Snap Shots Research Articles

Detection of Ovarian Tumors Using TVUS Imaging and Segmentation Techniques

Ovarian most cancers remains a giant health subject due to its regularly asymptomatic development until superior stages, where treatment options are confined. Early detection is critical for enhancing affected person outcomes and survival costs. This observe proposes a comprehensive technique leveraging superior photograph processing techniques for the early detection and category of ovarian tumors the usage of trans vaginal ultrasound (TVUS) snap shots. The method starts with preprocessing steps which include median filtering to do away with noise artifacts inclusive of salt and pepper noise from ultrasound photos. Ultimately, picture enhancement techniques are applied to enhance contrast and intensity, observed through binary sample evaluation to signify texture features vital for tumor identification. Photo segmentation is performed the use of okay- manner clustering and gray degree Co-prevalence Matrix (GLCM) analysis to isolate and analyze areas of interest within the ovarian photos. A Convolutional Neural community (CNN) is then hired for correct classification of ovarian tumors as regular, benign, or malignant primarily based on the extracted functions. This approach not handiest enhances the diagnostic accuracy but also minimizes affected person publicity to ionizing radiation, making it appropriate for ordinary medical applications. The proposed methodology represents a considerable advancement within the discipline of ovarian cancer detection, presenting a non-invasive and green way to evaluate ovarian health and facilitate timely intervention. Destiny research directions include refining the set of rules’s performance with large datasets and exploring additional imaging modalities for complete ovarian fitness evaluation.

Abstract B075: The "let's talk about Black people and cancer" conference

Abstract In the United Kingdom, despite having a healthcare service that is free at the point of access, inequalities in cancer care persist. Recent data shows that for certain cancer groups (e.g. breast cancer), Black patients present with worse grade and stage leading to poorer outcomes. Reasons for the disparities are multifactorial but include lack of awareness in the community, structural barriers to accessing healthcare, limited research into genetic variability and/or biological differences, and hesitancy to engage with health care professionals due to historic wrongs. Often different stakeholders work in different silos to try and remediate these barriers however they are not often in one space and have the opportunity to learn from patient experiences and how they can all dovetail together to improve patient outcomes. In an attempt to bring different sectors together the senior author established the “Let’s talk about Black people and cancer” conference 2024. The aim of this event was to increase awareness and understanding of cancer in the UK Black population. Methods This event covered the major cancer groups (breast, prostate, haematological, bowel and gynaecological) focusing on genomics, symptom awareness, screening programs, and how and when to access secondary care. We sought patient feedback from this conference style intervention to see the effect on attendees in terms of increase in knowledge specifically around Black people and cancer. This study utilized a two-part survey to evaluate the effectiveness of the conference, assessing knowledge held by registered attendees pre- and post-conference. A 5-point Likert scale was used to extract degrees of opinion, providing accurate and representative feedback. There was a total of 46 questions in the survey targeting the demographics of the attendees, their connection to cancer, and knowledge about cancer including the risks/outcomes regarding Black people specifically. Results The world cancer day conference had 190 registrants. 83 responses to the questionnaire (43.7% response rate). Pre-conference questionnaires highlighted around two thirds had knowledge of the 3 major screening programs, but after the conference this increased to more than 90%. A similar trend was noticed when looking at change in knowledge regarding the five cancer groups covered. Conclusion We have demonstrated that this targeted conference style intervention was effective in raising awareness not just for the public but for those professionals involved in the delivery of cancer care for Black people. While these are snap shots in time it would be useful to see how that information translates in a longitudinal fashion in terms of positive modifications in health behaviors and delivery of services. Citation Format: Teniola Adeboye, Georgette Oni. The "let's talk about Black people and cancer" conference [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr B075.

