A Review based Investigation of Exploratory analysis in AI and Machine Learning for a Variety of Applications

In recent years, the number of production settings that make use of machine learning (ML) and other types of AI has grown significantly. The research presents a comprehensive review of where machine learning (ML) applications stand in industrial contexts at present. The development of smart mining tools has allowed for the generation, collection, and exchange of data in near-real time. This is why there is so much interest in machine learning (ML) studies in the mining industry. Additionally, this study provided a thorough evaluation of data sciences and ML's applications in a variety of petroleum engineering and geosciences domains, such as petroleum exploration, reservoir characterization, oil well drilling, production, and well stimulation, with a focus on the rapidly developing area of unconventional reservoirs. Future directions for data science and ML in the oil and gas industry are discussed, and the properties of ML that are necessary to enhance prediction are analysed. This study provides a detailed comparison of various ML techniques that can be used in the oil and gas industry. New possibilities for analysing and predicting medical data have emerged thanks to the development of artificial intelligence and machine learning, which were covered in this article. Multiple recent studies have shown that AI and ML can be used to fight the COVID-19 pandemic. This article's goal is to offer reviewers with an overview of recent studies that have made use of AI and ML in a variety of contexts

Progressive Supranuclear Palsy-like Syndrome After Aortic Aneurysm Repair: A Case Series

The syndrome of progressive supranuclear palsy‐like syndrome is a rare complication of ascending aortic aneurysm repair. We report two patients with videos and present a table of prior reported cases. To our knowledge there is no previously published video of this syndrome. The suspected mechanism is brainstem injury though neuroimaging is often negative for an associated infarct. We hope our report will increase recognition of this syndrome after aortic surgery, especially in patients with visual complaints

Pseudo Trained YOLO R_CNN Model for Weapon Detection with a Real-Time Kaggle Dataset

The Recurrent Convolutional Neural Networks (RCNN) based deep learning models has been classified image patterns and deep features through layer architecture. In this world every country doesn’t encouraging violence, so that indirectly nations prohibiting usages of weapons to common people. This study proposes a novel YoLo Faster R-CNN based weapon detection algorithm for unusual weapon object detection. The proposed YoLo V3 R-CNN computer vision application can rapidly find weapons carried by people and highlighted through bounding-box-intimation. The work plan of this research is divided into two stages, at 1st stage pre-processing has been called to Faster R-CNN segmentation. The 2nd stage has been training the dataset as well as extracting 8-features (image_id, detection score, pixels-intensity, resolution, Aspect-ratio, PSNR, CC, SSIM) into .csv file. The labeling can be performed to RCNN-YoLo method such that getting real-time objects detection (Unusual things). The Confusion matrix has been generating performance measures in terms of accuracy 97.12%, SSIM 0.99, sensitivity 97.23%, and throughput 94.23% had been attained which are outperformance methodology

Screening Mammograms in Alzheimer’s Disease Patients

Very little guidance exists to help clinicians and families decide whether mammograms are useful in elderly women with Alzheimer’s Disease (AD). We present a case of a patient with moderate AD who had a positive mammogram and discuss the dilemma faced by the family and clinician in deciding what was best to do for the patient. In this case, the family opted for breast conserving surgery (BCS) followed by palliative care which brought up the question of whether screening was appropriate with this treatment goal in mind. We reviewed the literature on AD and breast cancer screening and summarize these findings in our discussion

A Novel Loom for Alacrity of Protected Lawsuit dealings using Cloud Computing Environment

This paper suggest a well-organized information system for facilitate the litigation procedures Information System courts. The purpose is to decrease the duration of processing cases in courts. The aspiration is to save the time and effort of judges and lawyer. In addition, we make use of the advantages of electronic systems and reducing traffic especially in developed countries. Advanced Encryption Standard is used to encrypt all the manipulated data for each case. All read document are encrypted to attain secure information system Litigation process. This is because the big data for all cases will be stored on cloud environment

Pseudo Trained YOLO R_CNN Model for Weapon Detection with a Real-Time Kaggle Dataset

The Recurrent Convolutional Neural Networks (RCNN) based deep learning models has been classified image patterns and deep features through layer architecture. In this world every country doesn’t encouraging violence, so that indirectly nations prohibiting usages of weapons to common people. This study proposes a novel YoLo Faster R-CNN based weapon detection algorithm for unusual weapon object detection. The proposed YoLo V3 R-CNN computer vision application can rapidly find weapons carried by people and highlighted through bounding-box-intimation. The work plan of this research is divided into two stages, at 1st stage pre-processing has been called to Faster R-CNN segmentation. The 2nd stage has been training the dataset as well as extracting 8-features (image_id, detection score, pixels-intensity, resolution, Aspect-ratio, PSNR, CC, SSIM) into .csv file. The labeling can be performed to RCNN-YoLo method such that getting real-time objects detection (Unusual things). The Confusion matrix has been generating performance measures in terms of accuracy 97.12%, SSIM 0.99, sensitivity 97.23%, and throughput 94.23% had been attained which are outperformance methodology

