Volume 12 | Issue 5

 Abstract

Upon completing higher secondary education, students around the globe often find themselves at a crossroads, unsure of which career path to pursue. This pivotal moment demands a level of maturity and self-awareness that many students may not yet possess. As individuals progress through these stages, they inevitably confront the question of what to pursue post-graduation. Our proposed solution tackles this challenge with a computerized career guidance system. By objectively assessing individual skills, this system aims to predict the most suitable career path for each student. Through this process, we aim to provide clarity and direction as students navigate their academic and professional journey.

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Counsell Career, Learning Algorithms for machines, Classification(key words)

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A large number of sanitation workers die every year due to erratic and lack of facilities available, and harmful toxic gases released while cleaning the sewage. This can include monitoring the environment for air quality, temperature, humidity, and sound levels, as well as tracking employee activity and movement. This project aims to develop an innovative Smart Safety Monitoring System (SSMS) for sewage workers, leveraging the capabilities of the Internet of Things (IoT) technology. Real time health monitoring systems for such workers will prove helpful. Sewage workers face numerous risks while performing their duties in confined and hazardous environments. The SSMS is designed to enhance their safety and improve communication. This real time health monitoring device will work in a sewage as a safety equipment. In this project, the device presented will monitor the pulse rate of a person using a pulse oximetry sensor, the methane concentration and the atmospheric oxygen concentration and provide alert to worker and exterior unit. when parameters deviate from the safe range. This parameters in real time will promptly alert the workers to stay safe and detect toxic gases before any harm.

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SMART SAFETY MONITORING SYSTEM FOR SEWAGE WORKERS WITH TWO WAY COMMUNICATION

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A groundbreaking flood prediction system emerges, blending meteorological, hydrological, and geospatial data with crowd-sourced inputs, all harmonized within a dynamic machine learning structure. Rigorous assessments affirm its prowess, particularly noting the efficacy of a multi-layer perceptron artificial neural network (MLP ANN) setup in delivering precise forecasts. This pioneering methodology harbors promise in bolstering flood mitigation tactics, streamlining preemptive actions, and fortifying rescue endeavors. This cutting-edge approach marks a pivotal advancement in flood management, poised to revolutionize how communities respond to and mitigate the impacts of inundation events.

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INTELLIGENT FLOOD FORECASTING SYSTEM EMPOWERED BY MACHINE LEARNING

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Cardiovascular diseases, particularly heart disease, remain a leading cause of mortality worldwide, necessitating advanced diagnostic systems that leverage clinical data for early and accurate prediction. Machine Learning integration techniques, particularly ensemble methods, is a way that enhances the precision and reliability of predictive models for heart disease diagnosis. The complex nature of heart diseases demands a comprehensive analysis of clinical data to derive actionable insights. While traditional diagnostic approaches have relied on individual risk factors, the combination of diverse clinical parameters offers a more broad perspective, enabling a more understanding and prediction of cardiovascular outcomes.

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Data Classification, Learning Algorithms for machines, Data Analysis

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Yoga pose estimation is a computer vision technique used to predict the position/ pose of a part of the human body. This paper presents the framework to analyse and assess yoga postures by designing, developing, and implementing a Yoga Posture Detection System using computer vision and deep learning (DL) models such as CNN and VGG16. The study employs advanced image processing algorithms to extract information from images or videos of individuals performing yoga poses. We employed several postures which includes camel, downdog, goddess, plank, tree, and warrior2. With the use of a deep learning model that has been built, the system is able to precisely recognise and categorise different positions while providing instantaneous feedback on proper alignment, balance, and posture.

