Volume 10 | Issue 12

 Abstract

The present invention relates to a semi-solid composition in the form of cleansing beads comprising ?-glucan, omega 3 and vitamin C for use in skin hygiene processes, wherein the brittle material of the beads is ruptured by force by an individual performing a skin hygiene process so that the fragile substance can store the active ingredient until the active ingredient is immediately released from the storage state. The ?-glucan and omega 3 are obtained from barley seed and flaxseed. Production of skin cleansers for improving the skin contour by restoring youthful mechanical properties of the skin. More particularly, it relates to semi-solid composition in the form of cleansing beads comprising ?-glucan, omega 3 and vitamin C for use in skin hygiene processes.

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There are so many reasons for pain, the most common cause of joint pain often affecting the middle age and older age people is sandhigatvata. The main symptoms of sandhigatvata are pain, stiffness, swelling, and crepitus. It is mainly occurs due to the etiological factors which are classified as Dhatukshyjanya and Margavrodhjanya. Aging and obesity are the major risk factor of increased prevalence as the occurance of osteoarthiritis. In present study 46 yr female having 88kg complaining of Janusandhishool, Kriyakashtata, Shoth, Malavshtambh was diagnosed as Upstambit Janusandhigatvata. The Ayurvedic drug combination which include Trifala guggul, Bhallatakasav, Medohar guggul, along with Panchakarma Procedure which includes Lekhan Basti, Taildhara of Chandanbala Lakshadi Tail, etc was given for 15 days to manage Upstambhit Janusandhigatvata

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Janusandhigatvata, Osteoarthritis, Lekhan basti,sthoulya

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Caste system is a special phenomenon seen only in India .The racial discrimination started in India when the Aryans and the former inhabitants started living together as one community. Caste system in Kerala has not existed anywhere else as the Arya Brahmins invaded and gained all the power here, they created the caste system to keep the society completely united under their leadership. As the Brahmins gained spiritual ascendancy and increased influence in the social and political arenas Chaturvarnnya became an inviolable custom. The castes from Nair to Namboothiri treated as privileged class and they exploit all the castes below them A political section emerged here to support the Brahmins and protected the social and economic conditions of that time.

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Casteism, Untouchability, pollution, Community

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Skin disease, one of the deadliest types of cancer is becoming more lethal as fewer people become aware of its symptoms and how to prevent it. The purpose of this research is to identify and classify using machine learning and image processing techniques to treat various types of skin cancer. In this effort, we created a pre-processing image. We modified the dataset, reduced the size of the images, and removed hairs from them in order to meet the requirements of each model. Using pre-trained ImageNet weights and tweaked convolution neural networks, the EfficientNet B0 skin ISIC dataset was trained.

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 Keywords

Disease Detection, Image Processing ,YOLOR, EfficientNet B0, Quantification

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Unusual human activity detection in crowded scenes. Suspicious behavior is hazardous in public settings and can result in significant casualties. Systems that detect motion or pedestrians have been developed using video frame collection, however even in real time, those systems lack the intelligence to spot unusual activity. To quickly and effectively manage a scamper issue before any casualties occur, it is necessary to spot scamper situations in real time via video monitoring. The proposed system focuses on identifying suspicious activity and aims to provide a technique that makes use of computer vision to detect suspicious behavior automatically. The system in this instance makes use of the OpenCV library to instantly classify various activities. The examination of the motion regularly shifts the location between two locations has been represented by a motion influence map. Utilizing CNN and YOLO algorithm for real-time object detection.

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Unusual crowd activity detection, OpenCV, CNN, YOLO

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This study attempts to give the user the machine's operational condition while simultaneously detecting flaws or damage in big industrial machinery in real-time. However, due to the complexity of real-world systems and the clear presence of nonlinear elements, it is highly challenging to diagnose a machine malfunction using standard approaches based on mathematical models. A computer-aided diagnostic system with artificial intelligence (CADS-AI), which aids in early fault diagnosis, can resolve this issue. To assure that the CADS solution can be trusted, a machine failure detection system based on sound patterns is created in this study. The YAMNet Model classified the sounds as normal or abnormal with an accuracy of 78.31%. By using window inference on the audio file with a mean score, the predicted output was decoded. By using real-time audio input or uploading machine sounds to the system, the proposed solution can be inferred.

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Pattern Recognition, Machine Learning, Machine Fault Diagnosis, Audio Processing, Acoustic emission signals

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Keywords: Herbal lipstick, Anthocyanin, Beta vulguris, Formulation, Evaluation.

