Plastic waste management has emerged as a critical global challenge, prompting concerted efforts from conservation authorities and international organizations like the United Nations to enhance detection and classification strategies. This research distinguishes itself by harnessing advanced deep learning techniques to identify plastic materials at the micron level, surpassing traditional macro-level methods. The primary goal is to classify plastics into four major distinctions, addressing a key challenge in plastic segregation by accurately measuring thickness. Leveraging the YOLOv8 architecture, this approach enables precise classification of plastics into Polyethylene terephthalate (PETE), High Density Polyethylene (HDPE), low density polyethylene (LDPE0, and Polyvinyl Chloride (PVC) categories based on thickness. To achieve this, incorporation of hardware components such as ultrasonic sensors and NodeMCU for detecting thickness variations is used. By facilitating effective segregation according to environmental impact, this innovation revolutionizes waste reduction efforts, offering real-time identification and enhancing overall sustainability in plastic waste management.

In this research, the importance of Optical Coherence Tomography (OCT) in diagnosing and monitoring various retinal disorders, including Drusen, Diabetic Macular Edema (DME), and Choroidal Neovascularization (CNV), is highlighted. These conditions can have a significant impact on retinal health and vision. The research presents a technique that utilizes batch normalization for preprocessing OCT images. For classification of retinal disorders, the research employs the Inception v3 architecture, which is known for its effectiveness in image classification tasks. The performance of the proposed technique is evaluated using performance metrics such as sensitivity, specificity, accuracy, and precision. In this work, a total of 3,133 images were obtained from Kaggle.com. Among these, 710 images were classified as CNV, 895 as DME, 725 as drusen, and 804 as normal retinal images. Python was used for both designing and Google colab was used for executing the algorithm.

Beside the conventional facial expression recognition methods, the research focuses on developing a system for recognizing various eye expressions under different screen conditions. This research deals with the use of Capsule Network (a recent Deep Learning algorithm) to enhance facial expression recognition capabilities and to develop adaptive screen technologies aimed at mitigating digital eye strain. The main objective of this research is to engineer a sophisticated system that employs the capabilities of Capsule Nets to recognize the various expressions that user makes and based on the recognized expression, dynamically modify screen settings, ensuring optimal user visual comfort. The research primarily concentrates on the exploration and application of various Capsule Net architectures designed for the recognition of expressions related to eye strain. The baseline model utilized elementary convolutional layers which feed into subsequent fully connected layers for the task of classification. The model has since been refined by incorporating advanced techniques such as attention mechanisms and more sophisticated network architectures where the classification is done by Capsule Network. Results have demonstrated a modest enhancement in the Capsule Net’s predictive performance, attributed to its superior spatial and hierarchical processing of facial features, in comparison to conventional deep learning approaches. The final model has an accuracy of 82.27%. As a final system the model has been deployed to an application to process frames from video camera in the device and make prediction to prompt the notifications or recommendations.

Heart failure, a leading global cause of death, poses challenges for early prediction of cardiac dysfunction, especially ejection fraction (EF). This study employs Convolutional Neural Networks (CNNs), utilizing ResNet and MobileNet architectures, on the CAMUS dataset with 500 patient records (2CH and 4CH). The goal is to aid healthcare professionals in accurately measuring EF. The CAMUS dataset, comprising multi-modality cardiac imaging and segmentation data, serves as the foundation. The CNN, ResNet, and MobileNet models are fine-tuned through transfer learning and their performance is evaluated based on accuracy. This comparative analysis identifies the model with the best predictive capabilities for EF, showcasing their potential for earlier diagnosis and intervention. Deep learning techniques enhance cardiac healthcare by providing reliable, noninvasive means of predicting heart failure, reducing its impact on patients and healthcare systems.

