JOURNAL OF IMAGE PROCESSING & PATTERN RECOGNITION PROGRESS
ISSN: 2394-1995 Scientific Journal Impact Factor (SJIF): 6.139 Index Copernicus (ICV: 57.52) Journal DOI no.: 10.37591/JoIPPRP
STM JOURNALS ( Consortium eLearning Network Pvt Ltd)
CNN-based Approach for Efficient Bell Pepper Leaf Disease Recognition
Convolutional Neural Networks (CNNs) have achieved remarkable results in the detection of diseased plant leaves, providing highly accurate predictions. This project offers a comprehensive examination of existing systems designed for the detection of plant-based diseases. Using CNN trained on a dataset of bell pepper plant images, various simulation approaches for neurons and layers were employed. Plant diseases have significant impacts on agricultural productivity, leading to economic losses, reduced crop quality, and decreased yield. Consequently, the timely detection of plant diseases in large crop fields has garnered increased attention. By obtaining reliable data on plant health and accurately identifying diseases, effective management strategies can be implemented to mitigate the spread of diseases. Therefore, our model plays a crucial role in the classification of healthy and diseased bell pepper plant leaves. In this context, it is highly recommended to promptly remove any infected plants to prevent the spread of disease throughout the entire garden. Taking immediate action upon identifying issues with the pepper crop helps minimize further contamination and ensures better overall crop health. (By using Convolutional neural networks (CNN) we obtained maximum accuracy of 99.99%.) Convolutional Neural Networks (CNNs) have achieved remarkable results in the detection of diseased plant leaves, providing highly accurate predictions. This project offers a comprehensive examination of existing systems designed for the detection of plant-based diseases. Using CNN trained on a dataset of bell pepper plant images, various simulation approaches for neurons and layers were employed. Plant diseases have significant impacts on agricultural productivity, leading to economic losses, reduced crop quality, and decreased yield. Consequently, the timely detection of plant diseases in large crop fields has garnered increased attention. By obtaining reliable data on plant health and accurately identifying diseases, effective management strategies can be implemented to mitigate the spread of diseases. Therefore, our model plays a crucial role in the classification of healthy and diseased bell pepper plant leaves. In this context, it is highly recommended to promptly remove any infected plants to prevent the spread of disease throughout the entire garden. Taking immediate action upon identifying issues with the pepper crop helps minimize further contamination and ensures better overall crop health. (By using Convolutional neural networks (CNN) we obtained maximum accuracy of 99.99%).
Copyright (c) 2023 JOURNAL OF IMAGE PROCESSING & PATTERN RECOGNITION PROGRESS
Object’s Area Measurement using Image Processing
Object measurement holds substantial significance across a wide spectrum of domains, spanning from quality control in manufacturing to applications in healthcare and environmental monitoring. In this study, a method is suggested to detect the object and calculate the area of the given object in real time using MATLAB. Also, comparing the result obtained with the known area and seeing how accurate the area calculated is. The proposed method for detecting objects and calculating the area is reliable and accurate compared to the area calculated is verified with the manual area calculation readings. A methods is suggested without any constraint of the height from which the object is captured. In this method, the object whose area is to be measured is kept on a white backgroung of known area, the backgroud edges are etracted, the image is thresholded such that the white background has pixel value 255 and the object to be measured has pixel value 0, the image is filled and thus the pixels values initially having 0 intensity now possess intensity level of 255, further, the pixel sum is calculated to find out the calibration factor (area per pixel); lastly, the object is extracted from background and the pixel sum of object is multiplied by the calibration factor to obtain its area. The proposed method was evaluated on the two sets of images: (1) rectangular object; and (2) circular object. The authors respond to the crucial requirement for accurate object measurement by presenting a systematic approach that includes image pre-processing, boundary detection, and calibration factor computation. The proposed methodology is rigorously validated through a series of empirical experiments, revealing its efficacy in accurately quantifying objects with an error of 0.02 cm2. This work constitutes a substantial contribution to the field of computer vision, offering a practical and versatile solution for object measurement tasks with broad applications.