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CONVOLUTIONAL YOGA POSE ESTIMATOR

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To label this, a paper has been initiated to create a machine learning model for Speech emotion recognition (SER) involves the identification of emotions conveyed in spoken language through analysis of speech signals. With the growing popularity of voice assistants and smart speakers, SER has gained significant attention in recent years. One approach to SER is to use deep learning models such as "Convolutional Neural Networks " (CNNs) and Long Short-Term Memory (LSTM) networks. In this particular paper, we suggest a novel approach for SER using a 2D CNN- LSTM architecture. The proposed model first uses a 2D CNN to extract the relevant characterstics from the speech signal, followed by a LSTM network for sequence modeling. We evaluated our proposed model on the Berlin Emotional Speech Database (EMO-DB), achieving state-of-the-art results. We also balance our model's performance with other existing SER models and found that our suggested model outperformed them. Our speculative results shows that the proposed 2D CNN-LSTM architecture is an effective method for SER and can be used in real-world applications such as recognition of emotion from voice assistants, call centers, and customer service applications.

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CNN, LSTM, 2D CNN LSTM.

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This paper conducts a comparative analysis of four reinforcement learning (RL) algorithms--Q-learning, Deep Q-Networks (DQN), SARSA, and Proximal Policy Optimization (PPO)--using the Pong game as a benchmark. Each algorithm's performance, convergence rate, and computational efficiency are evaluated. Results indicate that while Q-learning and SARSA exhibit simplicity, they struggle with discrete action spaces. DQN, with its capability to handle continuous action spaces, shows improved performance but requires longer training times. PPO demonstrates a balance between sample efficiency and computational complexity, achieving faster convergence and superior performance. This Examination sheds light on selecting appropriate RL algorithms for real-world applications.

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Reinforcement Learning, Pong Game, Q-learning, DQN, SARSA, PPO, Comparative Analysis, Performance Evaluation, Convergence Rate, Computational Efficiency.

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In recent years, transformer-based models have reshaped the landscape of computer visions, particularly in image classification tasks Vision Transformers (ViT) and BEiT (BERT Pre-Training of Image Transformers) stand out as notable examples, employing self-attention mechanisms. This paper presents a detailed comparative analysis of ViT and BEiT, aiming to elucidate their performance, strengths, weaknesses, and interpretability in image classification Through extensive experimentation across diverse benchmark datasets like CIFAR-10, CIFAR-100, and ImageNet[1], we evaluate the models based on classification accuracy, training efficiency, generalization capability, and robustness to adversarial perturbations Our findings offer insights at nuanced differences between ViT and BEiT, revealing ViT's efficiency and small-scale datasets, while highlighting BEiT's enhanced robustness to adversarial attacks and domain shifts Furthermore, we research the interpretability of learned representations and visualize attention patterns generated. The ability to capture meaningful image features and the comparative analysis not merely informs practitioners and researchers in computer visions but also paves the way for further advancements in transformer-based architectures for visual understanding.

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Transformer-based Models, Vision Transformers, BEiT, Image Classification, Self-Attention Mechanisms, Comparative Analysis, Interpretability, Robustness, Adversarial Attacks, Computer Vision.

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This paper introduces an innovative fusion of classical dance and artificial intelligence, focusing on the esteemed art form of Bharatanatyam. Our pioneering framework harnesses the power of Generative AI techniques to revolutionize both the creation and experience of Bharatanatyam performances. Through advanced machine learning models, textual descriptions are seamlessly translated into visually captivating dance sequences, effectively capturing the essence and intricacies of this ancient art form. The system not only facilitates the creation of choreography but also offers a user-friendly interface tailored for artists, enthusiasts, and learners alike, thereby fostering unprecedented engagement with Bharatanatyam. By meticulously preserving the grammatical structure and predefined steps inherent in Bharatanatyam, our approach ensures an authentic representation of this rich cultural heritage. Moreover, this project serves as a catalyst for revitalizing classical dance by infusing it with cutting-edge technology, while simultaneously encouraging creative exploration and interpretation. We firmly believe that this harmonious convergence of tradition and technology will not only redefine the boundaries of artistic expression but also significantly impact the future trajectory of cultural preservation and appreciation.

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Bharatanatyam, Generative AI, Dance Creation, Cultural Heritage, Artistic Expression.

