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Item Embargo NoFake Trustworthy Reviewer Scoring System for Detecting Fake Reviews(Institute of Electrical and Electronics Engineers Inc., 2025-12-09) Dissanayaka S.D; Jayawardhana R.A.D.G.S; Jayalath, T; De Silva, HThe proliferation of fake online reviews has severely undermined consumer trust and market fairness in digital platforms. Traditional detection systems relying on rule-based filters or shallow machine learning models often fail to identify sophisticated, artificially intelligent-generated, or behaviorally deceptive content. This research proposes an intelligent system for fake review detection that integrates advanced Natural Language Processing, behavioral anomaly analysis, and explainable artificial intelligence to enhance accuracy, transparency, and usability. The system employs a hybrid feature set of about 212 dimensions, including Term Frequency-Inverse Document Frequency scores, sentiment-rating inconsistency, readability metrics, and reviewer behavioral indicators such as review frequency and account metadata. A neural network trained on the Deceptive Opinion Spam dataset (40,000 reviews) classifies reviews as genuine or fake, achieving 81.55% accuracy and an Area Under the Receiver Operating Characteristic Curve of 0.9019. Anomaly detection models Isolation Forest generate a dynamic trust score for reviewers, improving detection of coordinated spam campaigns. To ensure transparency, SHapley Additive exPlanations are integrated into a full-stack web application, providing human-readable insights such as Sentiment-Rating Mismatch or Robotic Writing Detected. Results show the proposed system outperforms traditional methods in accuracy and interpretability, offering a scalable and trustworthy solution for digital trust ecosystems.Item Embargo AI Interviews with Facial Emotion Recognition for Real-Time Feedback and Career Recommendations(Institute of Electrical and Electronics Engineers Inc., 2025) Herath R.P.N.M; Arachchi D.S.U.; Gunaratne M.H.B.P.T.; Hansana K.T.; Wijayasekara, S.K; Jayasinghe, DThe hiring process is complex, requiring evaluation of candidates across multiple dimensions, including technical proficiency, behavioral traits, and credibility. Traditional interviews often suffer from biases and inefficiencies. This research presents an AI-driven Interview System integrating Machine Learning (ML), Natural Language Processing (NLP), and Computer Vision to automate and enhance recruitment. The system generates contextual interview questions, evaluates candidate responses using LLM-based scoring models, and provides real-time feedback for engagement. It includes speech-to-text transcription and offensive word detection to ensure professionalism. The behavioral analysis module leverages facial emotion recognition and computer vision to assess non-verbal cues such as confidence and attentiveness. Additionally, Curriculum Vitae (CV) parsing and LinkedIn data extraction use NLP-based entity recognition to extract educational background, work experience, and key skills, enabling personalized interviews. The technical assessment module administers real-time coding challenges, evaluating solutions for correctness, efficiency, and best practices while providing AI-generated feedback. By automating these key hiring aspects, this system enhances objectivity, efficiency, and decision-making, ensuring a data-driven, unbiased, and scalable selection process while improving the candidate's experience and employer insights