JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH & ADVANCES
ISSN: 2395-6720 Scientific Journal Impact Factor (SJIF): 6.744 Journal DOI no.: 10.37591/JoAIRA
STM JOURNALS ( Consortium eLearning Network Pvt Ltd)
Fusion of Intelligence: Biosensors and AI in the Medical Landscape
Healthcare stands poised to benefit immensely from emerging technologies like artificial intelligence (AI) and advanced biological sensors (biosensors). This study provides a comprehensive analysis on the integration of AI algorithms and biosensor devices for transformative health applications, assessing clinical relevance alongside ethical considerations. An overview of biosensors and AI in medical contexts is followed by examining how these technologies synergize for improved diagnostics, treatment personalization, remote monitoring, and other setups. Challenges around regulation, privacy, and AI bias are also highlighted. Case studies showcase cutting-edge implementations detecting cancer, monitoring neonatal health, and more, demonstrating evidence of clinical viability and tangible patient impact. Future directions centre on scalability enablers like biocompatible sensors, nanotechnology, and smart medical devices to deliver AI’s near-boundless analytical potential. The study concludes that multilayered datasets generated by AI-enabled biosensors can revolutionize medicine, but purposeful innovation and governance are vital to guide appropriate adoption while respecting ethical boundaries.
Copyright (c) 2023 JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH & ADVANCES
Stock Price Analysis and Forecasting Using Linear Regression and SVM Classifiers
In this study, we attempt to implement a Machine Learning approach to predict stock market prices. Linear Regression is very effectively implemented in forecasting stock prices, returns, and stock modelling. This project is for common users as the prediction is done on all of the companies. We outline the design of the Linear Regression model with its salient features and customizable parameters. We select a certain group of parameters with a relatively significant impact on the share price of a company. With the help of analysis, the relation between the selected factors and share price is formulated which can help in forecasting accurate results. Although, share market can never be predicted, due to its vague domain, this project aims at applying Machine Learning in forecasting the stock prices.
A Systematic Review for Heart Disease Prediction Using AIML Algorithms
Cardiovascular diseases (CVDs), commonly known as heart diseases, have consistently held the position of being the primary global cause of mortality for many years. They are also the most serious illness in India and the rest of the world. So, a method that is reliable, accurate, and easy to use is needed to find these diseases early and start the right treatment. Using a variety of medical datasets, machine learning and deep learning techniques have been used to automate the analysis of large and complex data sets. In recent years, many researchers have used a wide range of methods to help doctors and other medical professionals find heart-related illnesses. This study looks at a number of models that were made using these methods and techniques, and it figures out how well they work. Researchers have a strong inclination towards models rooted in supervised learning techniques such as Support Vector Machines (SVM), K-Nearest Neighbour (KNN), Naive Bayes, Decision Trees (DT), Random Forest (RF), ensemble models, and various deep learning algorithms.
Complementary Approach of SVM for Heart Disease Prediction
To the point, Coronary-Heart-Disease is in records of killing huge number of lives every year. India contributes to 32% of deaths among all countries. In terms of data, 85% of them resulted from either heart attacks or strokes. In other terms, ⅘ people are risked to heart strokes. Over 75% of CHD deaths occur in low- and middle-income countries, where high blood pressure is one of the most important risk factors for middle age group to the risk of death through CHDs fall in 48 to 70. So, it is a need of time to focus on heart disease and make people in an lively active mode. To predict the accuracy in detection of heart disease we have attempted to describe the popular SVM machine learning along with its own set of strengths which is showed through complementary approach, Confusion Matrix. Our study includes following parts: Introduction, Factor affection heart functioning, Literature Review, SVM as Complementary approach, Methodology, Conclusion and References.
Ensuring Excellence: Quality Assessment of English Text and Speech Data Using NLP and ML
The effectiveness and precision of speech processing and natural language processing (NLP) applications are substantially influenced by the quality of text and speech input. The combination of NLP and Machine Learning (ML) techniques has shown tremendous promise in recent years for improving the quality of written and verbal data. The present research enables a thorough investigation of techniques and strategies to enhance data quality using NLP and ML. The results discussed in this research provide useful insights into cutting-edge methods for combining NLP and ML to assess the quality of English data. The study promotes the reliability and efficacy of English data for various kinds of NLP and ML applications, such as sentiment analysis, information retrieval, and text categorization, and provides the foundation for additional research in this area.