Sign Language Recognition Using Deep Learning
People with hearing/speech disabilities use sign language to communicate their thoughts and feelings, but non-disabled people often find it hard to comprehend these hand gestures since they do not know sign language. This project aims to create a system that uses MediaPipe, LSTM and Keras for sign language recognition. The webcam will capture video footage of people performing sign language gestures, then MediaPipe will extract and identify the hand landmarks and motion from the video. After that, LSTM, a sequence modelling technique, is used to process the extracted features. Lastly, a deep learning LSTM model implemented in Keras is trained to recognize the different sign language gestures. This system can potentially aid those with hearing impairments in communicating with others quickly and easily.
Utilizing ML for Hand Gesture Recognition
Hand gestures are an instinctive and common method of communication during human interactions, representing a form of non-verbal expression. Gesture recognition technology aims to interpret and classify meaningful movements performed by human hands. The motivation behind its development is to revolutionize human-computer interaction, addressing drawbacks found in current systems. The study presents a novel algorithm that eliminates the requirement for image background filtering. This algorithm proves versatile, capable of recognizing various hand gestures and accurately determining the count of raised fingers. By focusing on the hand movements within the region of interest, the proposed system enhances the efficiency and precision of gesture recognition. Gesture recognition has extensive applications, spanning from virtual and augmented reality to healthcare and industrial automation. Through this technology, users can engage more naturally and intuitively with digital environments, while healthcare professionals can provide real-time feedback during rehabilitation exercises. In industrial settings, gesture recognition enables the control of machines and robots, enhancing productivity and reducing manual labor. Advancements in computer vision, machine learning (ML), and sensor technology have greatly improved the accuracy and effectiveness of gesture recognition systems. With further research and development, gesture recognition is poised to revolutionize human-computer interactions across diverse fields, enriching user experiences and optimizing task performance. By continuously refining algorithms and incorporating innovative techniques, gesture recognition is poised to become an integral part of future human-machine interactions.
A Survey on Hateful Memes Detection Techniques
With the advent of social media, meme, which is a combination of image along with some text are becoming a popular mode of communication for spreading jokes or sarcasm which may also include some toxicity like racial, sexist and many other types of hateful content. Identifying hate in text is a comparable easy task as it is a unimodal task; however, identifying hate in memes is a multimodal task and requires VL (Visual-Linguistic) Models. Recently, this issue has gathered much attention from researchers around the world. Researchers have worked on various Visual-Linguistic models to identify hateful memes. In this study, we have made a comprehensive study of various techniques to identify hateful memes and summarized these techniques based on various evaluation metrices like AUC, F1 score and accuracy. Finally, we have highlighted various challenges and future work in identifying multimodal hate in online memes.
A Brief Survey on Content-based Fake News Detection
The increase in spread of fake news causes destruction of democracy and public confidence, which has boosted the demand for reliable fake news identification dramatically. Recent developments in this area have opened up innovative approaches for identifying fake news by looking at how it travels on social media. However, in order to spot false news early, one has no information about news distribution because at an early-stage, fake news can only be created and later dissemination happens. Also, the speed and amount at which news is created and propagated online is destructive in nature. As a result, there is an urgent need to create methods for detecting false news just based on news content and eliminate the danger before it reaches the mass of people. This study is divided into five sections. We have an introduction where we describe the topic and the motivations behind this survey. Fundamental Theories is the second section where we discuss the current theories that are used to detect fake news. In the third section, we briefly touched upon the advantages of utilizing content-based detection. In the fourth section, we conducted a survey on content-based fake news detection and listed the advantages and disadvantages of the available techniques. We closed the study with a discussion of some potential topics to investigate further.
Hand Gesture Recognition using Machine Learning and OpenCV
People who are deaf or dumb use sign language to communicate with one another and within their own communities. The process of computer recognition of sign language begins with learning sign gestures and progresses until text or speech is generated. Static and dynamic sign gestures are the two categories. Although static gesture recognition is simpler to use than dynamic gesture recognition, both gesture recognition systems are essential to the survival of the human species. The processes for sign language recognition are described in this survey. Data gathering, preprocessing, transformation, feature extraction, classification, and results are all examined. Additionally, there were some suggestions for developing this area of work.