SVM, KNN, and Neural Networks Investigated for Machine Learning in Written Word Decoding

The capacity of a device to recognise and understand legible handwriting input from a variety of origins, including written material, snap shots, displays, and other electronics, is known as handwritten reputation. In this study, we investigate three classification algorithms: Support Vector Machines (SVM), K-Nearest_Neighbours (KNN), and Neural Networks for handwritten character recognition, and we will identify the best one among these three.

Assessing Seasonal and Inter‐Annual Marine Sediment Climate Proxy Data

AbstractThree recently published papers including Napier et al. (2022, https://doi.org/10.1029/2021PA004355) utilize novel microanalytical approaches with varved marine sediments to demonstrate the potential to reconstruct seasonal and inter‐annual climate variability. Obtaining paleoclimate data at a resolution akin to the observational record is vitally important for improving our understanding of climate phenomena such as monsoons and modes of variability such as the El Niño Southern Oscillation, for which appraisals of past inter‐annual variability is critical. The ability to generate seasonal and inter annual resolution sea surface temperature proxy time series spanning a thousand years or more is revolutionary and has the potential to fill gaps in our knowledge of climate variability. Although generally limited to sediments from regions with oxygen depleted bottom waters, there is great potential to integrate shorter seasonal resolution climate “snap shots” from other archives such as annually banded corals into composite time series. But as paleoceanographic data are used more by the observational and modeling fields, we make the case for conducting a thorough case‐by‐case assessment of the processes that influence the climate signal recovered from proxies, using careful replication to validate new approaches. Understanding or exploring the potential influence of processes which effectively filter the climate signal will lead to more quantitative paleoceanographic data that will better serve the broader climate science community.

Temporal dynamics of antibody level against Lyme disease bacteria in roe deer: Tale of a sentinel?

Changes in the risk of exposure to infectious disease agents can be tracked through variations in antibody prevalence in vertebrate host populations. However, information on the temporal dynamics of the immune status of individuals is critical. If antibody levels persist a long time after exposure to an infectious agent, they could enable the efficient detection of the past circulation of the agent; if they persist only a short time, they could provide snap shots of recent exposure of sampled hosts. Here, we explored the temporal dynamics of seropositivity against Lyme disease agent Borrelia burgdorferi sensu lato (Bbsl) in individuals of a widespread medium-sized mammal species, the roe deer (Capreolus capreolus), in France. Using a modified commercially available immunoassay we tested 1554 blood samples obtained in two wild deer populations monitored from 2010 to 2020. Using multi-event capture-mark-recapture models, we estimated yearly population-, age-, and sex-specific rates of seroconversion and seroreversion after accounting for imperfect detection. The yearly seroconversion rates indicated a higher level of exposure in early (2010-2013) than in late years (2014-2019) to infected tick bites in both populations, without any detectable influence of sex or age. The relatively high rates of seroreversion indicated a short-term persistence of antibody levels against Bbsl in roe deer. This was confirmed by the analysis of samples collected on a set of captive individuals that were resampled several times a few weeks apart. Our findings show the potential usefulness of deer as a sentinel for tracking the risk of exposure to Lyme disease Bbsl, although further investigation on the details of the antibody response to Bbsl in this incompetent host would be useful. Our study also highlights the value of combining long-term capture-mark-recapture sampling and short-time analyses of serological data for wildlife populations exposed to infectious agents of relevance to wildlife epidemiology and human health.

Utilization of Indoor Tanning: A Cross-Sectional Study Using Mobile Device Data

Indoor tanning using ultraviolet (UV) radiation is a known carcinogen (El Ghissassi et al., 2009), associated with increased risks of melanoma (Colantonio et al., 2014), squamous cell carcinoma, and basal cell carcinoma (Wehner et al., 2012). However, there is little information on where or when indoor tanning is used. The majority of literature on indoor tanning use is survey-based and designed to measure population-level behavior changes (Rodriguez-Acevedo et al., 2019). Surveys are susceptible to recall bias and only allow sequential cross-sectional ‘snap shots,’ often with years in between surveys.