Diagnosing Pneumonia from Chest X-rays Using Deep Learning Algorithms Through Convolutional Neural Network, Transfer Learning and Fine Tuning
Pneumonia is an inflammatory condition of the lungs that induces air sacs which leads to a contagious infection of lungs. Patients who are afflicted with the virus can be saved from death and the virus can be eradicated from spreading further through effective diagnosis. X-rays of the chest are frequently used to diagnose pneumonia. Detecting pneumonia from a Chest X-ray is typically slow and inaccurate. It is essential to identify pneumonia quickly so that patients can receive prompt care, especially in rural areas. This work proposes a system that evaluates chest X-rays and categorizes the images using Deep Convolutional Neural Network Architecture, Transfer Learning and Fine Tuning on different CNN architectures. As part of this project, the implemented algorithms include CNN, VGG16, and Xception. Prior to building a model, Data Augmentation and Data Balancing is performed in order to improve the model’s generalization performance and accuracy. Among the above-mentioned algorithms, it has been deduced that VGG16 model has returned best accuracy.
Revolutionizing Product Marketing: Harnessing the Power of AI/ML
The rapid convergence of artificial intelligence (AI) and machine learning (ML) technologies in marketing has ushered in a new era of product promotion and engagement. This study delves into the many ways in which AI/ML can revolutionize product marketing strategies, showing a comprehensive range of methods and tools available. The presented high-level architecture illustrates the interconnectedness of AI-based marketing components, ranging from personalized recommendations and chatbots to sentiment analysis and video engagement prediction. A detailed exploration of AI/ML's potential is accompanied by a practical guide to employing these techniques. From personalization and predictive analysis to influencer collaboration and sentiment analysis, this study examines how each approach contributes to crafting effective marketing campaigns. Moreover, the integration of AI/ML in marketing is accompanied by a plethora of machine learning algorithms that facilitate various tasks. The provided taxonomy outlines a diverse range of algorithms, their purposes, and estimated timeframes for implementation.
Develop Institutional Chatbot Using Deep Neural Networks and NLTK
Chatbots are intelligent software that can communicate and perform actions like those of a customer service representative. Chatbots are widely used for customer interaction and marketing on social networking and e-commerce sites. AI-based chatbots have the core ability to learn from any question based on initial training on a predefined dataset. A web-based platform provides a broad intelligent foundation for simulating human problem solving. The technology used here is based on deep neural networks and uses NLP for text processing and FLASK functionality for internal connectivity. Evolution has improved accuracy and performance rates on higher slopes. This recommended chatbot identifies the user context that triggers the specific intent of the response. Based on dynamic responses, it instantly generates the desired response for the user. The proposed system uses deep learning algorithms to train chatbots by experiencing different user responses and requests.
A Secure Approach for Cross-chain Transactions Using Machine Learning Model
The ability to conduct transactions or transfer assets between different blockchain networks is referred to as cross-chain transactions. It enables users to transfer assets from one blockchain network to another. In the Cryptocurrency ecosystem, the risk of fraudulent activities has become a significant concern. Due to these fraudulent activities, the cross-chain transactions have encountered challenges in terms of security and integrity. The need for robust fraud detection mechanisms becomes crucial to secure the integrity of transactions and protect investors from fraudulent activities. Detecting those fraudulent activities and preventing them is a challenging task due to the decentralized and pseudonymous nature of these digital assets. Machine Learning algorithms emerged as a power tool for fraud detection across various domains. This study proposes a novel secure approach for fraud detection in cryptocurrency transactions by leveraging machine learning algorithms. Our proposed methodology is to identify malicious activities and discern fraudulent transactions from legitimate ones. The machine learning models are trained on labelled datasets comprising both fraudulent and legitimate transactions by allowing them to learn patterns and detect anomalies indicative of fraudulent behavior. This proposed methodology contributes to the growing body of knowledge in cryptocurrency fraud detection and acts as a foundation for developing robust security measures and risk mitigation strategies to make the cross-chain transactions much stronger.
