Volume 12 | Issue 5

 Abstract

Stocksage is a transformative project aimed at revolutionizing stock market participation. By tackling barriers like financial literacy and market complexities, it empowers users with cutting-edge tools. The platform integrates Web Development upon machine learning and regression algorithms for predictive insights, fosters financial literacy, and promotes inclusive and informed investment decisions, both nationally and globally. Beyond its technical sophistication, Stocksage aspires to be an educational resource, promoting financial literacy and understanding of stock market dynamics. By addressing the multifaceted challenges faced by investors, Stocksage endeavors to unlock the latent potential of the stock market, empowering individuals to make informed decisions and participate actively in wealth creation.

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 Keywords

Stock Market, Financial Literacy, Web Development, Machine Learning, Deep Learning

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 Abstract

Distinguishing between authentic and fraudulent information has grown more challenging in the modern information landscape owing to the quick spread of news via digital channels. This project creates and implements a system for detecting fake news that uses state-of-the-art deep learning techniques, specifically Long Short Term Memory (LSTM) neural networks, to address this issue

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 Keywords

Fake News, LSTM, Deep Learning, Neural Network

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Citechgram is a social media platform that draws its inspiration from Twitter and it serves Cambridge University students alone. In this research, we will discuss the possibilities of using cloud computing and web development technologies in building Citechgram. Citechgram can provide an adaptable, safe, and dynamic platform for Cambridge students to connect with each other, share ideas and create a vibrant online community by exploiting cloud-based infrastructure and strong web development frameworks.

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 Keywords

cloud services, Web Application, CITECHGRAM.

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This project focuses on developing a dog identification app using deep learning principles, particularly convolutional neural networks (CNN) and the Inception model. It begins with collecting and preprocessing datasets to optimize images for model training. Emphasis is placed on selecting the appropriate model architecture for precise classification. Mechanisms for detecting dogs in user-supplied images are implemented, and procedures for saving and deploying trained models through an API are established. The app aims to provide accurate identification and classification of dog breeds, catering to researchers studying both physical and behavioral traits. This deep learning-powered solution offers a practical means for canine breed classification, leveraging state-of-the-art image processing techniques.

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 Keywords

Dog Identification, Deep Learning, Image Processing, CNN.

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 Abstract

In computer vision, the field of face emotion for circumstances that comprises a pre-processing detection is expanding with the goal of identifying and comprehending human emotions from facial expressions. We provide a system in this project that combines the CNN (Convolutional Neural Network) and Haar Cascade classification techniques. A sizable dataset of labeled facial expressions is used to train the CNN model, which 21 then uses these characteristics and patterns to identify various emotions. It allows for accurate emotion categorization by extracting high-level abstract features from the input photos. However, the Haar Cascade classifier adds more data for emotional analysis by identifying face landmarks like the lips, nose, and eyes. This cyclical method makes it easier to analyze the emotional states.

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 Keywords

Convolutional Neural Network, Haar Cascade Classifier, Facial Emotion.

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 Abstract

Convolutional Neural Networks (CNNs) play major role in accurately classifying brain tumors identified in medical scans such as MRI. This study presents a CNN architecture tailored specific task particular objective, contains convolutional layers to extract features followed by maximum pooling layers designed for dimensionality reduction. To prevent excessive fitting, dropout layers are employed strategically integrated, ensuring the generalizability of the model fitting. The task of classification involve using fully connected layers with the Softmax The activation function utilized in the suggested CNN architecture demonstrates effectiveness in Classifying brain tumors into three categories distinct types: meningioma, glioma, and pituitary tumors. Experimental evaluation reveals promising results, with the model achieving an overall classification accuracy of 98%. Specifically, it detects glioma with 96% accuracy, identifies no tumor with 99% accuracy, differentiates meningioma with 97% accuracy, and identifies pituitary tumors with 99% accuracy. The dataset comprises 3264 images, 90% of which are for training and 10% for testing. This method holds considerable potential to assist clinicians in accurate and timely diagnosis, thereby facilitating suitable treatment planning for patients with brain tumors. Further research can explore improvements to the network architecture and explore its applicability in different medical imaging datasets.