Analysis of Blind Spot in Heavy Vehicles Driving Using VIKOR Method

A blind spot is a phase of road out of doors the driving force’s view that cannot be visible via rear home windows or home windows. Vehicle height and period, huge blind spots All motors have blind spots. These are regions or zones on either facet of the car that aren’t visible to the driver when considered in the rearview or facet view reflect. If there are blind spots, a driving force must always turn his head and test the automobiles earlier than converting lanes. Look in the replicate. Blind spots may be large enough to without difficulty block another vehicle, motorbike, bicycle owner or pedestrian out of your view. The “blind spot” is not seen to the motive force in the windshield across the vehicle. They are in all automobiles, however there are four massive blind spots on 1/2 vehicles. What brought about the blind spot to seem at the back of the trailer? In each of our eyes The size of a pin There is little useful blind spot. In this small region, wherein The optic nerve passes through the surface of the retina, No photosynthesis. In the absence of photosynthetic cells that stumble on light, it creates a blind spot. On this page you will discover 18 comparable words, antonyms, linguistic expressions and associated words for blind spot, namely: lack of notion, invisibility, weak spot, oversight, and membrane in one’s eye, obstruction, blindness, mistakes, and look before changing path. Look for blind spots for your rearview replicate and vehicles close to your shoulders. Be aware of the dangers – appearance beyond the car in front of you. The small vicinity inside the retina that does not experience mild is called the blind spot. The blind spot is the area in which the nerve endings within the retina input the optic nerve. Due to the lack of visual receptors which includes stems and cones on this region, snap shots that fall on this location cannot be detected. The blind spot is an area inside your range of vision which you cannot see precisely, however you really want to look. For instance, whilst you drive a car, the region in the back of your shoulders is regularly the blind spot. What we need to focus on here is the mental (or metaphorical) blind spot, which is defined as “ones but often unknown prejudice or lack of knowledge” or “tendency to ignore anything particularly”. It is difficult or unpleasant. Some human beings see blind spots as weak. “What are the blind spots of your leadership?” Your approach has to be to respond absolutely to the weak spot you understand when you ask and to explain how you can decrease its effect. It is crucial to offer a solution that suggests which you are self-aware and may overcome your weaknesses. the final value, strongly preferred got first rank and equally preferred got fifth rank.

Inverse linear-quadratic discrete-time finite-horizon optimal control for indistinguishable homogeneous agents: A convex optimization approach

The inverse linear-quadratic optimal control problem is a system identification problem whose aim is to recover the quadratic cost function and hence the closed-loop system matrices based on observations of optimal trajectories. In this paper, the discrete-time, finite-horizon case is considered, where the agents are also assumed to be homogeneous and indistinguishable. The latter means that the agents all have the same dynamics and objective functions and the observations are in terms of “snap shots” of all agents at different time instants, but what is not known is “which agent moved where” for consecutive observations. This absence of linked optimal trajectories makes the problem challenging. We first show that this problem is globally identifiable. Then, for the case of noiseless observations, we show that the true cost matrix, and hence the closed-loop system matrices, can be recovered as the unique global optimal solution to a convex optimization problem. Next, for the case of noisy observations, we formulate an estimator as the unique global optimal solution to a modified convex optimization problem. Moreover, the statistical consistency of this estimator is shown. Finally, the performance of the proposed method is demonstrated by a number of numerical examples.

Machine Learning Based Prediction of Veno Occlusive Disease Following Allogenic Stem Cell Transplantation