Using Convolutional Neural Networks (CNNs) for Facial Emotion Recognition (FER) in criminal investigations is the main goal of the proposed study. Using cutting-edge CNN architectures trained on a variety of datasets to precisely identify emotions including joy, anger, fear, surprise, disgust, and sadness is one of the main components. The research seeks to improve the effectiveness of investigations by giving law enforcement a tool to interpret the subtle emotional cues in the facial expressions of potential suspects in crimes. The technique has potential uses in human-computer interface, security, and surveillance in addition to criminal investigations. The larger significance is in helping to create more precise and effective investigative procedures, which will ultimately promote a society that is safer and more secure. The study focuses on providing a brief overview, with a suggested method utilizing CNNs to trace crime suspects.

The rapid progress in technology has transformed the way people learn languages, offering students innovative tools to improve their skills. SpeakBuddy is built using the Flutter framework and utilizes Azure Speech AI for speech functionalities. Speak Buddy incorporates various features, such as interactive conversation practice, detailed lessons covering vocabulary, grammar, and pronunciation, fun games, and AI-powered voice assistance. The app's creation involved several design cycles where feedback from language experts and user testing was used to ensure it was easy to use and effective. Continuous assessments were conducted to evaluate the app's usability, performance, and impact on language learning outcomes. The results showed high levels of satisfaction among users, who reported feeling more confident and proficient in spoken English after using the app. A key feature of Speak Buddy is its ability to provide instant feedback on pronunciation, helping students improve their speaking skills.

Soft tissue tumors (STT) represent a significant medical challenge, requiring accurate and efficient analysis for timely diagnosis and treatment. This survey explores the application of machine learning techniques in the analysis of soft tissue tumors, focusing on enhancing efficiency in detection and classification. The review encompasses various approaches, including traditional image processing methods and the more recent advancements in machine learning. One of the key contributions of this survey is the proposal of a method employing Convolutional Neural Networks (CNN) for the analysis of soft tissue tumors. CNNs have demonstrated remarkable success in image-related tasks, making them particularly suitable for medical image analysis. The research particularly aims in distinguishing Melanoma from normal skin tissue. To enhance the efficiency of research on the analysis of soft tissue tumors, the proposed study provides a comprehensive review of relevant literature, focusing on the application of machine learning in the identification of soft tissue tumors. This review includes an evaluation of the merits and demerits of each system. Furthermore, the study introduces a suggested model capable of providing accurate classification for soft tissue tumors.

Diabetes is a metabolic disorder characterized by a malfunction in insulin release, resulting in a rise in blood sugar levels in the body. Diabetes diagnosis must be made on time and precisely in order to be effective and enhance patient outcomes. The diabetes management presents a formidable challenge in modern healthcare, demanding a combination of timely interventions, precise data analysis, and personalized medical services. Furthermore, there exists a growing demand for advanced predictive models that not only provide accurate forecasts but also offer transparency and interpretability. The study objectives are to develop an innovative machine learning model for data-driven diabetes prediction and medical services. To identify the machine learning model that best suits the proposed system, a literature review related to different methods used in diagnosing diabetes is conducted. The merits and demerits of each existing system were identified to devise a proposed model that seamlessly integrates data from various sources, including blood glucose levels and patient health information. Based on the review, the study suggests an innovative machine learning model that utilizes the Explainable Decision Tree Model, leveraging cloud computing to analyse diabetes patient data and make predictions. The integration of cloud computing allows for seamless data integration from various sources. This research project represents a significant step forward in personalized diabetes care, enabling patients to proactively manage their condition while providing healthcare professionals with a powerful tool for delivering tailored medical services.

With more than 2 billion viewers per month, YouTube is the most widely used video-sharing website worldwide. On this website, users can watch, upload, and share videos covering a wide range of subjects. YouTube comments include facts, opinions, and responses to videos in addition to starting discussions. The number of YouTube comments makes it difficult to manually analyze them all. The study of reading, understanding, and creating text in human languages encompasses a broad range of methods and techniques under the umbrella of natural language processing or NLP. The primary goal of the research is to find and analyze YouTube comments, which, when used with natural language processing algorithms, might be beneficial for the channels' continued development. One of the NLP methods used for this research was tokenization, which is used to break down text into individual words or tokens. Stemming and lemmatization are used to reduce the root words and normalize the variation. Categorization is performed by identifying the named entities, such as people, organizations, and locations. The machine translation was used to convert the comments from one language to another.