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 Keywords

CNN, maximum pooling layers, dropout layers, softmax activation

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 Abstract

This project presents an innovative application designed for both standalone and interconnected frameworks, aimed at the real-time automatic detection and localization of food items within dynamic scenes. By harnessing diverse configurations such as Single Shot Detection, Faster R-CNN, YOLO, EfficientDet, RetinaNet, and Mask R-CNN, this study employed A thoroughly managed dataset derived from various online repositories. These configurations were seamlessly integrated with food detection models and multiple convolutional network architectures, harnessing the power of multiple neural networks to enhance performance. Computer vision, an integral part of artificial intelligence, is employed to replicate human perception of three- dimensional structures in visual environments. Through the amalgamation of digital images and advanced deep learning models, this research aims to enable computers to interpret and comprehend the visual world, specifically focusing on the identification and classification of food items. Within the framework of the food industry, this paper highlights the significance of precise object recognition and classification, crucial for ensuring a nutritious diet and overall well-being. With the burgeoning advancements in nutritional science and the availability of diverse smartphone applications, the research aims to present a comprehensive framework capable of autonomously identifying, categorizing, and localizing food elements in various scenes and settings.

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 Keywords

Deep Learning, SSD, EfficientDet, YOLO, Faster R- CNN, RetinaNet, Mask R-CNN

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 Abstract

Sign language is an essential communication language that helps individuals with hearing loss interact with others. Convolutional Neural Network is a useful tool for image processing tasks, including sign language recognition. This paper proposes a novel CNN-based approach to sign language to text translation. The CNN model is intended to effectively extract temporal and spatial properties from sign language containing video sequences. Convolutional layers are utilized to extract hierarchical features, while pooling layers are employed to decrease spatial dimensions without sacrificing important information. The model in this work is trained on a large dataset of sign language images, allowing strong representation learning for accurate translation. The CNN model has performed well in translating American Sign Language into text, according to test results. Using datasets of American Sign Language, the model surpasses previous methods and reaches high accuracy. In general, the suggested CNN-based approach for translating sign language to text provides a pathway between people who use sign language and others who are not familiar with it. By providing real-time translation capabilities, this tool can improve accessibility and inclusivity for the community in a range of situations, such as everyday communication, healthcare, and education. This research promotes more equality and integration for those with hearing loss and enhances assistive technologies.

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 Keywords

Sign language recognition, Image processing, Deaf communication, Gesture-to-text conversion

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Creative Commons Attribution 4.0 and The Open Definition

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This paper presents a comprehensive Quality Assurance Framework designed specifically for text generation models. Our approach combines automated metrics such as BLEU, ROUGE, and perplexity scores with novel techniques for coherence and factuality assessment. We integrate human evaluation methodologies to ensure a balanced assessment of linguistic quality, coherence, factual accuracy, and diversity in generated texts. Through extensive experimentation across different text generation tasks, our framework demonstrates improved evaluation accuracy and provides valuable insights for model refinement and optimization, contributing to the advancement of trustworthy text generation models.

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A Quality Assurance Framework for Evaluation of Text Generation Models

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Distributed denial of service (DDoS) threats represents a significant cybersecurity challenge, constituting a variant of denial of service (DoS) in which IP addresses are exploited to launch attacks to a specific host or victim. DDoS attacks, characterized by meticulous coordination, exploit compromised secondary victims to target one or more victim systems, ranging from large-scale enterprise servers to less. These threats incur significant bandwidth and power costs, leading to the compromise of confidential data. Therefore, developing advanced algorithms to accurately detect various DDoS cyber threats, while considering computational load, has become urgent. The majority of the research currently in publication approaches DDoS threat detection as a binary classification problem, that is ascertaining whether or not an attack has started. However, to effectively protect the network and minimize significant damage, it's critical to distinguish the specific type of DDoS attack targeting the network or system. This study presents a comprehensive classifier that combines the strengths of the four best-performing algorithms. A comparative analysis is performed, comparing the Classifier with different artificial intelligence and machine learning (AI and ML) algorithms. Its goal is to improve the identification of various kinds of DDoS threats by transforming the problem into a multi-label classification scenario. Through this approach, the research aims to contribute to the refinement of cybersecurity strategies, ensuring a deeper understanding and proactive defence against various DDoS cyber threats.

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 Keywords

DDoS, cybersecurity, classification, multi-label, detection, attacks, algorithms, proactive defence, threat identification, network security.