Veno-occlusive disease/sinusoidal obstruction syndrome (VOD/SOS) is a potentially life-threatening complication of stem cell transplantation. Hitherto, patient snap shots have been focussed upon, while predicting the onset and severity of VOD/SOS. We present a novel mechanistic learning of the score of VOD/SOS onset and progression using recorded values of pre-transplant parameters and during-transplant observations on 25 patients, as part of a longitudinal data comprising data stored in patient records at multiple institutes. Subsequently, we learn the relationship between such pre-transplant variables and this learnt score, using Machine Learning (ML) techniques that permit the desired avoidance of imposed forms of this relationship. This allows reliable prediction of the VOD/SOS score of a new patient with observed pre-transplant parameters. We discuss results of such undertaking. Using the learnt relationship between VOD/SOS score and patient pre-transplant attributes, we also identify relative potency of each pre-transplant variable, towards VOD/SOS onset and progression. Our ML-based learning and prediction offer protocols that enable early identification of VOD susceptibility in a new patient at the pre-transplant stage, thus permitting intervention, enabling VOD/SOS mitigation.

A Model for Sign Language Recognition System using Deep Learning

Abstract: Conversing to someone with listening disability is usually the main challenge. Sign language has indelibly ended up the final panacea and is a completely effective device for people with listening and speech inability to speak their emotions and critiques to the world. It makes the combination technique among them and others easy and much less complex. However, the discovery of signal language alone, isn't always enough . There are many strings connected to this boon.The signal gestures regularly get blended and stressed for a person who has by no means learned or is aware of it in a exclusive language. However, this communique gap which has existed for years can now be narrowed with the advent of diverse strategies to automate the detection of signal gestures . In this paper, we introduce a Sign Language reputation the use of Sign Language. In this study, the consumer have to be capable of seize snap shots of the hand gesture the use of internet digital digicam and the device shall expect and display the call of the captured image. We use the HSV shade set of rules to come across the hand gesture and set the historical past to black. The snap shots go through a chain of processing steps which consist of diverse Computer imaginative and prescient strategies including the conversion to grayscale, dilation and masks operation. And the location of hobby which, in our case is the hand gesture is segmented. The capabilities extracted are the binary pixels of the snap shots. We employ Convolutional Neural Network(CNN) for schooling and to categorise the snap shots. We are capable of realising 10 Sign gesture alphabets with excessive accuracy. Our version has carried out a wonderful accuracy of above 90%.

Adaptive Threshold and Directional Weighted Median Filter-Based Impulse Noise Removal Method for Images

Elimination of impulse noise in image snap shots with side renovation is one of the complex duties in digital image processing. In this paper, the removal of random impulse noise is done in two important levels. In first level, the detection of the impulse noise is done on the premise of a double threshold selecting strategy after which in the another level, elimination of impulse noise is done by the usage of median filter and directional weighted median filter relying upon the noise map (Nmap) construction of corrupted pixels detected within the first level. The proposed method makes use of the statistical characteristics of noisy image graphs and the brink obtained is adaptable to one of a kind of snap shots and noise conditions. Comparative evaluation with different widespread de-noising techniques shows that the proposed method outperforms in terms of PSNR, SSIM, NMSE and Computation Time (CT) of the distinct trying out test images, with exclusive noise levels.

Numerical Study of Transient Elastohydrodynamic Lubrication Subjected to Sinusoidal Dynamic Loads for Rough Contact Surfaces

Abstract The purpose of this paper is to study the behaviour of transient elastohydrodynamic contacts subjected to forced harmonic vibrations, including the effect of surface waviness for concentrated counterformal point contact under isothermal conditions. Profiles of pressure and film thickness are studied to reveal the combined effects of sinusoidal external load and surface roughness on the lubrication problem. The time-dependent Reynolds’ equation is solved using Newton–Raphson technique. The film thickness and pressure distribution are obtained at different snap shots of time by simultaneous solution of the Reynolds’ equation and film thickness equation including elastic deformation and surface waviness. It is concluded that the coupling effects of the transient sinusoidal external load and wavy surface would result in increase in modulations of the pressure and film thickness profile in comparison to the case where the smooth contact surfaces are subjected to sinusoidal external load.

