Browse Articles
Discover research articles across all indexed journals
Vocal Based Parkinson’s Disease Detection
James Parkinson described Parkinson’s disease as a neurological syndrome, it affects the central nervous system as a result the patients face the issue in talking, strolling, tremor during motion. Parkinson’s disease patient typically has a low-volume noise with a monotone quality; this system explores the classification of audio signals feature dataset to diagnosis Parkinson’s disease (PD), the classifiers we utilized in this system are from Machine Learning. The significant classification models that are effectively utilized are (K-Nearest Neighbor) KNN, Decision Tree, Logistic regression and eXtreme Gradient Boost (XGboost). The performance analysis of the Parkinson’s disease datasets has a variety of features. There are 21 features which play an important role for predicting the best algorithm in the classification. The system has achieved better result in predicting the PD patient healthy or not, XGboost provided the peak accuracy of 92% and the Matthews Correlation Coefficient (MCC) of 78%. MDVP: Jitter (%), Jitter (Abs), MDVP: RAP, MDVP: PPQ, Jitter: DPP, MDVP: Shimmer, MDVP: Shimmer (db), Shimmer APQ3, Shimmer APQ5, MDVP: APQ, Shimmer: DDA, NHR are the selected features from the dataset, which are important for the prediction to achieved high accuracy.
Fall Detection
Researchers from all across the world are interested in human fall detection and activity recognition. Fall detection is an exciting topic that may be tackled in several ways. Several alternatives have been suggested in recent years. These applications determine whether a person is walking, squatting, or falling, among other activities. Among these tasks detecting elderly falls is crucial. This is because it is a pretty typical and dangerous occurrence that affects people of all ages, with the elderly having a disproportionately negative impact. This is the motivation behind the development of an aged assistance system that detects falls and notifies the caretaker. One of the key characteristics is that it sends an email notification along with a call to the caretaker once a fall is detected.
An Efficient and Intelligent Decision Making For Eco Fertilizers
Fertilizer use is typically under the limited control of farmers. For the farmers to achieve higher yields and reduce fertilizer loss, competent guidance is required for the best use of these fertilizers. Additionally, there is a connection between rainfall volume and nutrient loss for various fertilizer applications after each rainfall event. Rainfall that is moderate and falls at the right moment can help nutrients penetrate the soil's rooting zone and dissolve dry fertilizer. However, too much rain can increase the possibility of runoff and the pace at which nutrients like nitrogen (N) which is quintessential, phosphorus (P), and potassium (K) which is crucial, manganese (Mn), and boron (B) that is present in the soil. This research presents nutrient recommendations using an updated iteration of the random forest algorithm which is based on time-series data to forecast the required quantity of nutrients for various crops by examining rainfall patterns and crop fertility. The method suggested in this study, comes in handy for improving soil fertility by providing nutrients recommendations for optimum conditions for crop growth and reducing leaching and runoff potential.
Virtual Notes Assistant Using ML
The voice assistance takes the voice input through our microphone (Bluetooth and wired microphone).It converts our voice into computer understandable language gives the required solutions and answers which are asked by the user. Voice assistance makes people smart and gives instant and computed results. The main focus is on user based information through user communication. They are able to interpret human speech. An intelligent software program called a virtual notes assistant (VNA) employing machine learning (ML) offers individualized note-taking and task management solutions. To increase productivity and lessen cognitive burden, it makes use of speech recognition, natural language processing, and user behavior analysis.
Landslide Monitoring and Detection
The project titled "Landslide Monitoring and Detection" aims to detect and monitor potential landslides using a combination of sensors and machine learning algorithms. The sensors used in this project are MQ, rainfall, vibration, soil moisture, DHT, and water flow sensors. These sensors collect data that is sent to an Arduino Uno, which then sends it to a Node MCU. The Node MCU sends the data to a Java server using NetBeans IDE, which stores the data in a text file. The machine learning algorithms used in this project are KNN, random forest, XGBoost, and decision tree. These algorithms are applied to the data in the text file, with a given threshold value based on training data, to determine which algorithm provides the best accuracy. The algorithm with the highest accuracy is then used as the result for landslide detection and monitoring. The use of multiple sensors and machine learning algorithms improves the accuracy of landslide detection and monitoring. The sensors provide real-time data that is continuously monitored and analyzed by machine learning algorithms. The results of this project can aid in preventing and mitigating the effects of landslides, which can cause significant damage to infrastructure and loss of life. This project has the potential to be implemented in areas prone to landslides, providing early warning systems and improving disaster management strategies.
