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Brain Tumour (BT) a mass or a lump or a growth which occurs due to abnormal cell division or unusual growth of cells in the brain tissue. Initially, the two major types of BT are Primary BT and Secondary BT, the tumour that originate from brain is known as Primary BT and it may be cancerous or non-cancerous. The tumour the initiates from other part of the body and spreads to the brain is stated as secondary BT. Diagnosing BT generally involves a multiple investigation method, such as MRI, CT, PET, SPECT as well as the neurological examinations and blood investigations, whereas some of the patients may need biopsies to evaluate the tumour size and stage. Here we use MRI and CT images for BT segmentation whereas these modalities play a major role in diagnosing, treating, planning and monitoring the BT patients. Moreover, the multimodal data can provide a quantitative information's about the tumour size, shape, volume and texture. While segmenting the BT the lack of segmentation methods and the interpretability of the segmented regions are limited. To overcome this, we propose a novel LSTM autoencoder bas NAS method which is used for the extracting the BT features and these features can be fused using Contextual Integration Module (CIM) and segmented using the Segmentation Guided Regulizer (SGR) which helps to overcome the stated issues. Finally, the performance metrices are calculated by comparing with the state-of -the -art methods and our method achieves a best segmenting metrices. [ABSTRACT FROM AUTHOR]

Brain tumor is a condition due to the expansion of abnormal cell growth. Tumors are rare and can take many forms; it is challenging to estimate the survival rate of a patient. These tumors are found using Magnetic Resonance (MRI) which is crucial for locating the tumor region. Moreover, manual identification is an extensive and difficult method to produce false positives. The research communities have adopted computer-aided methods to overcome these limitations. With the advancement of artificial intelligence (AI), brain tumor prediction relies on MR images and deep learning (DL) models in medical imaging. The suggested layered configurations, i.e., layered network model, are proposed to classify and detect brain tumors accurately. The modified CNN is proposed to automatically detect the important features without any supervision and the convolution layer present in the network model enhances the training feasibility. To improve the quality of the images, some essential preprocessing is used in conjunction with image-enhancing methods. Data augmentation is adopted to expand the number of data samples for our suggested model's training. The Dataset is portioned as based on 70% for training and 30% for testing. The findings demonstrate that the proposed model works well than existing models in classification precision, accuracy, recall, and area under the curve. The layered network model beats other CNN models and achieves an overall accuracy of 99% during prediction. In addition, VGG16, hybrid CNN and NADE, CNN, CNN and KELM, deep CNN with data augmentation, CNN-GA, hybrid VGG16-NADE and ResNet+SE approaches are used for comparison. [ABSTRACT FROM AUTHOR]

The contamination of microplastics (MPs) creates a substantial risk to both the environment and human health, necessitating the development of efficient methods for detecting and categorizing these micro pollutant particles. As a solution, Dense‐UNet with Convolutional Vision Transformer (Dense‐UNet‐CvT), a novel deep learning (DL)‐based model is proposed to detect and classify the MPs by performing the computer vision tasks. The main objective of this work is to enhance the detection accuracy in detecting the MPs classified from the input images. Initially, a holographic MPs image dataset comprising primary classes such as polyethylene (PE), polystyrene (PS), low‐density polyethylene (LDPE), polyhydroxyalkanoate (PHA) is collected for training and evaluating the research model. The images from the dataset are preprocessed by performing image resizing, Recursive Exposure based Sub‐Image Histogram Equalization (RESIHE)‐based image enhancement, Gaussian Adaptive Bilateral Filtering (GABF)‐based denoising to improve the visual quality of the images. The preprocessed images are applied for segmentation using the Dense‐UNet model for performing semantic segmentation. The CvT model is implemented to extract useful features and to perform classification on detecting the known and unknown classes of MPs labeled in the collected dataset. The MPs detection and classification performances are computed in terms of detection rate, accuracy, f1‐score, and precision. The Dense‐UNet‐CvT model achieved 98.22% detection rate, 98.59% accuracy, 98.35% f1‐score, and 98.76% precision. These performances are compared with the current models for proper validation, in which the research model outperformed all the compared models in terms of performance. Overall, the proposed Dense‐UNet‐CvT model demonstrates superior performance across multiple evaluation metrics, suggesting its effectiveness in detecting and classifying MPs contamination in holographic images. [ABSTRACT FROM AUTHOR]