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This paper presents a comprehensive framework for combating fake news by integrating deepfake video detection and text analysis techniques. With the proliferation of misinformation, especially through deepfake technology, there is an urgent need for robust detection methods. Our approach involves extracting text from social media posts, generating interrogative sentences, querying a web server for relevant information, and summarizing the authenticity of news, videos, or posts. By combining advanced AI algorithms for deepfake detection and text analysis, our framework offers a powerful solution to enhance the credibility of news sources and combat the spread of misinformation in digital media. Keywords-- Deepfake video detection, Text analysis, Fake news detection, Misinformation, Artificial intelligence (AI), Generative AI, Deep learning (DL), Natural language processing (NLP), social media, Web server querying, Factchecking, Digital media ecology.

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Deepfake video and Text Detection, LSTM, Artificial intelligence (AI), Generative AI, Deep learning (DL), Natural language processing (NLP).

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This paper presents a novel approach to driver sleep detection using deep learning techniques to enhance road safety. With the increasing number of accidents caused by drowsy driving, an effective detection system is needed. Our framework uses convolutional neural networks (CNNs) to analyze facial expressions, eye movements and head positions captured by an in-vehicle camera While extracting meaningful features from these inputs , the proposed model differentiates driver warning and sleep states accurately in real time. RNNs) are employed to further improve detection performance Extensive tests on various datasets demonstrate the efficiency and robustness of the proposed method under various lighting conditions and driver characteristics enable integration in onboard systems a it already exists without it. Overall, the proposed deep learning-based method provides a practical and reliable solution to enhance road safety by better detecting driver sleep in world conditions in the self-contained.Cambridge Institute of technology Bangalore, India

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Driver Drowsiness Detection Using Deep Learning

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This paper presents an innovative approach for myocardial infarction (MI) detection through an ensemble of three distinct models: Support Vector Machine (SVM), Random Forest, and Convolutional Neural Network (CNN). Trained on labeled electrocardiogram (ECG) image datasets, each model is individually optimized for effective discrimination between MI and non-MI cases. The models' unique strengths, encompassing SVM's handling of high-dimensional feature spaces, Random Forest's ensemble learning, and CNN's proficiency in hierarchical feature extraction, are strategically combined through the AdaBoost ensemble method. The resulting ensemble model is rigorously evaluated on a separate set of ECG images, demonstrating its enhanced diagnostic accuracy. Key performance metrics, including accuracy, precision, recall, and F1 score, are presented to assess the ensemble model's robustness in real-world clinical applications. This research contributes to the advancement of medical image classification by showcasing the potential of ensemble methods in improving myocardial infarction detection accuracy.

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Myocardial infarction detection, Ensemble learning, Support Vector Machine (SVM), Random Forest, Convolutional Neural Network (CNN), AdaBoost, Electrocardiogram (ECG) images, Diagnostic accuracy, Feature extraction

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In this paper we aim to develop an prediction system using machine learning to detect and classify the presence of diabetes in e-healthcare environment using Ensemble Decision Tree Algorithms for high feature selection. A significant attention has been made to the accurate detection of diabetes which is a big challenge for the research community to develop a diagnosis system to detect diabetes in a successful way in the e-healthcare environment. In this paper we aim to develop an prediction system using machine learning to detect and classify the presence of diabetes in e-healthcare environment using Ensemble Decision Tree Algorithms for high feature selection. A significant attention has been made to the accurate detection of diabetes which is a big challenge for the research community to develop a diagnosis system to detect diabetes in a successful way in the e-healthcare environment. The existing diagnosis systems have some drawbacks, such as high computation time, and low prediction accuracy. To handle these issues, we have proposed diagnosis system using machine learning methods, such as preprocessing of data, feature selection, and classification for the detection of diabetes disease in e- healthcare environment. Model validation and performance evaluation metrics have been used to check the validity of the proposed system. We have proposed a filter method based on the Decision Tree algorithm for highly important feature selection. Two ensemble learning Decision Tree algorithms, such as Ada Boost and Random Forest are also used for feature selection and compared the classifier performance with wrapper based feature selection algorithms also. Machine learning classifier Decision Tree has been used for the classification of healthy and diabetic subjects. The experimental results show that the Decision Tree algorithm based on selected features improves the classification performance of the predictive model and achieved optimal accuracy. Additionally, the proposed system performance is high as compared to the previous state-of-the-art methods. High performance of the proposed method is due to the different combinations of selected features set. Furthermore, the experimental results statistical analysis demonstrated that the proposed method would be effectively detected diabetes disease.