A Comparative Study on Various Machine Learning Techniques for the Prediction of Cardiac Ailment
Over the last couple of decades, cardiovascular complexities have become the leading source of death in impoverished regions. With heart attack rates on the acceleration at a youthful age, it is necessary to put in place a process to recognize the symptoms of a heart attack early and thus limit it. It is impossible for a common man to often undergo expensive tests such as an ECG and thus there must be a system that is both efficient and stable to forecast the tendency of heart illness. Early recognition of heart complications can diminish morbidity. However, it is impractical to explicitly supervise patients often and an expert’s consultation is not accessible as it needs knowledge and expertise. In this study we generated and analysed the models for forecasting heart illness, supporting a patient’s heart attributes, and detecting approaching cardiovascular illness using the techniques like Gradient Boost, AdaBoost, CatBoost etc., on a dataset accessible publicly on the Kaggle site, with the results significantly assessed using a confusion matrix. In contrast to other machine learning algorithms, the CatBoost classifier approach has the accuracy of 90.16%, in step with the trial conclusions.
Block Chain Based Supply Chain for Grading Milk Quality Using Machine Learning Techniques
Milk is a primary source of nutrition in practically every country on the globe. Milk is widely packaged in a variety of containers such as packages, cans, glass bottles, and polyethylene terephthalate (PET) bottles. Milk, on the other hand, has a limited period for the customer to use it because it has an expiration date. Despite the clearance of the expiration date, it is possible that the milk could have become spoiled due to improper storage after opening the initial container. Numerous prior research studies have been conducted to explore issues related to milk spoilage and staleness. To assess the purity percentage in raw milk, the fraud detection algorithm is employed. This article systematically examines machine learning approaches for detecting milk spoilage and staleness. It delves into various aspects of the detection system, sensor technology, image processing techniques, and machine learning methods. Furthermore, the paper offers insights into the methodologies previously utilized by other researchers in their investigations. Finally, the approach presented in this paper helps prevent milk spoilage and finds the spoilage percentage in advance.
Using Neural Network Method to Construct 3H Content Model
This paper proposes a 3H (hero, hub, and hygiene) content model by neural network method to predict the conversion rate of audiences between each of the three layers. The empirical data is collected from a brand of beauty groceries store. There are134,569 audiences who subscribe to this brand’s Youtube channel over one year are involved to make parameters estimation, weight value of each attribute in each content, and calculate the probability of conversion rate. The results show that in the hub content, the weight value of community and engagement are higher than episode. It means that contributing audience or customer engaging and forming communities are important goals of hub content. In hygiene (help) content, the results show that the weight value of cultural factor is the highest value than the second highest relevant and evergreen and real time is the lowest weight value. It demonstrates that in creating hygiene (help) content, the company shows more attention on the cultural element. For the probability of conversion rate, there is a higher rate of hub content from hygiene (help) content than from hero. Finally, the conclusions are made for future application.
Beyond Abacus, Mathematics Curriculum and Their Extension Using Artificial Intelligence
In today's education system, along with National Education Policy (NEP) 2020, in India, recognizing each student's aptitude for mathematics and collaborating with them to address their queries, worries, and decisions about how to approach math—not just as a subject but also as a way of comprehending, considering, and living it—will aid them in coping with the stress, anxiety, and issues that are usually connected to it. To help students identify their learning path and ultimate goal for mastering math at the school level, as well as any statistics, Data Visualization, Design Thinking, Strategic Thinking- they may wish to pursue outside of the classroom, not limited to: data analytics, machine learning (ML), probability, etc., I investigate the use of artificial intelligence (AI) to diagnose students' current math proficiency and then provide them with worksheets and carefully selected online and offline content that is all mapped with AI. This paper aims at comparing existing systems with usage of AI in monitoring students for their studies and results generation. It also focuses on evaluating and finding out the ratio of higher-order thinking skills (HOTS) as compared with AI skillsets desired among students. We also find the generic causes of bad/below average performance of students in maths, in traditional teaching environment.