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Empowerment, Women Entrepreneur, Independent, Social Barriers, Environment, Gender

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Brain tumor is the growth of abnormal cells in brain some of which may leads to cancer. The usual method to detect brain tumor is Magnetic Resonance Imaging (MRI) scans. From the MRI images information about the abnormal tissue growth in the brain is identified. In various research papers, the detection of brain tumor is done by applying Machine Learning and Deep Learning algorithms. When these algorithms are applied on the MRI images the prediction of brain tumor is done very fast and a higher accuracy helps in providing the treatment to the patients. These Prediction also helps the radiologist in making quick decisions. In the proposed work, a self-defined Convolution Neural Network (CNN) is applied in detecting the presence of brain tumor and their performance is analyzed Efficient Network is one of CNN models that proposes high accuracy and less computational. Accordingly, this study suggested using the Efficient Network architecture to classify the types of glioma, meningioma, and pituitary brain tumours. Efficient Network has eight levels of category, which are from EfficientNet-B0 to EfficientNet-B7. This study obtains accuracy for best results in EfficientNet-B3 which achieved a accuracy of 97.34%.

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Image classification, Brain Tumor, EfficientNet

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We are surrounded by a world of electronics and fasting technologies and hence IoT is presently an emerging technology worldwide. The Internet of Things is a network of physical devices like (vehicles, home applications, and other objects) embedded with such devices (electronics, software, sensors, actuators, and network connectivity) that allows us to enable these objects/devices to connect and exchange data. Through internet they can be inter operated with each other. With the introduction of 5G wireless communication, the world of the internet has changed. 5G wireless technology helps to deliver higher multi-Gbps peak data speeds, more reliable, ultra-low latency, increased availability, huge massive network capacity, and more similar user advantages for more users. Higher performance and better efficiency provides better user experiences and connecting new industries through 5G. In this study, we provide a vision of IoT which will be the driving force behind the huge massive digital revolution in the future. The communication technologies and the architecture of 5G in IoT systems have been discussed in detail. In addition, we also indicated the profound challenges of current standard communication technologies in IoT and future research directions of IoT.

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IoT, 5G, 4G, Wifi, LoraWan, Bluetooth, Wifi Direct, Zigbee, architecture

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Two biocontrol agent viz., Trichoderma viride and Trichoderma harzianum were evaluated to test the antagonism against Fusarium oxysporum under in vitro conditions. All the two biocontrol agents have the potential of parasitizing the growth of Fusarium oxysporum in vitro. For control of seed-borne infection of F.oxysporum the best results were obtained from biological agent Trichoderma viride. Maximum control of F.oxysporum incidence (71.15%) and infected seedling (85.0%) was obtained when T.viride was applied as 80ml concentration which was followed by 20ml concentration. Control of pathogen incidence (42.3%), infected seedlings (60%) and seed germination (76.25%) was low in 120ml concentration. In F.oxysporum infected seeds, the maximum control for pathogen incidence 56% to 80% and infected seedling 47.61% to 92.86% was observed in 20ml-240ml dilutions of T.harzianum.

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Bio-efficacy, Fusarium oxysporum, lentil, Trichoderma, Wilt

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Keywords: plant diseases and pests; classification; detection; forecasting; precision farming; machine learning; smart farming.

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The technology of Internet of Things (IoT) and cloud has exposed devices to vulnerabilities. As they are distributed, the different devices communicate real time information to open, private or hybrid clouds, with the possibility of collecting, storing, and analyzing in new forms. In the healthcare context, the increased deployment of IoT devices makes patient information a subject to malicious attacks depending on the security and privacy of the IoT devices. While several researchers have explored such security challenges and open problems in IoT, there is an unfortunate lack of a systematic study of the security challenges in the IoT for e-Health on clouds. In this paper, we aim at bridging this gap by conducting a thorough analysis of IoT security Vulnerability. We present then security challenges in the cloud for e-Health domain and recent proposed solutions. We also provide a proposition of an IoT system in the cloud.

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IoT; CLOUD; EHEALTH; SECURITY.

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As we go from the original text to the translated text, we can understand that the translation undergoes a subjective interpretation by the translator. This leads to a(n) (in)voluntary manipulation of certain aspects of a literary work. This article aims to study the manipulation of self-image in the translation of Amrita Pritam's Rasidi ticket.