Smart Shoes for Blind
Blind people come across various challenges in their life. They take assistants of a guardian or a care taker to reach their destination. There are over 2.2 million people who suffer from blindness. The proposed system overcomes this problem by using Arduino UNO board with AtMEGA328P microcontroller and an Android Application which is voice controlled. The Ultrasonic sensors detect the manhole, staircase and obstacles. The Moisture Sensor is used for detecting the water and gives an alert Buzzer Sound to the user while walking. The Android Application is connected through Bluetooth to the Arduino board so that it can give the voice messages to the user.
Food Recommendation System
In the past, people obtained suggestions for restaurants from friends or other conventional sources or sites. Although this method is straight forward and user friendly, it has some severe limitations. First, the recommendations from friends or other common people are limited to those places they have visited before. Thus, the user is not able to gain information about places less visited by their friends. Besides that, there is a chance of users not liking the place recommended by their friends. Second, the information provided by the site can often be biased; thus, the information provided cannot always consider being accurate. Recommender systems are widely deployed to predict the preferences of users to items. They are popular in helping users find restaurants and products in general. The primary aim of the Food Recommendation System is to suggest the best restaurant nearby along with food using the given user preferences. This application takes the food preferences and ratings into consideration to recommend the food to the users.
CV Building Chatbot with Career Assistance
A professionally created resume is the foremost requirement to land your dream job. CV Building Chatbot with Career Assistance is a text-based chatbot designed and developed to help students in designing an effective and attractive CV and to also provide career assistance with course recommendation based on the skills they possess. The Chatbot is designed using Natural Language Processing (NLP) to ensure utmost efficiency of the chatbot. A usable system using the proposed algorithm will be implemented. A comparative analysis of all the algorithms was done to select an algorithm with the best efficiency. Ideas of chatbots with other domain are also considered in order to ensure that CV building chatbot with career assistance will have all the functionalities and provide the best service and performance. A unique feature of chatbot is that students can get access to different templates provided and can get career guidance based on the information they have provided.
Pothole Detection Using Deep Learning
Particularly in poor nations, potholes are a nuisance that frequently cause car damage or danger to the people inside of moving vehicles. The potholes are holes on roadway surface that can cause damage to vehicles which can cause accidents. Poor road conditions are not just a public nuisance because that causes discomfort to drivers, passengers, as well as damage to vehicles which leads to accidents. The pothole detection project helps drivers avoid potholes on a given route by providing advance alerts of their presence. Create a piece of software that can locate potholes is the plan. The concept may be expanded to create automobiles that can recognize road abnormalities like humps. The program may be used to efficiently address the issue of rising accidents brought on by potholes. And also can be synchronized with GPS or NAVIC to create a real time inter-vehicular alerting system.
Fake Currency Detection
Indian is a developing country, Production, and printing of Fake notes of Rs.100, 500 and 1000 were already there but after the demonetization, the counterfeit notes of new Rs.50,200,500,2000 have also come to the light in very short time and which effects the country’s economic growth. From last few years due to technological advancement in color printing, duplicating, and scanning, counterfeiting problems are coming into the picture. In this article, recognition and verification of paper currency with the help of digital image processing techniques is described. The characteristics extraction is performed on the image of the currency and it is compared with the characteristics of the genuine currency. The currency will be recognized and verified by using image processing techniques. The approach consists of a number of components including image processing, edge detection, image segmentation and characteristic extraction and comparing images. The desired results will be the text and voice output of the currency recognized and verified.
Trust Enhanced Collaborative Filtering Recommendation Algorithm
Recommender systems have been designed to suggest to users the suitable items based on the user profiles and therefore reduce the danger of information overload, however, the recommender systems are still prone to profile injection attacks which then exposes a user to a potential fraud, which leads to a sense of untrustworthiness and reduced accuracy due to malicious manipulations. In this research, we developed a model which should be embedded into the recommendation pipeline in order to improve trustworthiness of recommender system output. We extended the classical collaborative recommendation algorithm by adding a new trust parameter and then compare the prediction accuracy of the trust enhanced collaborative filtering algorithm against that of the classical collaborative filtering algorithm using Mean Absolute Error and Root Mean Square Error and then test the hypothesis using t-test. We found that the new trust parameter improves the accuracy of collaborating filtering algorithm significantly.