Empowering Machine Learning: Unraveling Algorithms, Real-world Use Cases, and Evolving Research Trends
Machine learning has brought about a revolutionary transformation across numerous industries, offering automated solutions to intricate problems. This article aims to delve into the realm of machine learning algorithms, exploring their real-world applications and the evolving research directions in this rapidly advancing field. The paper presents a comprehensive overview of popular machine learning algorithms, encompassing supervised learning, unsupervised learning, and reinforcement learning, while elucidating the underlying principles that govern them. In addition to exploring established algorithms and their applications, the research also delves into emerging areas of investigation that hold promise for the future of machine learning. These frontiers encompass topics like interpretability, bias mitigation, transfer learning, meta-learning, and federated learning, addressing critical challenges and advancements in the field. The primary objective of this paper is to offer readers a comprehensive understanding of machine learning, from its fundamental principles to its practical implementations. By delving into real-world use cases and discussing emerging research directions, the study aims to inspire further exploration and innovation within the machine learning community. Through a deeper comprehension of this transformative technology, the research strives to contribute to the advancement of machine learning and its potential impact on shaping the future across various disciplines.
Machine Learning Based Approaches for the Detection and Classification of Crop Leaf Diseases
Crop leaf diseases pose a significant threat to agricultural productivity worldwide, leading to substantial crop losses and economic consequences. Timely and precise identification and categorization of these diseases are vital for effective disease management and safeguarding crops. Over the past years, machine learning methods have gained prominence due to their potential to automate disease diagnosis and classification procedures. This review paper presents an overview of the various machine learning based techniques employed for the diagnosis and classification of crop leaf diseases. We discuss the fundamental concepts and methodologies of machine learning algorithms used in this context, along with their strengths and limitations. Furthermore, we analyze and compare the performance of different machine learning techniques reported in the literature and highlight the key factors influencing their effectiveness. Lastly, we pinpoint the present challenges and future research avenues to drive progress in this domain.
Data-driven Comprehensive Predictive Model Leveraging Market Insights and Topographical Factors for Crop Productivity and Profitability
For global food security and economic expansion, the agricultural industry is essential. However, farmers face numerous challenges such as unpredictable weather patterns, soil degradation, and market volatility, which affect crop productivity and profitability. This article proposes a prediction model that leverages market trends and topographical factors to optimize crop productivity and maximize profits. The model is designed using machine learning techniques and takes into account variables such as soil type, temperature, precipitation, and market prices. The results of the study demonstrate that the proposed model outperforms traditional crop management methods in terms of crop yield and profitability. The findings suggest that the model could be a useful tool for farmers and agricultural policymakers to make informed decisions regarding crop management and planning. The study also identifies the need for further research to validate and refine the proposed model, as well as to address potential challenges in its implementation, such as data availability and accessibility.
Artificial Intelligence’s Foundation for Neural Correlates of Consciousness
Consciousness is an aggregation and multidimensional phenomenon that has been the subject of intense research across various disciplines. In recent years, the field of computational neuroscience has made significant strides towards understanding the underlying mechanisms of consciousness and its potential implementation in machines. The neural network correlates of consciousness are important elements of neuronal aggregates that are involved in conscious awareness but are not uniformly distributed throughout the central nervous system. This paper presents a study of the current state of research on consciousness in computation, including an overview of the neural correlates of consciousness hypothesis, deep learning approaches, and the potential implications for fields such as artificial intelligence, robotics, and virtual reality. This paper additionally discusses a recent study showing that the structural intrinsic correlates of consciousness are primarily constrained to a posterior cortical hot zone that comprises sensory regions, as contrast to a frontoparietal services and associated in task tracking and reporting.
