Due to a lack of resources for diagnosing mental illnesses including depression and early treatment, millions of individuals suffer from mental illness.Anxiety disorders, bipolar disorder, sleep difficulties, depression, and in certain situations, self-harm or suicide, are frequently caused by mental health problems.Because of this, identifying individuals who are having difficulties and offering prompt treatment continue to be extremely difficult tasks.Traditionally, patient interviews and Patient Health Questionnaire (PHQ) scores were used to identify facial expressions; however, these techniques frequently lacked accuracy.In order to increase accuracy, this study presents a hybrid deep learning model for emotion identification that makes use of visual characteristics from patient answers.The "Japanese Female Facial Expression (JAFFE)" and "Karolinska Directed Emotional Faces (KDEF)" databases are used to identify the behavioural characteristics of emotions.To expand the original dataset, the Generative Adversarial Network (GAN) model generates synthetic data.The suggested hybrid model for feature extraction makes use of "Long Short-Term Memory (LSTM)" approaches and a Convolutional Neural Network (CNN)" model for emotional characteristics that have been developed.According to the results, deep learning is a more effective method of detecting emotion, with Hierarchical Convolutional (HC)-LSTM models attaining a 99.09% total accuracy.The results show the effectiveness of emotion data, with a "HC-LSTM" loss of 0.2.Compared to other models, "HC-LSTM" has a greater learning rate.Plots showing training loss, validation loss, training accuracy, and validation accuracy are also included in the findings.Additionally, confusion matrix analysis was used to compare predicted and real labels, offering HC-LSTM and LSTM models insights into emotion detection.

Understanding the motives behind news dissemination is essential in today's digital age to ensure media transparency and trust.While extensive research has focused on detecting fake news, limited attention has been given to the computational analysis of the underlying motives driving news propagation.This paper introduces Insight-FakeNet, an Explainable Artificial Intelligence (XAI) model that leverages Natural Language Processing (NLP) techniques to classify and interpret the motives behind news dissemination.Utilizing the MIND-Set dataset, a re-annotated version of widely used fake news datasets with motive-specific labels, Insight-FakeNet identifies motives such as political influence, financial gain, entertainment, propaganda, and policy-driven agendas.The model incorporates TF-IDF-based feature extraction and evaluates several tree ensemble methods, with LightGBM achieving an F1 score of 0.92, outperforming other approaches.To ensure interpretability, the model integrates Local Interpretable Model-agnostic Explanations (LIME), offering insights into key linguistic features that influence classification decisions.The findings demonstrate the model's capability to computationally analyse and explain diverse motives in news content, aiding stakeholders such as policymakers, youth online safety practitioners, journalists, and AI practitioners in fostering trust and accountability in media systems and mitigating online harms among young audiences.This research bridges gaps in motive analysis and contributes to advancing explainable AI applications in computational language processing, with direct implications for youth online harm mitigation.

Original equipment manufacturers face difficulties in lowering diesel engine emissions due to strict emission regulations.Nitrous oxide (NOx) emissions were a major concern during the development of diesel engines.The selective catalytic reduction method known as urea-Selective Catalytic Reduction (Urea -SCR), which uses urea as a reducing agent, has been thoroughly studied and is now widely used for the reduction of NOx emissions in a diesel engine.This study examined the performance, combustion, and emission characteristics of a diesel engine running on annona seed oil biodiesel.In order to reduce the NOx emissions from the engine, the biodiesel was supplemented with 50 ppm zinc oxide (ZnO) nanoparticles.A cutting-edge technology known as the SCR system was employed to further improve the NOx reduction work.For further comprehension and research, the conventional diesel (D), 20% biodiesel blend (B20), and B20 with 50 ppm ZnO (B20Z50) were prepared and used as fuel for the engine with and without SCR.Compared to biodiesel fuel, the addition of nanoparticles showed 3.2% decreased NOx emissions.The SCR system decreased NOx emissions for diesel, B20, and B20Z50 by 7.81%, 9.51%, and 9.05%, respectively.

Renewable energy's growing importance, driven by dwindling fossil fuel resources and heightened climate change awareness, underscores the need for innovative solutions.The unregulated nature of renewable energy has made DC-DC power converters, attractive due to their adaptability across a wide power range.The industrial focus on the reliability of DC-DC power converters has prompted the design and analysis of controllers like Proportional-Integral (PI) and Linear Quadratic Regulator (LQR) using a linear approach.The linear approach to control design simplifies the analysis and design of controllers by approximating the converter's behavior near an operating point.This project emphasis lies on achieving a stable and desired output of 24 V, 16.8 W for LED driver applications.The project contribution lies in its unique comprehensive approach, encompassing both the design and analysis of controllers for a boost converter in the renewable energy context.By exploring the difference of PI and LQR controllers, the research aims to optimize stable and desired output, such as robustness and dynamic response.

Advancements in technology and the rapid growth of the internet have transformed it into a crucial national resource, supporting various sectors, including national security.However, these developments have also paved the way for significant network threats, with Distributed Denial-of-Service (DDoS) attacks being among the most disruptive.This study introduces an innovative Adaptive Rate Limiting with Dynamic Defense Framework (ARL-DF) designed to detect and mitigate DDoS attacks effectively.By employing a two-phase defense mechanism consisting of immediate rate limiting and a subsequent controlled recovery phase, the framework ensures robust protection It calculates an Attack Severity Score and leverages entropy-based analysis to enhance detection accuracy.Experimental results indicate that ARL-DF successfully reduces malicious traffic while preserving service availability, outperforming conventional methods in maintaining consistent throughput under varying attack intensities.Its adaptive and resilient design makes it a reliable solution for defending against evolving DDoS threats

The low spectral efficiency (SE) of analog precoding, a hybrid precoding (HP) algorithm with efficient phase shifters (PSs) is proposed for mmWave massive MIMO vehicle-to-vehicle (V2V) system.Specifically, in this paper, digital and analog precoding non-convex and matrix variable constraints of the radio frequency (RF) precoder and RF combiner are decomposed into two independent optimization problems, jointly designs the digital precoder and analog precoder, and uses the discrete iterative optimization idea to solve the analog precoder and the digital precoder alternately.In addition, the effect of finite-resolution PSs is taken into account in the precoder algorithm optimization.Simulation result shows that the suggested algorithm has fast speed, and it can approach the SE of full digital precoding with only 3-bits resolution PS, and the performance difference among the suggested algorithm and the existing hybrid precoding algorithm with infinite-resolution PSs is almost negligible.Moreover, the proposed algorithm performs superior than that of the infinite-resolution PS hybrid precoding algorithm and full digital precoding algorithm.