Knee Osteoarthritis Radiology Assistant
Knee osteoarthritis is a degenerative joint disease affecting millions worldwide. Early detection and monitoring of knee osteoarthritis are critical for effective management of the disease. Magnetic resonance imaging (MRI) is a powerful tool for detecting knee osteoarthritis, but the interpretation of MRI images can be prolonged and subjective. In this paper, we propose a deep learning-based approach for the automatic diagnosis of knee osteoarthritis from MRI images. Our approach involves a deep learning architecture known as Dense Net, which has shown promising results in image classification tasks. We also incorporate a Squeeze-and- Excitation (SE) layer into the network, which can selectively emphasize informative features in the input images. We train and validate our approach using the OAI dataset of MRI images from patients with knee osteoarthritis and healthy controls. The images will then be fed into the Dense Net with SE layers to automatically classify them as either healthy or osteoarthritis. The python script generates a sample report for the MRI scans uploaded in the portal. The patient should be able to access their report but should also be able to generate a report if they have their MRI scans. The proposed model with Dense Net architecture gives an accuracy of 88.5%. The performance of our approach will be evaluated using various metrics such as accuracy, precision, recall, F1 score, confusion matrix, and area under the receiver operating characteristic curve (AUC-ROC).
EMOJIFIER
Emojis are small images that are commonly included in social media text messages. The combination of visual and textual content in the same message builds up a modern way of communication. Emojis or avatars are ways to indicate nonverbal cues. These cues have become an essential part of online chatting, product review, brand emotion, and many more. It also led to increasing data science research dedicated to emoji-driven storytelling. With advancements in computer vision and deep learning, it is now possible to detect human emotions from images. Here we will classify human facial expressions to filter and map corresponding emojis or avatars. Emojifier is software which deals with the classification of facial expressions, text and speech into Emojis or Avatars.
Detection and Classification of Acute Lymphocytic Leukemia
Acute Leukemia is a life - threatening disease common both in children and adults that can lead to death if left un treated. Acute Lymphoblastic Leukemia (ALL) spreads out in children’s bodies rapidly and takes the life within a few weeks. To diagnose ALL, the hematologists perform blood and bone marrow examination. Manual blood testing techniques that have been used since long time are often slow and come out with the less accurate diagnosis. This work improves the diagnosis of ALL with a computer –aided system, which yields accurate result by using image processing and deep learning techniques. This research proposed a method for the classification of ALL into its sub types and reactive bone marrow (normal) in stained bone marrow images. Classification of ALL into its subtypes and reactive bone marrow (normal) in stained bone marrow images. A robust segmentation and deep learning techniques with the convolutional neural network are used to train the model on the bone marrow images to achieve accurate classification results. Experimental results reveal that the proposed method achieved more than 96% accuracy having four different classes. The obtained results exhibit that the proposed approach could be used as a tool to diagnose. Acute Lymphoblastic Leukemia and its sub-types that will definitely assist pathologists.
Determination of Generator Maintenance Level on Motor Tanker Using Fuzzy Logic
The electricity necessity on the motor tanker are supported by generators with a capacity of 3x1,225 MVA. This generator is used to drive the load of the main engine, pump, cargo plant, and steering system. The problem is how to know the condition of generator performance to be standardized because the generator operation must be available to use to support the ship operation. Consequently, if the generator does not work optimally, the ship operation will be hampered and can be detrimental to the company’s operation. The solution to this problem is the need to carry out appropriate and standardized maintenance. This study focuses on determining the maintenance level for a 3x1,225 MVA ship generator. The model is specified as a scheme of the generator maintenance system. The parameters set include: vibration, insulation resistance, power, and fuel consumption. Fuzzy logic is used to define maintenance actions: corrective, planned, and predictive. The object of this investigation is on MT Salmon Mustafa. The results of this research are that fuzzy logic can be used to determine the level of maintenance actions. The average percentage of the error or uncertainty level measured both by four input variables and by Fuzzy logic consisting of three conditions is 6.9952%. The truth level of the calculation results is 93.0048%. This indicates that the calculation results are sufficiently accurate and reliable.
Rank-based Ensemble of CNN Models for Breast Cancer Detection in Histopathology Images
People today suffer from a variety of diseases as a Breast cancer is one of the most common types of cancer in women worldwide. Breast cancer can be invasive or non-invasive. Invasive breast cancer can spread into surrounding tissues also in distant organs whereas non-invasive breast cancer spread surrounding the milk ducts or lobules of the mammary glands in the breast. However early detection followed by the diagnosis can not only reduce the treatment cost but also increase the chance of survival. One of the most effective methods for the detection of tumor malignancies is breast histopathology image analysis. However, manual image analysis for breast histopathology is subjective, time-consuming, and prone to human error. Convolution neural network (CNN) based models have produced encouraging results for the classification of breast histopathology imaging. In this work, we will combine the results of three transfer learning experiments to create a model utilizing a novel rank-based ensemble method rather than depending on a single CNN model, namely GoogleNet, VGG16 and MobileNetV3 Small. Here, it is suggested that the 2-class classification problem for breast histopathology pictures can be handled using the Gompertz function. This strategy yields superior classification results in contrast to publicly available standard datasets such as BreakHis and ICIAR-2018, state-of-the-art methodologies.