Solitary Identification and Analysis of Considerate Nutritional Consumption and Identifying Malnutrition
The food supply chain is in danger from various factors, including climate threats, such as heat waves, floods, droughts, and other extreme occurrences such as intensive livestock farming, demand for animal protein, practices for antimicrobial resistance and various other factors leading to serious food shortages and an increase in food prices. Malnutrition is a major issue, with an estimated 663 million individuals affected. Poor nutrition is a serious problem that affects a vast number of people globally. Recurring infections, which make the issue worse, add to this dilemma. The fact that 22% of children under the age of five are considered “stunted,” meaning they are much shorter than the usual child of their age, is one of the most alarming effects of this problem. Additionally, a sizeable portion of people—over 697 million—who account for 9% of the global population—experience extreme food insecurity. The majority of fatalities occur in mostly high and medium poverty nations. One of major factors that helps us identify the quality of nutritional consumption is the body's main defense against external aggressions is its skin, which is affected by chronological ageing and photo-ageing. Poor eating habits and nutritional imbalances are major contributors to undernutrition and overnutrition among individuals, and also obesity, so it is important to build a machine learning model to regulate the flow of nutritional consumption and depict malnutrition among individuals. This model can be used to anticipate malnutrition on an individual basis, making it a more precise and effective method of recognizing the need to avoid incorrect food intake.
Big Data Dimensionality Reduction Technique with Deep Autoencoder
Dimensionality reduction refers to the act of narrowing the focus from an abundance of potential variables to a more manageable set. Google created the open-source Flutter framework for creating mobile apps. The issue of high-dimensional data hindered the system's effectiveness in terms of providing correct findings (the quantity of false-positive, negative, true-positive, and negative outcomes). The process of solving a classification problem, carrying out a precise visualization, and transmitting vast amounts of data will be made easier by the reduction of high-dimensional data. Because of the high dimensional problem and big data, this paper proposes a reduction technique named Deep Autoencoder Network that was used in reducing high-dimension data to low-dimension data. The proposed technique was applied to big data for dimensionality reduction. The outcome of the Deep Autoencoder demonstrates how the big data runtime was decreased and improved accuracy was attained. For test data, 78% accuracy was achieved. The output of the Deep Autoencoder was evaluated by principal component analysis (PCA) and singular value decomposition. SVD and PCA were used to assess the Deep Autoencoder's (SVD) output. Runtime and accuracy were also contrasted. The results show that the Deep Autoencoder outperforms PCA and SVD with an accuracy level of 78%, and runtime of 10 seconds over 14% for PCA, 12% for SVD, with a runtime of 46 seconds, and 53 seconds, respectively.
Comprehensive Survey on Intelligent Prognostication of Profitable Farming
Nowadays, agriculture has become an important field of research. Particularly, when it comes to crop prediction, agriculture depends on soil, climate, and temperature. Formerly, farmers used to decide which crop to cultivate, what is needed for its growth, and when to harvest it at its best based on their expertise. But due to continuous changes in environmental conditions, it has become very difficult to take any decision related to farming manually. Choosing the right crop can lead to more yield and more profit for farmer. Therefore, we have begun to use machine learning techniques for crop prediction in recent years. As crop prediction depends on various environmental factors, deciding these factors is also a crucial part of feature selection. To guarantee that the machine learning model that we are using is working at its best and accurately, we need to use correct feature selection process which can convert raw data into precise dataset with minimum redundancy and more significant features for our model. In this study, we are looking for various feature selection as well as classification methods to be used in suggesting suitable crop mainly in Sangli, Satara, and Kolhapur region in Maharashtra. Along with crop and fertilizers to increase yield, we also propose surrounding industries where farmers can sell the yield and maximize their profit.
Copy-move forgery detection (CMFD) is unquestionably one of the most active research areas in the field of blind image forensics. Most existing algorithms rely on block and key-point strategies, either separately or jointly. Recent applications of deep convolutional neural network (CNN) techniques include image forensics, image hashing retrieval, and picture classification. These techniques have outperformed more conventional techniques in many areas. In this study, a brand-new convolutional neural network based CMFD approach is provided. The proposed approach first uses a pretrained model from a huge dataset, such as Kaggle, and after that performs small-scale modifications to the net structure. Forgery detection can be a particularly challenging task when an altered image is used for compression and other applications. For instance, JPEG picture pressure appears to make falsification recognition more difficult. In any case, several JPEG compression qualities are exploited to identify the alterations left behind during crime scene assessment. JPEG quantization and double JPEG compression are two techniques that can be combined. Neutral images are typically captured under a variety of lighting conditions. As a result, when two or more photographs are combined to create a fashioned picture, the lighting of the produced location might not match the first. In material science-based techniques, the differences in light source between certain objects in the scene are used to find signs of manipulation, changing discovery technique that figures a low-dimensional description of the lighting environment in the image plane and employs the path of occurrence light.