FRUDON: A Fruit Donation Platform with Convolutional Neural Network Based Fruit Shelf-life Prediction for Mitigating Starvation
According to the United Nations Global Hunger Index, India's hunger situation is portrayed as alarming, as it ranks 101st out of 116 nations. The government predicts that 69% of children under the age of 5 years would die from malnutrition and hunger by the end of 2023, as starvation deaths have grown widespread in the nation. To tackle this concern, the mitigation of food wastage has been recognized as a prospective approach. This method introduces a two-part system, comprising a fruit shelf-life predictor and a mobile/web application that serves as a platform linking donors and NGOs. The fruit shelf-life predictor employs deep learning algorithms within a convolutional neural network (CNN) framework to examine and categorize fruit images, delivering an assessment of the remaining shelf life in terms of safety, nutrition, and taste. The mobile/web application serves as a platform for users to input fruit images, receive shelf-life predictions, and connect with NGOs for donating surplus fruits to reduce wastage. Similar to the human brain system, the CNN architecture employed in the fruit shelf-life prediction can recognize and arrange different elements in fruit photos. This system aims to leverage technology to reduce food wastage and combat hunger in India by providing an efficient and user-friendly platform for donors and NGOs to connect and share surplus fruits.
Enhancement of Digital Mammograms Using Intuitionistic Fuzzy Entropy Function
A novel intuitionistic fuzzy entropy-based algorithm is developed for increasing the contrast of digital mammograms. The contrast enhancement helps in the early detection of masses and microcalcification in tissues thus paving the way for the auxiliary diagnosis of breast cancer. The current techniques for the enhancement of digital mammograms do not consider the uncertainty in pixel intensities of mammograms. The proposed technique aims at enhancing the mammograms using intuitionistic fuzzy entropy. To enhance contrast, Contrast Limited Adaptive Histogram Equalization (CLAHE) is implemented on small regions. The quantitative measures such as Discrete Entropy, Absolute Mean Brightness Error, Absolute Mean Brightness Coefficient, and Contrast Improvement Index and qualitative measure like visual quality are shown to be better than those of the state-of-the-art techniques. An application of the Hanman classifier on the enhanced images gives better classification accuracy than that of classifiers like support vector machine and convolutional neural network.
Contrast limited adaptive histogram equalization
An Accuracy Improvement in Facial Verification through Siamese Neural Networks
Due to the ever-growing number of digital photos on the Internet, the task of automatically analysing images to extract semantic content has become quite important in recent years. In order to successfully organize the photographs, image indexing and image retrieval strategies need top-notch, effective image analysis and pattern recognition algorithms that can extract relevant semantic data. Face recognition plays a critical role in a variety of real-world applications, including AI-driven access control, interactions, immigration, education, and retail. All these industries have successfully used machine learning. However, the applications can be computationally expensive and may be challenging to perform when limited data is available. This is where the grey area exists. In this study, we provide a Siamese neural network-based technique for facial verification. To categorize the similarity between two input faces, a neural network is used. The Siamese neural network was successfully used in earlier experiments to recognize characters. This study will attempt to expand on the research into facial recognition software. The network can predict fresh data as well as new classes from unknown distributions once it has been tuned. In this study, convolutional neural networks were employed to exceed predictions made using previously developed methods.
Land Verification System Using Artificial Intelligence
Our analysis in “Land Verification System Using AI”, runtime security and secure key disposal procedures carried out using sandboxing method eliminate risks linked to private keys. Depending on the type of land ownership, the framework's usefulness varies, and the discussion's end objective takes care of future tasks. A useful and practical solution for land vault confirmation using a biometric-based enhanced signature scheme is the end result of this investigation. The management and organization of land properties is one of the most well-known and frequent issues faced by non-industrial countries like India and Japan due to poor land proprietorship confirmation practices used by the government in the domain of land vault division. This gives rise to situations like land fraud, trespassing, abuse, and neglect. This research study outlines the conditions of a biometric-based check system for land executives that ensures the validity of land ownership, the veracity of land data, and the non-disavowal of land trades. The aim of the research is to use a staff member's unique mark to create biometrically based random keys that can be used for securely marking land library reports and then checking that mark in a safe manner. To ensure the security and robustness of the system, some key strategies should take place inside a separate area like sandbox weather.