Multilingual Repository on (DSpace): A Study of Open Archives Initiative and Protocol for Metadata Harvesting (OAI-PMH)
The study discusses the use of multilingual institutional repositories in library and information science to provide access to scholarly and cultural content in multiple languages. The use of information and communication technology (ICT) has revolutionized library services, and the use of institutional repositories has added a new dimension to access scholarly works. The study shows the process of creating a multilingual repository to promote diversity, inclusivity, and accessibility in scholarly communication. And to support the preservation of cultural heritage, enhance discoverability of content, promote interoperability with other repositories, and support research and learning. The study features the OAI-PMH protocol facilitates standardized dissemination of metadata to make scattered information resources discoverable via A multilingual institutional repository which can increase accessibility, international outreach, and cultural exchange.
NUTRACEUTICALS : A NEW ERA OF MEDICATION
Abstract:- One of the most important healthcare challenges in the world today is neurological disorders.Nutraceuticals are substances derived from food that are thought to be good for human health. It is crucial for controlling brain physiology, preventing neurodegeneration, and maintaining cognitive function. Given how commonly used they are for enhancing health status, nutraceuticals have recently attracted the attention of numerous scientists. especially for the avoidance or minimization of side effects in the prevention and treatment of various neurological diseases or disorders.A number of studies on functional foods have been started recently in an effort to develop foods with higher therapeutic activity and fewer adverse effects. As a result, terminology like "pharmafoods," "medifoods," "vitafoods," and "medicinal foods" have influenced the definition of the phrase "functional foods." With a focus on clinical studies, the current study aimed to assemble all popular (major) nutraceuticals used to treat various neurodegenerative diseases (NDDs), such as Parkinson's disease (PD), Alzheimer's disease (AD), and Huntington's disease (HD). Nutraceuticals have been suggested as a preventative treatment for neurological treatments as a result of a relationship between dietary habits and lifestyle and neurodegeneration. Nutraceuticals have exhibited a variety of neuroprotective qualities, including the ability to reduce inflammation, oxidative stress, intracellular calcium excess, and mitochondrial dysfunction, acting at several biochemical and metabolic levels. These food-based methods are thought to target several pathways in a slow, more physiological way without having a significant negative impact.The most prevalent neurological movement condition in the world, Parkinson's disease (PD), is currently treated with pharmacological methods that are mostly symptomatic and frequently have unfavourable side effects.Nutraceuticals are substances obtained from whole-food sources that have therapeutic value. Their development has made it possible to use alternative methods to treat neurodegenerative disorders like Parkinson's disease. We will look at some of the significant efforts made to better understand the role of nutraceuticals in PD in this study. This study will help regular people choose better nutritional supplements, combined with traditional neuroprotective medications and altered lifestyle habits, to battle various NDDs.
DIDR - Design and Implementation of a Dust Remover from a Conventional Blackboard Duster to Prevent Respiratory Problems
The idea behind the project is to create a device that can be used to dust a blackboard duster. In other words, ingesting a piece of black blackboard chalk won't kill you, but breathing in the dust over an extended period of time might cause or worsen respiratory issues. Thus, it is essential to have the capability of automated cleaning the duster as well as the capability of collecting the dust in order to prevent the release of airborne dust that poses health risks. The principles of "product design and development" have been used in the creation of this product. A machine with greater performance was required due to the advancement of technology in order to meet market and human demands. Because of its potential usage, this project was selected to simplify human labour and reduce the need for human power. A solution that will make it easier to clean and help with appropriate disposal of chalk dust has been created as part of the endeavour to eradicate the dust.
Wireless Caller ID Display for Security and Driver Safety Purpose using Bluetooth and Arduino
Modern technological advancements such as Android smartphones and smart driving are rapidly altering people's lives. The short-wavelength radio waves used by Bluetooth technology, which promises to communicate data wirelessly across short distances, are supplying the essential technologies for ease, intelligence, and controllability. the creation of an ANDROID application that communicates with the vehicle's hardware and uses a Bluetooth module to show caller ID, location, and message information. In todays busy world, everyone is always pressed for time, many critical tasks are completed over the phone, urgent communications are shared over the phone, and there are many traffic issues to contend with. Receiving calls and checking messages wherever and at any time was not feasible at that time. We thus need technology to simplify our lives for this reason. The existing approach has the drawback that individuals might experience significant life issues while using phones, which also breaches traffic laws. All of these issues are resolved by our technology, the Automatic Notifier for Vehicle. Your calls, texts, and maps will be shown on the dashboard of a smart car at your convenience. We will only show your calls and texts since using a phone or any other gadget while driving is dangerous. Using this method when operating the automobiles is quite helpful. In emergency situations, it provides pinpoint precision when pinpointing a place on the LCD display.