A group of neurological disorders that manifests in a wide range of motor and cognitive symptoms, Parkinson's Disease (PD) ranks second among those affecting older adults. Many people mistakenly believe that PD symptoms are caused by other conditions, such as essential tremor or natural aging. There may not be a cure for PD, but there are numerous medications that can help with symptoms. Since gait impairment is a prominent and early sign of PD in a clinical context, doctors typically use visual observations to analyse gait abnormalities as one of many manifestations to determine the severity of PD. Nevertheless, therapists' reliance on their own knowledge and judgment makes gait examination a difficult and subjective process. Extracting useful gait features from various input signals for gait analysis has recently been used Deep Learning (DL) models. Intermediate Medical Unit (IMU) patients with movement problems may benefit from a quick and clinically relevant evaluation of gait abnormalities performed automatically utilizing DL algorithms. Using gait analysis, this study provides a comprehensive overview of various DL frameworks that have been created for the purpose of PD prediction and categorization. At the outset, In the first part of the study, various PD prediction and classification systems that have been developed by various researchers and that rely on DL algorithms via gait analysis are reviewed briefly. To find a better way to identify and categorize the PD, we next undertake a comparative analysis to learn about the shortcomings of those algorithms. [ABSTRACT FROM AUTHOR]

The structure of the traditional stock market consists of collaboration of various individuals, organizations and several intermediaries namely brokers and traders. In the Indian Stock market, an investor can participate in the trading process only through stock brokers. Stock broker firms charge these investors for the services provided. Each party manages its own system and collaborates with others to facilitate trading on the stock exchange platform. The buy and sell orders go through different parties before settlement. The execution of post-trade settlements takes several days. Involvements of several intermediaries lead to shortcomings such as single point failure, brokerage charges, weak transparency and longer time for posttrade settlements. Blockchain technology is a distributed ledger technology that allows peer to peer transactions eliminating third parties. In this project, we use a blockchain-based architecture to implement the stock trading process. Our architecture uses a private blockchain network to create consortium networks leveraging few participants in the traditional stock market as the validating nodes. The stock trading logic is implemented in a smart contract that is deployed on the private blockchain network. Since the new platform does not introduce significant changes to the stock exchange trading logic and does not eliminate any of the major traditional parties from the system, our proposal promotes efficient adoption and deployment of decentralized stock exchange platforms. [ABSTRACT FROM AUTHOR]

Nowadays, most of the shopping malls, airports, hospitals, etc. have underground parking lots where hundreds of vehicles can be parked. However, first-time visitors find it difficult to remember the parked location in the indoor parking structures. Hence, in this project a navigation system for visitors to find their cars in indoor parking garages is proposed. The proposed system comprises a positioning-assisting subsystem and a car-searching mobile app. The subsystem uses beacon technology for indoor positioning and supporting Mobile app for self-guidance. In the phase of setting up this position assisting system, local coordinate is estimated to support the identification of the locations of parking spaces, beacon devices ranges and to overcome the trilateration issues. The position-assisting subsystem is implemented using Moko H2 navigation beacon devices and a carsearching mobile app was designed to using Android platform. The proposed system is deployed on real time car parking platform and the experimental results verified that the system works well to provide the visitors with route guidance. The designed system also finds its scope in providing flexible guidance to people in confusing, unknown buildings and building complex. [ABSTRACT FROM AUTHOR]

This Study Is Made To Identify The Employee Satisfaction On Welfare Measures In Shipping And Logistics Sector. An Organizations Success Lies In The Hands Of Its Employees. An Organization Can Grow And Reach Big Heights If Its Employees Are Happy And Given Constant Motivation. As Long As The Organization Does This, Half The Problem Occurring In The Organization Won't Be Happening. This Study Helps The Organization To Know Whether Their Employees Are Happy, Motivated, Working Environment Is Good Or Not, Working Conditions Is Being Maintained Or Not. Welfare Measures For Employees Are Important As Much As The Vision And Mission Of The Company. The Employeesmay Have Different Perception And Varying Opinions On The Welfare Measures Of The Organization. Questions Such As Leave Policy, Working Hours And Environment, Restroom And Lunchroom Facilities, Handling Of Complaints And Grievances Are Asked In The Questionnaire By Which The Satisfaction Level Of Employees Is Identified. [ABSTRACT FROM AUTHOR]