Seat-belt Detection Using Image Processing in MATLAB
In this study, a method is suggested to detect seat belts in a monitoring image of a moving vehicle. The method relies on several image processing techniques such as edge detection, closing operation, and Hough transform, to precisely identify the seat belts. The proposed method for detecting seat belts is more dependable and sturdier compared to the methods that previously depended on driver's visual monitoring or basic belt detection systems. The initial step of the algorithm involves detecting the lines present in the input image, achieved through the utilization of the Hough transform. Then, the detected lines are filtered based on their orientation and location, allowing only the lines that are likely to represent seat belts to be selected. The closing operation is then used to connect the selected lines and form a complete seat belt. Finally, the algorithm verifies the seat belt by checking the continuity and width of the detected line. The proposed method was evaluated on a dataset of monitoring images containing various driving scenarios, and the results show high accuracy in seat belt detection. The algorithm was able to detect seat belts with a high true positive rate demonstrating its effectiveness in real-world scenarios. Overall, the proposed method provides a reliable and accurate approach to seat belt detection, which can be integrated into existing monitoring systems in vehicles to improve safety. This approach underscores the potential of technology to improve road safety and mitigate the likelihood of accidents, as demonstrated through the application of image processing techniques.
Real-time Sign Language Detection Using Computer Vision and Machine Learning Techniques
Communication plays an important role in our day-to-day lives as it assists people in sharing, and connecting to each other. Despite knowing the significance of the same, some deaf-mute pupils who are not able to communicate freely. These groups of people use sign language for sharing their thoughts. It could be a tough task to understand sign language for a group of humans who are not deaf-mute. We developed a system that makes use of American Sign Language as the solution to the identified problem. The strategy involves implementing the module which detects pre-defined American sign language through hand gestures. For detection of movement of gestures, we use basic hardware components like a camera and OpenCV, media pipelined images and the image is passed to the CNN model for identification followed by word formation. The principal results of using this system will aid deaf-mute humans to share their ideas fruitfully and communicate with normal individuals proficiently and without hesitation.
Utilizing Machine Learning to Recognize Faces: A Review
These days, one of the top computer vision technologies is facial recognition. The task of face recognition in computer vision, illumination position, and facial expression is always quite challenging. Target objects are tracked using face recognition in real-time video images captured with a video camera. To put it simply, it is a system program used to recognize a person automatically from a still image or video frame. In this article, we used different algorithms for face recognition, like the Viola-Jones algorithm, PCA algorithm, KLT algorithm, and EmguCV. There are various techniques through which we can perform the above techniques. The present work here depicts the details of various technologies which have been researched and discovered. Let us take an example of designing colour filters that we use in cameras to make them more colorimetric.
Review of Deep Learning in Medical Imaging
In the past few years, computer-aided analysis has fallen quickly behind. Organ categorization and segmentation is a key stage in computer-aided diagnosis. In recent years, there has been a lot of interest in the division of abdominal organs such as the liver, stomach, kidney, pancreas, and bladder from various picture modalities. Radiologists or medical specialists analyze abdominal pictures the majority of the time. Due to subjectivity, intricacy of the picture, substantial variations between interpreters, and fatigue, human specialists' ability to understand images is quite restricted. Following the success of deep learning in real-world applications, it is now offering innovative solutions for medical imaging with high precision and is regarded as a crucial technique for upcoming medical applications. When compared to conventional models, the development of deep convolutional neural networks (CNN) tends to provide superior categorization in medical imaging analysis. Although CNN is the most frequently used architecture in medical picture analysis, this form of analysis also uses supervised machine learning methods, which need a lot of training data. A data set of images related to the abdomen can be used to test new unsupervised learning algorithms and architectures like VAE (Variational auto encoders) and GAN (Generative adversarial networks) that are emerging in the field of deep learning to get a sense of how performance measures related to diagnostic results might improve. This can be done to gain a greater understanding of how to enhance the diagnosis performance.