Face Mask Detection in Crowd Areas using Deep Learning for Avoid Spreading of Covid-19 Corona Virus

Object discovery, focused on robotically noting device factors on snap shots or movies, expands the photo kind. In the previous couple of years, smart traveler manipulate has been appreciably utilized in small matters detection methods made use of in website vacationer manage, shrewd radar to manipulate structures, armed forces matters detection tools in the Navy. Great for beneficial treatments made use of to discover. Positioning of scientific tools made use of within the navy, medical navigating, operations, and so forth. COVID-19, a special corona virus dispersing in overdue 2019, stands for a opportunity for public fitness. Numerous international locations demand that everybody wear a public masks to keep away from the unfold of the Corona virus. To prevent the unfold of the Corona virus, an item discovery method just like a unmarried shot detector focuses on specific in addition to real-time popularity of face mask in supermarkets. Contributions are made with 3 parts: 1) to attract out the functions used by the lightweight spine network, that's primarily based upon SSD and additionally exquisite separable convection, which enhances discovery charge and Satisfies real-time detection wishes. 2) Suggest Feature Improvement Module (FEM) to strengthen the CNN fashions' deeper capabilities to beautify tiny things feature examples. 3) Develop a COVID-19-Mask, a big facts set to decide if customers are sporting mask, by gathering snap shots in 2 supermarkets. The corporation goals to reap high detection accuracy and also real-time efficiency of the proposed components.

Integrated Genomic and Bioinformatics Approaches to Identify Molecular Links between Endocrine Disruptors and Adverse Outcomes.

Exposure to Endocrine Disrupting Chemicals (EDC) has been linked with several adverse outcomes. In this review, we examine EDCs that are pervasive in the environment and are of concern in the context of human, animal, and environmental health. We explore the consequences of EDC exposure on aquatic life, terrestrial animals, and humans. We focus on the exploitation of genomics technologies and in particular whole transcriptome sequencing. Genome-wide analyses using RNAseq provides snap shots of cellular, tissue and whole organism transcriptomes under normal physiological and EDC perturbed conditions. A global view of gene expression provides highly valuable information as it uncovers gene families or more specifically, pathways that are affected by EDC exposures, but also reveals those that are unaffected. Hypotheses about genes with unknown functions can also be formed by comparison of their expression levels with genes of known function. Risk assessment strategies leveraging genomic technologies and the development of toxicology databases are explored. Finally, we review how the Adverse Outcome Pathway (AOP) has exploited this high throughput data to provide a framework for toxicology studies.

Lungs disease detection using image processing through python

The novel coronavirus disease (COVID-19), with a start line in China, has spread hastily amongst human beings dwelling in other international locations, and is coming near approximately 34,986,502 instances worldwide in line with the facts of Edith Cowan University (ecu) Centre for disorder prevention and control. There are a restrained number of COVID-19 test kits to be had in hospitals due to the growing cases day by day. Consequently, it is important to implement an automated detection machine as a brief opportunity diagnosis choice to prevent COVID-19 spreading among human beings. Fusion was considered as a concatenation between the two-person vectors on this context. Speckle-affected and coffee-fine X-ray images along with top first-class pictures have been utilized in our test for carrying out exams. If training and trying out are done with best selected right fine X-ray photos in a super situation, the output accuracy can be observed higher. However, this doesn't constitute a real-existence situation, wherein the photo database would be a mixture of each appropriate- and poor-first-rate pictures. Therefore, this technique of the use of different excellent snap shots could test how nicely the machine can react to such real-lifestyles situations. A modified anisotropic diffusion filtering technique become hired to take away multiplicative speckle noise from the test photographs. The software of these techniques ought to successfully conquer the restrictions in enter photograph quality. Subsequent, the function extraction changed into finished on the test photographs. Ultimately, the Convolutional Neural Network (CNN) classifier accomplished a type of X-ray photographs to pick out whether or not it changed into COVID-19 or until now. Pneumonia, an interstitial lung sickness, is the main reason of loss of life in children under the age of five. It accounted for approximately 16% of the deaths of kids below the age of 5, killing around 880,000 kids in 2016 according to a look at conducted with the aid of United Nations International Children's Emergency Fund (UNICEF). Affected children were mostly much less than two years old. Well timed detection of pneumonia in youngsters can assist to the technique of restoration. This paper gives convolutional neural community fashions to accurately hit upon pneumonic lungs from chest X-rays, which can be utilized inside the actual global by using medical practitioners to treat pneumonia. Experimentation was conducted on Chest X-Ray images dataset to be had on Kaggle.