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The goal of this project is to promote inclusivity in social interactions and employment possibilities by addressing the communication issues that people who are blind, deaf, or mute confront. The suggested remedy consists of a two-way smart communication system made to make it easier for people with and without sensory impairments to communicate with each other. The initial component of the system provides an easy-to-use interface that helps people who are blind, deaf, or mute effectively communicate. To empower users with a range of sensory needs, the system makes use of cutting-edge technologies including gesture control, speech recognition, and haptic feedback. Voice instructions, tactile gestures, or both can be used by users to input messages. The system then converts these inputs into a textual and auditory format that is simple to comprehend, making the messages transmitted understandable to people with different levels of communication proficiency. With the use of machine learning algorithms, this interface enables accurate and speedy transcription of spoken words into text through speech-to-text conversion.

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 Keywords

Inclusivity, Social Interactions, Employment opportunities, sensory impairments, gesture control, speech recognition, haptic feedback, Voice instructions, tactile gestures, user-friendly interface, machine learning algorithms, speech-to-text conversion

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 Abstract

Textualizing Sign Language, the challenge purpose is to have a look at numerous techniques for effective inter-communication between Sign Language and its impact on communication. English Language. The version can identify almost every alphabet. Various gestures which include each palm have been added to our dataset. This model has big potential as it could interpret any gesture of diverse Sign Languages if provided in the dataset. The consumer can also upload extra gestures within the dataset, making it rather custom-designed. Further, the data exists to an application to convert the obtained textual content to speech.

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 Keywords

Early Detection, Deep learning, CNN Algorithm, Image Processing.

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The utilization of deep learning techniques in "Deep Learning-driven Food Recognition and Calorie Estimation for Intelligent Diet Monitoring" offers a unique method to improve diet monitoring and encourage healthy eating habits. The main goal of this initiative is to accurately identify various food items and estimate their calorie content in real-time, granting users access to intelligent and tailored diet monitoring features. By utilizing the Python programming language and the MobileNet architecture model for food recognition and calorie estimation, this project has achieved a remarkable level of precision in recognizing and categorizing different food items. Through the use of deep learning algorithms, the system can swiftly analyze input images on the web framework to pinpoint the specific food item within seconds. Additionally, the system can estimate the calorie content of the identified food, equipping users with essential information to effectively monitor their dietary intake. The intelligent diet monitoring capabilities of SmartBite empower users to make well-informed decisions regarding their food selections. By monitoring and evaluating their daily food consumption, users can gain valuable insights into their nutritional patterns, establish personalized objectives, and make necessary adjustments to attain a well-rounded and healthy diet.

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 Keywords

Early Detection, Deep learning,CNN Algorithm, Image Processing.

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 Abstract

We propose a computer vision-driven approach aimed at automating the detection of cracks on railway tracks to enhance inspection and security measures.Beginning with the acquisition of images using digital cameras, the system employs pre-processing methods like color transformation and noise removal to enhance image quality. Image segmentation isolates crucial regions using techniques such as Canny edge detection, while feature extraction utilizes advanced models like ResNet and Darknet to capture intricate patterns. Deep learning algorithms, including YOLOv5 and CNN, facilitate real-time object detection and classification, with YOLO focusing on high-probability areas and CNN performing classification based on extracted features. The proposed algorithm demonstrates a detection accuracy of 94.9% on the acquired images, with an overarching error rate of only 1.5%.

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 Keywords

YOLO, ResNet , DarkNet ,CNN

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 Abstract

Cyberbullying is a severe problem that impacts teens and adults on the internet.It has led to incidents like sadness and suicide.The demand for social media platform content regulation is expanding. To develop a model based on the use of natural language processing to identify cyberbullying in text data and machine learning, the following study uses data from two different types of cyberbullying: hate speech tweets from Twittter and comments based on personal assaults from Wikipedia forums. To determine the optimal strategy, three feature extraction techniques and four classifiers are examined. The model yields accuracy levels over 90%.