Copyright (c) 2022 JOURNAL OF IMAGE PROCESSING & PATTERN RECOGNITION PROGRESS
Automated Brinjal Leaf Disease Detection Using Machine Learning: A Sri Lankan Study
The spread of plant diseases has a significant negative impact on agricultural output quality and yield, resulting in significant economic losses. Plant disease automation in agricultural science is our country's main concern due to the economic crisis and the emphasis on organic farming practices. Furthermore, the accuracy and efficiency of recognizing sick leaves have increased due to the expanding usage of smart technology. This study uses machine learning and image processing to create a system that can classify diseases in brinjal leaves with greater accuracy. For comparison with healthy leaves, tobacco mosaic virus, collar rot, bacterial wilt, and cercospora leaf spot were chosen. For this investigation, pictures of 35 leaves from the Tirunelveli Purple Brinjal (Solanum melongina L), including healthy leaves, were taken on-site in Atchuveli, Northern Province of Sri Lanka. First, using the KNN approach, Histogram Oriented Gradients (HOG) and Principal Component Analysis (PCA) based features were used to classify the images, with accuracies of 0.95 and 0.97, respectively. Increased training data is required to achieve high accuracy with these techniques. The suggested system will enable farmers to recognize the appropriate disease. The development of the full system, including the hardware, camera, and classification scheme that will be applied in the field, is the study's ultimate objective. In the future, we plan to design a complete system with automatic detection.
Convolutional Neural Network, K-Nearest Neighbors, Histogram of Oriented Gradients, Principal Component Analysis, brinjal leaves
An Interface for Communication for the Deaf Using Hand Gesture Recognition through Computer Vision and Natural Language Processing
Hand gesture recognition is essential for conquering various challenges and improving human existence. A machine’s capacity to interpret human behaviors and their significance is being used in a large range of applications. Sign language recognition is one area of particular attention. This study will provide a deep analysis of use cases and technologies utilized now for current gesture recognition of hand, recognition of sign language, research done on it and the previous history of the same. The approaches under investigation are logically broken down into the following stages: data gathering, pre-processing of hand data, segmentation of data and features, feature extraction of the refined data, and picture recognition. At each level, several algorithms are explained, and their merits are assessed. We also draw attention to the drawbacks and restrictions of the study of gesture recognition. We also talk about a project on hand gesture recognition using computer vision and natural language processing, which will help the deaf community communicate with people using the idea of artificial sign language to a translator of natural language.
Medical Image Processing Using Data Science: A Review
The purpose of this work is to introduce data science in medical imaging. Both theoretical advancements and real-world applications are covered in this research work. The healthcare industry is distinct from all other industries and is a special sector. It is a top-priority industry that consumes a sizable percentage of the federal budget. In general, only doctors can analyses images, but thanks to technology, we are now able to use principles from physics, arithmetic, statistics, and other fields to process images for medical purposes. The World Health Organization (WHO) considered COVID-19 to be a major distraction for virtually the whole world as a result of its slow escalation in time, particularly for nations with poorer health infrastructure. Deep learning, data analytics, and machine learning are all applications of data science that have been used in the healthcare industry. Examples include the diagnosis of diabetes, classification of the lungs, thyroid detection, ultrasound, X-ray, and mammography, among many other sectors. This fact has spurred a huge number of medical research projects, which have been undertaken. In this study, we first summarize the most recent data science research studies and their applicability to medical image processing. Then, we give a brief overview of data science and the processing and analysis of medical images that arise from it. Finally, it provides overview of technology which has resulted a drastic change in healthcare.
