of informing about the medical condition, the user is referred to a nearby appropriate medical specialist. We designed an architecture for MIRA that considers the limitations of existing virtual medical assistants such as weak authentication, lack of understanding multiple intent statements about a specific medical condition, and uncertain diagnosis recommendations. To implement the designed architecture, we collected the chief complaints along with the dialogue corpora of real patients. Then, we manually validated these data under the supervision of medical specialists. We then used these data for natural language understanding, disease identification, and appropriate response generation. For the prototype version of MIRA, we considered the cases of glaucoma (eye disease) and diabetes (an autoimmune disease) only. The performance measure of MIRA was evaluated in terms of accuracy (89%), precision (90%), sensitivity (89.8%), specificity (94.9%), and F-measure (89.8%). The task completion was calculated using Cohen’s Kappa $(k=0.848)$ that categorizes MIRA as ‘Almost Perfect’. Furthermore, the voice-based authentication identifies the user effectively and prevent against masquerading attack. Simultaneously, the user experience shows relatively good results in all aspects based on the User Experience Questionnaire (UEQ) benchmark data. The experimental results show that MIRA efficiently predicts a disease based on chief complaints and supports the user in decision making.

Multimodal emotion recognition has gained much traction in the field of affective computing, human–computer interaction (HCI), artificial intelligence (AI), and user experience (UX). There is growing demand to automate analysis of user emotion towards HCI, AI, and UX evaluation applications for providing affective services. Emotions are increasingly being used, obtained through the videos, audio, text or physiological signals. This has led to process emotions from multiple modalities, usually combined through ensemble-based systems with static weights. Due to numerous limitations like missing modality data, inter-class variations, and intra-class similarities, an effective weighting scheme is thus required to improve the aforementioned discrimination between modalities. This article takes into account the importance of difference between multiple modalities and assigns dynamic weights to them by adapting a more efficient combination process with the application of generalized mixture (GM) functions. Therefore, we present a hybrid multimodal emotion recognition (H-MMER) framework using multi-view learning approach for unimodal emotion recognition and introducing multimodal feature fusion level, and decision level fusion using GM functions. In an experimental study, we evaluated the ability of our proposed framework to model a set of four different emotional states (Happiness, Neutral, Sadness, and Anger) and found that most of them can be modeled well with significantly high accuracy using GM functions. The experiment shows that the proposed framework can model emotional states with an average accuracy of 98.19% and indicates significant gain in terms of performance in contrast to traditional approaches. The overall evaluation results indicate that we can identify emotional states with high accuracy and increase the robustness of an emotion classification system required for UX measurement.

Click jacking attacks are the emerging threats to websites, especially to online social networks (OSNs). In this paper, we describe some new attacks to online websites. The new Click jacking attacks cause serious damage to users by stealing their personal credentials or by sharing their personal information on social networks bringing moral degradation to them. The attacker applications are hidden behind the sensitive user interface to steal the clicks of the user and use them for the illegal purposes. To detect and prevent Click jacking attacks, we propose a browser-based solution referred to as Cursor Spoofing and Click jacking Prevention (CSCP). CSCP ensures protection Cursor spoofing attack with high effectiveness and also the Like jacking attacks, other variation of Click jacking attacks which associate malicious code to Facebook Like buttons. We have conducted our studies on 442 participants to evaluate the effectiveness of our attacks and also defenses. Results show that our attack success rate falls between 76% and 78%.