Prediction of Air Pollution LSTM Model Use in Machine Learning
A major environmental problem that has an impact on the health and happiness of millions of people is air pollution. Accurate air quality forecasting is crucial for maintaining human health, the environment, and urban planning. In recent years, machine learning algorithms, such as Long Short-Term Memory (LSTM) models, have been increasingly used to predict air pollution levels. This study presents an abstract for a system that uses an LSTM model for air pollution prediction. The proposed system will collect data from various sources, pre-process the data, train an LSTM model, predict the air quality index (AQI), and alert users when the predicted AQI exceeds a certain threshold level. The system is designed to be scalable, reliable, secure, user-friendly, and compatible with different types of operating systems. This study highlights the importance of air pollution prediction and proposes a solution using an LSTM model that can contribute to improving public health and environmental protection.
Use of Machine Learning to Identify Plant Disease: A Review
As the population is increasing in India, there is a constant problem of food, its main reason is plant diseases. A lot of scientists are looking for new technology to solve this problem. One of the important techniques among them is machine learning. We can find plant diseases with the help of machine learning because machine learning by analysing the data (e.g., plant colour, leaf shape) finds the disease of the plant and also predicts it. With the help of this page, we can see how to solve plant diseases with the help of machine learning techniques.
Application of Generic Multi-criteria Economic Model for Line Balancing to a Hypothetical Organization Using Soft Computing Techniques
Total Productive Manufacturing (TPM) is feasible only if the Overall Equipment Effectiveness (OEE) of the production line meets world-class standards. In the current research, crisp and fuzzy simulation models are developed and implemented for exploring OEE parameters for a hypothetical manufacturing organization. As OEE parameters, availability, performance efficiency, and quality, in turn, depend on several other parameters, a sort of cascaded effect is generated which can effectively be modeled as Cascaded Fuzzy Inference System (CFIS). CFIS is developed and compared with its crisp counterpart. The nature of membership functions is reflected in the fuzzy model and the trend is decided by the overlap of membership functions. The current work is beneficial to any small and medium scale manufacturing organization hosting a single channel multi-phase production line and provides the necessary support from manufacturing method selection to design and analysis of production line, determining optimum service level, performing sensitivity analysis on critical components of the production line, determining OEE and comparing it with world-class standards.
An Efficient Deep Learning Classifiers Algorithm for Examining Public Perception of Covid SOPs
In December 2019, Wuhan, China, reported the first coronavirus case, confirming the current pandemic. Following that, it swept across the entire world. The leading cause of this disease is still unknown, though. Governments are emphasizing physical distance and wearing masks in public places. The people mostly ignore the safety SOPs, which results in a surge in infected people rates and a healthcare burden on the national economy. Through the identification of face masks, this study used machine learning and deep learning models to analyse sentiment. Then, it compared the effectiveness of machine learning and deep learning techniques RMFD, SMFD, and LFW using three modern datasets. We used two deep learning algorithms, Convolutional Neural Network and Visual Geometry Group, and one machine learning algorithm Histogram of Oriented Gradient-Support vector machine, for accurate facemask detection. The reason for using a machine learning algorithm is that it has achieved promising results regarding face mask detection accuracy when trained with a large amount of data. The findings of a comparative analysis with accuracy results have been discussed at the end, and the conclusion is that the proposed framework produced more excellent accuracy results.