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 Keywords

Wikipedia, Twitter, machine learning, hate speech, personal attacks, and cyberbullying

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 Abstract

The most common kind of cancer in women, breast cancer is bad for both physical and emotional health in those who have it.. Breast cancer remains a significant health concern worldwide, with early detection crucial for successful treatment. Due to complexities present in Breast Cancer images, image processing technique is required for detecting cancer. New deep learning techniques were needed for early breast cancer detection. Histopathological pictures are used as the dataset for this research. Breast tissue histopathological investigation is essential for the diagnosis of breast cancer. This project aims to develop a web application using Django, a Python-based web framework, for managing and viewing breast cancer histopathological images. Images are processed using histogram normalization techniques. This project implements the Convolutional Neural Network (CNN) model based on deep learning and helps in improving the efficiency of breast cancer diagnosis.

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 Keywords

Early Detection, Deep learning, Histopathological Images, CNN Algorithm, Image Processing.

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 Abstract

An blood group measurement method is in high demand worldwide, with developing nations having the greatest need for this kind of technology. Building this solution would be a good use for image processing, which is the most widely used device in both resource-rich and resource-poor places. A noninvasive method of measuring blood group is proposed in this project. In order to determine blood groups, it is also compared how different data collection locations,CNN,biosignal processing methods, theoretical underpinnings, photoplethysmogram (PPG) signal and features extraction procedures, image processing algorithms, and detection models vary. The results of this research were then utilized to suggest practical strategies for developing a noninvasive point-of-care tool for blood group assessment based on image processing.

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 Keywords

Non invasive, biosignal processing, photoplethysmogram ,Image Processing.

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 Abstract

Machine learning has application in many different fields worldwide. It is crucial for healthcare professionals to utilize machine learning algorithms and data analysis tools in order to improve patient outcomes and provide more accurate diagnoses. Such knowledge, if anticipated well in advance, can provide physicians with vital intuitions, allowing them to adjust their diagnosis and method specific to each patient. Using machine learning techniques, we are attempting to predict potential cardiac disorders in humans. This study compares the effectiveness of variety of classifiers, comprising of the Random Forest, SVM, KNN, and logistic regression. We also provide an ensemble classifier that combines the best features of both strong and weak identified Modes to conduct hybrid classification, as it may need an abundance of features.

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Machine learning, Heart failure.

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 Abstract

The internet's explosive growth has resulted in a rise in cyberthreats, with phishing assaults presenting a serious risk to both people and businesses. Phishing websites aim to trick visitors into disclosing private information, including bank account information and login credentials. Real-time detection of these harmful websites is essential to protecting consumers' privacy and security when they are online. Online security is seriously threatened by the frequency of phishing attempts, in which malevolent actors try to obtain personal information by pretending to be reputable websites. In order to counter this threat, this project presents an extensible and open-source system that uses an artificial neural network (ANN) to detect phishing websites. The goal of the phishing website detection system is to accurately distinguish between legitimate and phishing websites, improving the capacity to safeguard internet users from malicious attacks

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PHISHING WEBSITE DETECTION USING DEEP LEARNING

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Stress, which is a more and more common part of contemporary life, can have a serious negative effect on a person's physical and mental health. Determining and tracking stress levels is therefore essential to improving general health and quality of life. The "Human Stress Detection Based on Sleeping Habits Using Machine Learning with Random Forest Classifier" project offers a cutting-edge and successful method for determining a person's degree of stress by looking at how they sleep. Utilizing the robust features of the Python programming language, the research makes use of the Random Forest Classifier algorithm, which is renowned for its adaptability and precision in classification assignments .This project's primary objective is to develop a reliable stress detection system 4 that can provide insightful data about people's stress levels, enabling timely interventions and promoting improved mental health. Numerous significant variables related to stress levels and sleep patterns are included in the dataset that was carefully chosen for the study. The user's snoring range, respiration rate, body temperature, limb movement rate, blood oxygen levels, eye movement, heart rate, number of hours slept, and stress levels--which are divided into five classes--are among these parameters. The classes are 0 (low/normal), 1 (medium low), 2 (medium), 3 (medium high), and 4 (high). By including these several criteria, a thorough examination of sleep patterns and their relationship to stress levels is ensured. The model was able to learn complex patterns from the dataset and forecast stress accurately based on the user's sleeping patterns, as seen by the high accuracy that was attain. Research and treatments in medicine as well as personal health monitoring are just a few of the many possible uses for this stress detection system. People can take proactive steps to reduce stress, enhance sleep quality, and promote general well-being by using the system to analyze their sleep patterns and receive insights into their stress levels

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 Keywords

Random Forest Classifier algorithm ,Decision Tree

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