A Novel Method for Multi-Variate Text Summarization

In this modern age, where vast quantities of data are accessible on the Internet, it is crucial to provide a better mechanism for extracting information quickly and efficiently. Manually extracting the description of a huge text document is incredibly hard and time-consuming. On the Internet, there is a wealth of text content. As a result, finding relevant documents among the large set of documents available and extracting necessary details from them is a challenge. Automatic text summarization is critical for solving the two problems listed above. The method of identifying the most important and pertinent material in a document or a group of related documents and compacting it into a condensed version while maintaining its overall significance is known as text summarization. Before precluding text summarization, it’s important to know the actual import of the Summary. A summary is a text that extracts information from one or more texts and conveys it concisely. The aim of Automatic Text summarization is to covert the source material into a semantically shorter adaption. The most relevant benefit of using a summary is that it shrinks the amount of time it takes to comprehend. Extractive and Abstractive are two types of content summarization techniques. An extractive summary technique involves selecting key sentences, pieces, and other elements from the original report and connecting them into a more manageable structure. An abstractive method is an apprehension of the key ideas in a text and then expressions of those ideas in a plain regular language

Cytosolic DNA Sensing Protein Pathway Is Activated in Human Hearts With Dilated Cardiomyopathy

INTRODUCTION: The genome is constantly exposed to numerous stressors, which induce DNA lesions, including double-stranded DNA breaks (DSBs). DSBs are the most dangerous, as they induce genomic instability. In response to DNA damage, the cell activates nuclear DNA damage response (DDR) and the cytosolic DNA sensing protein (CDSP) pathways, the latter upon release of the DSBs to the cytosol. The CDSP pathway activates NFκB and IRF3, which induce the expression of the pro-inflammatory genes. There is scant data on the activation of the CDSP pathway in human hearts with dilated cardiomyopathy (DCM). AIM: We aimed to determine expression levels of selected components of the CDSP pathway in human hearts with DCM. METHODS: The DNA strand breaks were detected by the single-cell gel electrophoresis or the comet assay and expression of selected proteins by immunoblotting. Transcript levels were quantified in the RNA-Seq data. RESULTS: Single-cell gel electrophoresis showed an approximately 2-fold increase in the number of COMET cells in the DCM hearts. Immunoblotting showed increased levels of cyclic GMP-AMP synthase (CGAS), the canonical CDSP; TANK-binding kinase 1 (TBK1), an intermediary kinase in the pathway; and RELB, P52, and P50 components of the NFκB pathway in human heart samples from patients with DCM. Likewise, transcript levels of over 2 dozen genes involved in inflammatory responses were increased. CONCLUSIONS: The findings provide the first set of evidence for the activation of the CDSP pathway in human hearts with DCM. The data in conjunction with the previous evidence of activation of the DDR pathway implicate the DSBs in the pathogenesis of human DCM

Implementation of MHMIP and Comparing the Performance With MIP and DHMIP in Mobile Networks

Managing the mobility efficiently in wireless networks causes critical issue, in order to support mobile users. To support global mobility in IP networks The Mobile Internet Protocol (MIP) has been proposed. The Hierarchical MIP (HMIP) and Dynamic HMIP (DHMIP) strategies are also proposed for providing high signaling delay. Our proposal approach “Multicast HMIP strategy” limits the registration processes in the GFAs. For high-mobility MTs, MHMIP provides lowest mobility signaling delay compared to the HMIP and DHMIP approaches. However, it is resource consuming strategy unless for frequent MT mobility. Hence, we propose an analytic model to evaluate the mean signaling delay and the mean bandwidth per call according to the type of MT mobility. In our analysis, the MHMIP gives the best performance among the DHMIP and MIP strategies in almost all the studied cases. The main contribution of this paper is to implement the MHMIP and provide the analytic model that allows the comparison of MIP, DHMIP and MHMIP mobility management approaches

Chk1 Haploinsufficiency Results in Anemia and Defective Erythropoiesis

Erythropoiesis is a highly regulated and well-characterized developmental process responsible for providing the oxygen transport system of the body. However, few of the mechanisms involved in this process have been elucidated. Checkpoint Kinase 1 (Chk1) is best known for its role in the cell cycle and DNA damage pathways, and it has been shown to play a part in several pathways which when disrupted can lead to anemia.Here, we show that haploinsufficiency of Chk1 results in 30% of mice developing anemia within the first year of life. The anemic Chk1+/- mice exhibit distorted spleen and bone marrow architecture, and abnormal erythroid progenitors. Furthermore, Chk1+/- erythroid progenitors exhibit an increase in spontaneous DNA damage foci and improper contractile actin ring formation resulting in aberrant enucleation during erythropoiesis. A decrease in Chk1 RNA has also been observed in patients with refractory anemia with excess blasts, further supporting a role for Chk1 in clinical anemia.Clinical trials of Chk1 inhibitors are currently underway to treat cancer, and thus it will be important to track the effects of these drugs on red blood cell development over an extended period. Our results support a role for Chk1 in maintaining the balance between erythroid progenitors and enucleated erythroid cells during differentiation. We show disruptions in Chk1 levels can lead to anemia