Comparing Algorithm Performance in Machine Learning for Landslide Susceptibility Studies: An Overview
Machine learning algorithms have gained popularity over the past years, and with this comes the rise in their efficiencies; this study demonstrated that machine learning algorithms could successfully be used to identify landslide susceptibility areas. Machine learning algorithms like decision trees, support vector machines, K-Means, Hierarchical Clustering, Self-Organizing Maps (SOMs), artificial neural networks, and Bayesian networks have been used in recent studies. This study thus reviews several studies and results from several datasets and the advantages and disadvantages of these machine learning algorithms comprehensively; it is clear that the decision tree algorithm was the most accurate and interpretable algorithm tested and should be considered for further investigations into landslide susceptibility. The SVM algorithm also performed well in accuracy but was not as interpretable as the decision tree. The K-Means, Hierarchical Clustering, SOM, ANN, and Bayesian network algorithms all produced good results but were more complex and less interpretable than the decision tree. With these insights, future studies should focus on developing more sophisticated algorithms that can better capture the complexity of the problem. Additionally, more data should be collected to accurately reflect the area of interest's characteristics. Finally, the evaluation of algorithms should be conducted in a rigorous, systematic manner to ensure that the algorithms accurately capture the underlying relationships between landslide susceptibility and the various factors.
Detection of Cardiovascular Disease using AI and ML: A Review
In the 21st century according to statistics, the risk of cardiovascular disease has become more common and the death rate caused by it is increasing way more. Around 17.9 million lives are taken by CVDs each year. There are various reasons causing it but most importantly it is caused by not identifying it, hence it becomes a very important task for the human race to identify the CVDs and deal with the proper treatment so that the death dance caused by CVDs can be decreased and risk of it at an early age too. This work mainly aims to review and analyse various methods and approaches to detect the presence or absence of CVDs using AI and ML with accurate predictions. An artificial intelligence system for detecting heart disease from phonocardiogram (PCG) signals has been developed utilizing Artificial Neural Networks (ANN) algorithms and also by driving various AI algorithms on the given electrocardiogram (ECG) data of the patients we can predict the absence or presence of CVDs.
Survey of Liver Disease Prediction Using Machine Learning
Liver disease is one of the major causes of death in all over world that impacts the number of people around the world. Diagnosis of liver at early stages is crucial to reduce morality. As technology is growing in health care, machine learning significantly affects health care for predicting conditions at early stages. This study reviews the different approaches based on machine learning and deep learning to predict liver disease. The survey shows that blood test method is used to classify patient as healthy or sick and liver diagnosis based on radiologist images such as Ultrasonic images (US), Computer tomography (CT) images, Magnetic resonance imaging (MRI) etc. are used for liver cancer, fatty liver and cirrhosis detection.
Oculight: AI Based System for Visual Assistance to Blind and Visually Impaired People
This research introduces a new captioning model that utilizes both image and caption models to produce textual descriptions of images. By combining a convolutional neural network (CNN) and a long short-term memory (LSTM) recurrent neural network (RNN), this deep learning architecture offers a promising solution for improving the quality of life and independence of blind people through AIbased systems. The system's implementation on an Android device ensures that it is highly accessible and user-friendly, while the integration of text-to-speech technology further enhances its potential for improving human-computer interaction. The CNN is responsible for extracting meaningful features from input images, which the RNN then uses to generate textual descriptions. This research highlights the potential for future development to expand the system's capabilities to support a wider range of visual tasks and environments and improve the efficiency and accuracy of the model. With further refinement, this technology could have a profound impact on the lives of blind people, improving their overall well-being and sense of independence.
Machine Learning Based Automated Detection of Sepsis: A Study
Sepsis is a serious condition that occurs when the body's response to infection leads to tissue damage, organ failure, or death. Human body generally releases chemicals into the blood vessels to fight any kind of infection. Approximately 30 million people suffer from sepsis and 6 million people die from sepsis every year according to WHO. Around 4 million 200 thousand new-born and children are affected by sepsis all over the world. Early prediction of sepsis and treatment are necessary for improving sepsis results where every hour of deferred treatment has been related with approximately a 4–8% expansion in mortality. In response to the PhysioNet/CinC Challenge 2019, many researchers developed several early detection techniques for sepsis. In this work, the various early detection techniques are discussed and how they work is explained. We have studied and compared techniques developed using Machine Learning in response to PhysioNet/CinC Challenge 2019 for early prediction of sepsis using clinical data. Apart from that we have presented three different methods to detect sepsis early, using the data taken from PhysioNet/CinC Challenge 2019 based on Random Forest, Decision Tree and K Nearest Neighbors.