Segmentation and Classification of Hand Gestures for Indian Sign Language with YCbCr and HSV Color Models with Dissimilar Signers Under Different Lighting Conditions using AlexNet
Indian Sign Language (ISL) Recognition System can be implemented by considering the signs as hand gestures and developing a hand gesture recognition system. The impact of illumination changes on the performance of segmentation and classification where different lighting conditions such as normal, bright, dim, and artificial light under different color spaces like RGB, HSV and YCbCr are studied, and performance parameters are analyzed. It is observed that YCbCr color space is effective for complex images with different illumination. Occlusions of the hand with the face put additional constraints on the segmentation process. The effect of the varied skin color of different signers is also subjected to the study in this work. In India/Kerala, the skin color of the people varies drastically. It is difficult to fix the pixel value of the skin color as it affects the segmentation process. It will select other objects with the same color as the skin in the background. These constraints are overcome by selecting large, connected components in the frame as hand and are segmented. After modeling and analysis of the input hand image, gesture classification is done using SVM and AlexNet to recognize the gesture. The accuracy obtained with SVM is 93.8% and with AlexNet is 98.5%.
A Systematic Review: Impact of Preprocessing and Segmentation Methods in Classification of Celiac Disease
In this era of technology, endoscopy emerges as the clinically acceptable imaging modality in the detection and diagnosis of celiac disease. As cases of celiac disease are increasing day by day, so it is necessary to make detection automated. The first step of preprocessing and then image segmentation is considered as the most essential step in research before classifying images, in the last two decades. To process the very large size images, image preprocessing is done, and to divide the image into desirable building blocks, image segmentation is used. As the availability of algorithms is vast, so the analysis of these algorithms might be interesting for researchers. This study compares the results of preprocessing and segmentation methods used. The images are processed using MATLAB and various pictorial results are presented based on image resizing, RGB to gray, edge-based, and ostu thresholding, based on their advantages and disadvantages. The unique aspect of the work is how the two modified filters are employed for segmentation to appropriately assess the intestinal wall. This work compares classification accuracy, sensitivity and specificity with other segmented and nonsegmented images. It is proved that using modified filters, celiac disease can be diagnosed without using a complicated deep learning algorithm or any state-of-art model.
Smart Agriculture System Using IOT and Image Processing
This study presents a new method for identifying and classifying leaf diseases and soil moisture levels. The particular type of leaf disease that affects a plant’s leaf is difficult to distinguish with human eyes. As a result, in order to accurately diagnose leaf diseases, the application of the proposed study attempts to use plant leaf image processing to diagnose illness. India is the global leader in agriculture, and a wide variety of crops are grown here. Agriculture makes up a large portion of the Indian economy, and while there are numerous factors that can affect the quality and quantity of agricultural products, one of them is climate change, which can lead to disease and, ultimately, a decline in both. The purpose of this project is to improve our irrigation system by automating and intelligently controlling it. This project’s main purpose is to use a microcontroller to measure the moisture of agricultural soils in real time and reduce the amount of human labour required by the farmer. The sensor detects the moisture content.
Image Super-resolution Using Super-resolution Convolutional Neural Network
Several studies have been conducted to improve low-resolution photos to a higher resolution version utilizing classic super-resolution methods such as pixel-wise interpolation. While the resolution of images has been rapidly increasing in recent years with the development of high-performance cameras, advanced image compression, and display panels, the demand to generate a high-resolution image from pre-existing low-resolution images is also increasing for rendering on high-resolution displays. Some of these methods involve cropping a large image into small parts and applying superresolution to each one individually. The downside, however, is that in many cases, the cropping lines are distinctly visible, which in turn reduces the quality of the output image. Studies concentrating on integrating CNN-based neural networks into super-resolution are evolving as deep learning technologies advance, to improve speed and minimize memory load on numerous devices. Neural networks can learn an end-to-end mapping between low- and high-resolution images by themselves. The mapping is represented by a deep convolutional neural network (CNN), which takes a lowresolution image as input and produces a high-resolution image as output.