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Identity, translation, manipulation

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Face recognition, OpenCV, Deep Learning, VGG

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This project presents the prediction of traffic-violations using machine learning, more specifically, when most likely a traffic- violation may happen. Also, what are the contributing factors that may cause more damages (e.g., personal injury, property damage, etc.) are discussed in this work. The national database for trafficviolation was considered for the mining and analyzed results indicated that a few specific times are probable for traffic-violations. Moreover, most accidents happened on specific days and times. The findings of this work could help prevent some trafficviolations or reduce the chance of occurrence. These results can be used to increase cautions and traffic-safety tips. This work presents an in-depth analysis of road and traffic violations pattern using Data Analytics methods, aimed at improving road and traffic management, government planning and decision making. The study identified the road and traffic current management practice as basis of the design development and implementation of the road and traffic management system. The application managed all the road and traffic violation that will produce recorded set for analysis, which carried out from over of five years. Through data cleansing a total of twenty thousand six hundred forty record set was derived. It is important to find use of this record set, build analysis models, and use interactive tools to produce predictive data, understand the relevance, trends, and driving behaviors from the road and traffic violations data in terms of the following predictors: gender of the violator, vehicle owner address, location of violation, month and time the violation was committed and traffic enforcer who issued the citation. The study was able to establish a data analysis model by using a powerful classification and random forest which was executed using an open source application named PyCharm. Finally, the developed application was evaluated by Python.

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Machine Learning, Traffic Violation,Data Analysis

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Video in the national defense, education, monitoring, entertainment and other fields have been widely used, so data security on the internet cannot be ignored. Video encryption protects the original video information and improves the security of video information. Researchers have done a lot of research on video encryption and put forward a lot of video encryption methods. Video encryption methods are mainly divided into complete encryption and partial encryption algorithm. In order to improve the generalization performance of video encryption and reduce the amount of data in video encryption, this paper proposes a video encryption on regions of interest (ROI) method based on Faster R-CNN by combining machine learning with information security. The method trains a Faster R-CNN model using the ROI dataset firstly, and then uses the model to extract ROI in the video. Different encryption algorithms are used to encrypt ROI and non-ROI in the video respectively. To overcome the shortcomings of encryption algorithms that can only be used for a specific coded video, a special video encryption method is proposed to encrypt the video with different video coding structure and has better generalization performance. Compared with the encryption method in the video coding process, this method considers the content information of the video fully and has better performance. It can be concluded through experiments that the encryption method in this paper has the characteristics of higher security and less calculation.

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Video Encryption, Image Processing, Deep Learning

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Identification of faults via computer-based techniques is a growing trend in all fields these days. Two main characteristics of Bone Fracture Detection are fast identification and high precision which is described by highly sensitive device by incorporating advanced techniques and effective resource usage. The effect of undue external stress above the limits of what the bone may tolerate is a crack in a bone or bone fracture. Canny Edge detection is an image processing technique that identifies bone fracture by utilizing automatic fracture detection efficiently and overcomes the question of noise reduction. There are many methodologies accessible in today's world for edge detection, such as Sobel, Canny, Log, Prewitt, and Robert. These processes, though, are hampered by crucial limitations such as a lack of capacity to conduct multi resolution research, culminating in the failure to identify small information during the analysis. The other major drawback of the techniques is that they operate well with high resolution and high-quality pictures, but because of their intrinsic lack of ability to differentiate between edges and noise elements, they do not work well with blurry images. The approach being suggested uses the CNN algorithm to solve these issues. The findings of the simulations carried out suggest that the approach proposed is a far more effective system for conducting edge detection on aggregate scales. The suggested system has also shown to be sufficiently resilient to retrieve the required details and do the necessary analysis on key portions of the images and manage noise in a much better way than the edge detectors currently usable.

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Machine Learning, Image processing using X-ray images, Canny Edge Detection, SVM algorithm.

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Machine learning is an important decision support tool for crop yield prediction, including supporting decisions on what crops to grow and what to do during the growing season of the crops. Several machine learning algorithms have been applied to support crop yield prediction research. In this work, we performed a Systematic Literature Review (SLR) to extract and synthesize the algorithms and features that have been used in crop yield prediction studies. Based on our search criteria, we retrieved relevant studies from six electronic databases, of which we have selected five studies for further analysis using inclusion and exclusion criteria. We investigated these selected studies carefully, analyzed the methods and features used, and provided suggestions for further research. According to our analysis, the most used features are temperature, rainfall, and soil type, and the most applied algorithm is machine learning in these models. After this observation based on the analysis of machine learning-based algorithm, to recognize machine learning, we conducted additional researches in databases on crop yields. To find studies that used machine learning, we also searched crop yield datasets. This further study reveals that the Decision Tree Method is the most frequently employed machine learning algorithm in these studies.

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Machine learning, Crop Yield Prediction, Decision tree algorithm

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Handwriting is one of the distinguishing characteristics that distinguishes a person's identity, and it is a method of identifying the writer's physical characteristics. It displays a person's genuine personality, including their actions, emotional outbursts, sense of self, rage, creativity, honesty, phobias, and a range of other traits. In this paper a multi-layered approach is proposed for analyzing personality traits by identifying the type of handwriting and classify the personality of the individual human being using deep learning models such as Resnet 34 and YOLO v5 model.

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Handwriting Analysis, Personality Classification, Feature, Deep Learning.

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