Study of Methodologyof Optimizing Image Segmentation and Processing Using Genetic Algorithm

In the literature, several worksfocal point on the description of contrast metrics and standards that allow to count the overall an image processing algorithm performance. These assessment standards can be investigated to outline new photo processing algorithms by way of optimizing them. In this work, we suggest a universal scheme to phasephotographswith the aid of a genetic algorithm. The developed approach makes use of an estimation standard which counts the first-class of a photo segmentation outcome. The suggested type segmentationapproach can combine a local ground reality when it is accessible in order to set the preferred degree of the last result accuracy. A genetic algorithm(G-A) is then investigated in sort to decide the first-rate mixture of statistics excerpted with the aidof the chosen standard. Then, we exhibit that this method can either be utilized for gray-levels or multicomponent snap shots in a supervised context or in an unsupervised one. Last, we exhibit the efficiency of the suggested approachviaseveral experimental outcomes on a number of(levels of gray) and multi-components images.

A Pilot Study of the Application of Natural Language Interpretation to Identify Insights on Acute Lymphocytic Leukemia and Its Treatment from Social Media Data

Social media (SM) data is emerging as an important source of information on patient disease and treatment experiences. SM data provide researchers access to unsolicited information shared by patients/caregivers (CGs)/healthcare providers (HCPs), without burdens associated with traditional research methods or influence of interviewer bias. SM facilitates open communication on serious illness in an unstructured forum; offering snap shots of information that highlight important issues prioritized by patients/CGs/HCPs. SM data is growing exponentially, manual review of this noisy “Big Data” is time consuming and impractical. Natural language understanding (NLU) provides an optimal approach to extracting structured data elements from unstructured text. This pilot study aimed to use NLU to aggregate, analyze, and better understand various aspects of patients/CGs/HCPs experiences of Acute lymphocytic leukemia (ALL). ALL is a blood and bone marrow cancer with a significant patient symptom burden and detrimental impact on patient/CG health-related quality of life (HRQOL).Publicly available, English-language SM data were indexed from a 25-month period (Jul.2018-Aug.2020). Data were extracted using Sprinklr (Twitter), and Board Reader (boards/blogs/forums/reviews). NLU was applied to capture insights (e.g., emerging patterns/trends/relationships). Advanced analytics identified and analyzed relevant insights, and advanced logic determined user type and post contributor (e.g., patient/CGs/HCP). Posts underwent further manual evaluation using thematic content analysis to explore the experience of ALL shared by users and to support NLU-identified insights.6,365 ALL-related posts were identified; 289 (4.5%) were contributed by patients, 657 (10.3%) by CGs, 1224 (19.2%) by HCPS and 4,195 (65.9%) by organizations. Topics included expressions of positive support/encouragement for patients/monetary donation requests/treatment type/impact of side effects. NLU identified notable discussion areas: patient/CG commentaries on treatment experiences, decisions, side effects and outcomes, and patient knowledge regarding ALL treatments. 189 patient/CG posts discussed ALL treatments in general, including in-hospital time: missing life events due to intensive treatment regimens and extended hospital stays. 195 patient/CG posts discussed specific ALL treatment experiences: chemotherapy, n=120; bone marrow transplant, n=22; stem cell transplant, n=16; immunotherapy, n=14; Kymriah, n=11; CAR-T, n=8; radiation, n=4. Chemotherapy was mentioned in relation to daily and/or durable short-and long-term effects (e.g., sickness, pain) which had major impacts for patients' and their family's HRQOL. Chemotherapy was associated with unforeseen impacts related to patients compromised immune systems (e.g., restricted social functioning due to increased risk of infection). Fear of infection and impact of treatment notably increased patients' needs to isolate which had a substantial impact on the broader family life. Bone marrow transplant posts described it as ‘lifesaving’. Despite uncertainty of treatment success and negative treatment effects, patient/CG posts noted that side effects were an acceptable part of the journey to become cancer free. Posts also illustrated a general shift in patient/CG perception of ALL treatments, specifically that no one treatment works for everyone in the same way, and recent treatment developments mean that ALL is no longer perceived as a death sentence. 66 patient/CG posts commented on the cessation of treatment or lack of treatment due to remission, alternative treatments or end of life. Financial burden due to ALL treatment was an important issue for patients/CGs as it prevented the start of treatment or impaired patients‘/CGs’ lives.This pilot study shows how NLU can effectively extract SM data expressed by patients/CGs/HCPs relating to ALL. It is important to note that SM data are unregulated, not peer reviewed and inherently reliant on user self-identification. Thus, caution should be used interpreting SM data particularly as the information is not generalizable and/or reflective of the whole ALL patient population. Nevertheless, the pilot demonstrated how user-generated SM data can offer valuable insights on the experience and impact of ALL and its treatments that exist outside of the formal research context. DisclosuresCrawford: Pfizer Inc: Consultancy. Welch: Pfizer Inc: Current Employment. Shah: Pfizer Inc: Current Employment. Doward: Pfizer Inc: Consultancy. Truchan: Pfizer Inc: Current Employment.