COVID-19 Prediction using X-Ray Images and Deep Learning Model
In the year 2019, the novel corona virus was emerged in China. Since, then every individual has put their own self to fight against this pandemic situation. Till April 14, 2022, the total COVID-19 confirmed cases are 500,186,525 and deaths are 6,190,349 worldwide. RT-PCR and Rapid antigen test are the standards for COVID-19 detection by WHO. These both tests are time consuming and costly and also there are higher possibilities of false results. On this conquest, we decided to implement a CNN model to detect COVID-19 patients from chest X-ray images. Convolutional Neural Networks (CNN) are important in the field of deep learning for diagnosing a variety of diseases, such as Coronary Artery Disease, Alzheimer’s disease, and Parkinson’s disease. Similarly, CNN has a good chance of identifying COVID-19 patients using diagnostic pictures such chest X-rays. The proposed model performs with a 97.84% accuracy and 99.11% precision. Similarly, the Receiver Operating Characteristic (ROC) has the AUC of 97.8% and the F1-score generated is 97.81. All these results can be improved by using different techniques and equally larger dataset for training of model.
A Comparative Analysis on Image Enhancement and Filtering Techniques for Efficient Melanoma Skin Cancer Detection
Skin cancers are becoming more prevalent worldwide due to the shortage of awareness on the symptoms and skin safety measures. The use of computer-aided diagnosis systems for melanoma detection can aid the doctors and technicians. Pre-processing an image plays a crucial role in separating the lesion from the skin and identifying the region of interest (ROI). As a result, in this proposed study of work, a thorough analysis is done on various image enhancement and filtering techniques. Most prominent image enhancing and filtering techniques are reviewed with a detailed result analysis. The analysis demonstrates that by eliminating the undesirable hair particles from the skin lesion, the dull razor algorithm for image improvement works effectively. Then, gaussian image smoothing works well for noise filtering. According to the findings summarised in this research work, the two strategies stated above performed the best, with a 96.25% accuracy rate.
An Automated Facial Mask Detection System for Minimizing COVID-19 using Multi-Task Cascaded CNN Model
An outbreak triggered by a new coronavirus has continued to spread over the globe till today. Nearly every area of development has felt the effects of COVID-19. The state of healthcare is now quite problematic. One of the many preventative steps adopted to limit the spread of this illness is the use of a mask. To limit the spread of COVID-19, we propose a system that identifies persons who are not wearing any face masks in a smart city network that is monitored by CCTV cameras in every public location. It is the cameras’ job to take pictures of people in public locations, and the photos they acquire are sent into a system that looks for anyone without a face mask. Anyone found without a face mask will have their identity reported to the appropriate authorities, who will take the appropriate measures. Authorities are alerted via the city’s network whenever an unmasked individual has been found in the area. On a face mask dataset that includes photos of individuals under different masks, deep learning-based MTCNN architecture is implimented. People with and without face masks were identified with 99.76% accuracy by the trained architecture in previously unreported test data. It is believed that the findings of this research would help many nations throughout the globe combat the spread of this contagious illness.
Home Security Weapon Detection and Face Recognition
In today's world, home automation is very popular. With the help of the Internet of Things, we can control our safety and take precautions if an unknown person arrives with weapons such as knives and firearms (IOT). A thief's intrusion can cause problems for the homeowner. The innovative and insightful use of PC in IOT has resulted in the safety of person and home. The objective is to improve this framework using image processing for object recognition. The machine utilizes cameras at the entryway for the faces known so as to get entry. Furthermore, vibration and entry gate magnet sensors have been installed at the passage point to detect when a robber or killer attempts to rob inside the house. PIR sensors are used to detect human presence. A vibration sensor is also used to provide an alert if a nearby shock is detected. The system permits access only to authorized individual like proprietor or registered person to access the database. The person may be recognized by providing legitimate proof of identity. If it does not recognize the individual, it sends an alarm to the owner, who may then follow the activities via a live stream from the camera. All sensor signals are validated, and the system’s status is regularly updated. If a criminal attempts to break in, the sensor activates, and alert messages are sent to the owner and the police for their protection.