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Ensemble learning Decision Tree algorithms, such as Ada Boost and Random Forest

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The assignment "class of site visitors signs and signs and symptoms using deep studying" represents a tremendous expand inside the situation of laptop imaginative and prescient with a completely unique cognizance on the recognition and kind of site visitors signs and symptoms. We used the energy of Python to resolve a complex visitors signal class trouble the usage of prominent models: the MobileNet and YOLOv5 architectures. The MobileNet structure done vast tiers of typical overall performance with a schooling accuracy of 97.00% and a validation accuracy of 98.00%. The quit end result is a set of four,100 seventy carefully curated snap shots overlaying fifty eight education of numerous road symptoms, which incorporates speed limits, site visitors signs and symptoms, prohibition symptoms and signs, threat warnings, and additional. the ones hours cover the overall range of site visitors rules and offer complete coverage of what is going to be stated. The YOLOv5 implementation introduced real-time road sign reputation using actual-time pictures and webcam statistics. The version changed into professional on a dataset containing 39 specific website online visitors sign instructions. those instructions encompass a enormous style of signs and signs and symptoms at the side of pedestrians, pace limits, warning and regulatory signs, and help you observe your mission to actual-international

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Traffic Sign Recognition, Neural Network Architecture, Object Detection

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This paper proposes a novel approach utilizing machine-gaining knowledge of strategies and Xception architecture for PD detection, focusing on spiral and wave drawings, common diagnostic tools in clinical practice. Through a dataset collection process, including individuals with and without PD, preprocessed data were employed to train machine learning models. Results indicate promising performance, demonstrating the potential of machine learning and Xception architecture in early PD detection. This approach offers advanced accuracy and efficiency in diagnosis, ultimately leading to better patient outcomes and enhanced quality of life.

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Parkinson's disease, Neurodegenerative disorder, Machine learning, Xception architecture, Early detection, Diagnosis, Spiral and wave drawings, Clinical practice, Image classification, Convolutional neural networks, Depthwise separable convolutions, Inception modules, Model performance

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This study provides an in-depth examination of sophisticated machine learning techniques for predicting brain strokes using the Healthcare Dataset Stroke Data. Brain stroke prediction is a critical task in healthcare, having the capacity to greatly enhance patient outcomes via early identification and intervention. In this study, We evaluate the effectiveness of four cutting-edge algorithms: Convolution-Based Neural network(CNN), CNN with Long Short-Term Memory (CNN-LSTM) architecture, XGBoost, and LightGBM. We evaluate these algorithms based on their predictive accuracy, sensitivity, specificity, and computational efficiency. Our research clarifies the advantages and disadvantages of each algorithm in the context of brain stroke prediction, providing valuable insights for healthcare practitioners and researchers seeking to leverage machine learning for early stroke detection. This dataset is used to predict whether a patient is likely to get stroke based on the input parameters like gender, age, and various diseases and smoking status. A subset of the original train data is taken using the filtering method for ML and Data Visualization purposes.

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Brain stroke prediction, XGBoost, LightGBM, Convolution neural networks (CNN), CNN-LSTM, Early stroke detection, Data visualization, healthcare stroke dataset.