A Review on Breast Cancer Detection and Diagnosis using Machine Learning Techniques
Breast cancer is a complicated illness that is the second leading reason for women mortality due to cancer. A growing body of research suggested that a variety of variables (including genetic and environmental factors) might be linked to the onset and development of cancer in women breast. A major component of breast cancer treatment is the early detection of people with the disease. It initially presents an introduction of the various methods in breast cancer detection and classification with machine learning, followed by an overview of the various deep learning algorithms and other strategies. To provide a comprehensive introduction of the field, we also present a quick summary of the various picture categorization approaches.
Correlating Color Complexity and Mental Health
Mental health has been a cyclic evolution rather than a linear one. This means that the mental health depends on the surroundings, culture and period of time. People have been studying emotions from numerous periods. In spite of this, it is not surprising that we all know quite a bit about them; what is surprising is that the lack of understanding round the necessity of both feelings for healthy functioning. Positive emotions are feelings that we generally find pleasurable to sustain. The Oxford Handbook of Positive Psychology which has an article written by Cohn & Fredrickson in the year 2009 which explains the exact meaning of the feelings and emotions and their positive and negative attributes. On the other hand, negative feelings are those that we generally do not find pleasurable to sustain. Negative feelings are often defined as “as a bitter or unhappy emotion which is raised in individualities to precise a negative effect towards an occasion or person” (Pam, 2013). There are many instances which prove that colors have the potential to impact a variety of aspects of human behavior and it can also affect the mental state of any individual. There is also few research which state that emotional words and colors have a connection, and their findings have revealed that luminousness is repeatedly related to positive emotional words and darkness with negative emotional words. All this helps in stating the fact that colors have a greater depth to them, and this depth can be used to form a connection between mental state of a person and the colors.
A Novel Deep CNN Model for COVID-19 Detection based on Lung CT Images
In recent months, worldwide society has been destroyed by COVID-19 (Coronavirus Disease 2019), a catastrophic contagious illness that has ravaged the whole world. Deep learning (DL) approaches are the most widely used strategies for chest CT scan processing and illness prediction in the medical field. COVID-19 is infectious sickness that mostly influences the lungs also has the potential to be fatal if left untreated in its extreme form. It also affects the lung CT scans of individuals who are impacted. The use of lung CT scans has allowed us to develop a deep convolution neural network (DCNN)-based model that will make it easier to provide a more appropriate COVID-19 detection method throughout this pandemic. The dataset we used for training and testing consisted of 349 COVID-19 and 397 non-COVID-19 lung CT frames, which we collected for these reasons. In addition, the data augmentation approach was adopted, and 1744 CT frames of COVID-19 patients and 1588 CT frames of non-COVID-19 patients were obtained. Of these, we have utilized 80% of lung CT frames for training reasons and just 20% of the lung CT frames for testing purposes. Results of studies suggest that our models are successful under COVID-19 testing conditions. The test findings demonstrate that our method is superior. The experimented results reported that the proposed CNN algorithm attained overall accuracy of 99.79% which is higher than the existing model. With the findings of this study, it is possible to use DL to help in the objective of diagnosis of COVID-19 disease via CT scans of the lungs, which has a promising potential in terms of future research.
Music Recommendation System and Mood Tweak Using Facial Emotion Recognition
A user’s emotions can be detected through their facial expressions, these characteristics can be parsed, and the information can be used to improve the user’s experience. With the computing capabilities of on-board graphic processing, there is a new trend of functionality being considered in the field of emotion detection. The use of Convolutional and Recurring Neural Networks (CNN & RNN) to conduct a comparison on which deep learning can be implemented for emotional recognition. Both neural networks are trained employing a dataset with differing types of emotion classes. The trained models are applied employing a music player supporting one’s facial expressions. Within the proposed system, an emotion-based music player is developed to perform real time mood detection which recommends songs as per the user’s current mood. If the user is not satisfied with his mood, he can change it. This becomes a further feature to the normal music player. Customer satisfaction is a significant benefit of implementing mood detection. The goal of this technique is to evaluate the user's image, anticipate the user's expression, and propose a song that is suited for the detected mood in order to provide the best experience possible.