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The increasing popularity of the electric vehicles(EVs) has spurred the need for best battery thermal management systems to ensure optimal performance, longevity, and safety of energy storage systems. It focuses on leveraging Artificial Intelligence (AI) technologies, specifically the Multilayer Perceptron (MLP) algorithm, to enhance the efficiency of battery thermal management in EVs. I algorithms, such as MLP,offers the potential to model and predict the thermal behavior of batteries more accurately, allowing for real- time adjustments and improved control strategies. With the large-scale commercialization and growing market share of electric vehicles (EVs). Their focus has been on higher energy efficiency, an improved thermal performance, and optimized multi- material battery enclosure designs. The combination of simulation-based design optimize the battery pack and Battery Management-System (BMS) is evolving and has expanded to include novelties such as artificialintelligence/machine learning (AI/ML) to improveefficiencies in design, manufacturing, and operations for their application in EVs and energy storage systems. Specific to BMS, these advanced concepts enable a more accurate prediction of battery performance such as its State of Health(SOH), State of Charge(SOC), and State of Power(SOP). This study presents a comprehensive evaluation of the latest developments and technologies in battery design, thermal management, and the applicationof AI in Battery Management Systems(BMS) for electric vehicles (EVs).

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Battery Thermal Management in EV Using AI

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With the growing use of authentication systems in the recent years, fingerprint spoof detection has become increasingly important. In this model, we use Convolutional Neural Networks (CNN) for fingerprint spoof detection. Our system is trained on the datasets used in The Liveness Detection Competition of years 2009, 2011 and 2013, which comprise almost 50,000 real and fake fingerprints images. The CNN is pre-trained on natural images and fine-tuned with the fingerprint images, CCN with random weights, and a classical Local Binary Pattern approach. The project shows that pretrained CNNs can yield state-of-the-art results with no need for architecture or hyperparameter selection. Dataset Augmentation is used to increase the classifiers performance, not only for deep architectures but also for shallow ones. We also report good accuracy on very small training sets (400 samples) using these large pre-trained networks. The model achieves an overall rate of 97.1% of correctly classified samples - a relative improvement of 16% in test error when compared with the best previously published results

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Fingerprint recognition, Feature extraction, Convolutional neural network

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The "Converting podcast episodes into text format and summarizing them" project aims to automate the process of summarizing podcasts, making it convenient for users to quickly grasp the key points of lengthy audio content. The process begins with data collection, where podcast episodes are gathered either as transcripts or audio files. For transcripts, preprocessing techniques are applied to clean the text data, removing unnecessary characters and tags. For audio files, speech recognition tools are employed to convert spoken words into text. The summarization techniques primarily include both extractive and abstractive methods.

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CONVERTING PODCAST EPISODES INTO TEXT FORMAT AND SUMMARIZING THEM

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Automatically identifying animal species in images is vital for ecology, conservation, and biodiversity studies. Deep learning, particularly convolutional neural networks (CNNs), has become a powerful tool for this task. We compared five pre-trained CNN models (AlexNet, VGG16, VGG19, ResNet50, InceptionV3) on a dataset of 20 animal species with 19 classes from KTH-Animal dataset and one class from Kaggle dataset. Our approach involved fine-tuning these models with pre-extracted features. We evaluated accuracy, precision, recall, F1 score, false acceptance rate (FAR), and false rejection rate (FRR).VGG16 achieved the highest accuracy (95.73%) and F1 score (0.94), excelling at correctly identifying animals with minimal misclassifications (FAR and FRR of 5% each). InceptionV3 followed closely (94.51% accuracy, 0.95 F1 score). AlexNet and ResNet50 showed a trade-off between precision and recall, making them potentially useful for specific needs. This study highlights the effectiveness of pre-trained features in CNNs for animal species recognition, especially after fine-tuning. This approach reduces reliance on large, labeled datasets, making it valuable for ecological applications with limited data. Our VGG16-based approach outperforms previous works, showcasing the potential of deep learning for animal species recognition

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Animal species recognition, deep convolutional neural networks, transfer learning, camera-trap, KTH dataset.

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