Face Recognition Based Attendance Record System

In today’s world, face recognition is the type of biometric that is used in almost every field. This technology is used for security purposes and can be used in many verification and security system. Though it is less efficient than eyes recognition and fingerprint recognition, is still in market due to its untouchability and non-intrusive method. Besides, face recognition should also be utilized for attendance checking in schools, colleges, offices, etc. Face Recognition method pivot to build up a class attendance system which uses the idea of face recognition as present hand done attendance process is lethargic and not suitable to keep. And there are chances of too much proxy attendance. Thus, the want for this method is much needed. This method involves 4 stages- database introduction, face detection, face recognition, attendance updation. The database is made by taking the snap shots of the students in elegance. Face detection and popularity is done using python opencv. Attendance is to be exported at the end of semester.

Bangla numerical sign language recognition using convolutional neural networks (CNNs)

<p>The amount of deaf and mute individuals on the earth is rising at an alarmingrate. Bangladesh has about 2.6 million people who are unable to interact with the community using language. Hearing-impaired citizens in Bangladesh use Bangladeshi sign language (BSL) as a means of communication. In this article,we propose a new method for Bengali sign language recognition based on deep convolutional neural networks. Our framework employs convolutional neural networks (CNN) to learn from the images in our dataset and interpret hand signs from input images. Checking their collections of ten indications (we usedten sets of images with 31 distinct signs) for a total of 310 images. The proposed system takes snap shots from a video by using a webcam with applying a computer vision-based approach. After that, it compares those photos to a previously trained dataset generated with CNN and displays the Bengali numbers (০-৯). After estimating the model on our dataset, weobtained an overall accuracy of 99.8%. We want to streng then things as far aswe can to make silent contact with the majority of society as simple asprobable.</p>

Quaternary environments and monsoonal climate off northwest Australia: Palynological evidence from Ocean Drilling Program Site 765

The bathyal Ocean Drilling Program Site 765 at 5725 m water depth, offshore northwest Australia at 16°S is directly under the influence of the Australian monsoon during the Austral summer and is the recipient of continental dust during the Austral winter. It is downstream of the Indonesian Throughflow, which is a major arm of the global thermohaline circulation. As such it is ideally situated to record the climate and oceanic consequences of Quaternary climate variability. Despite being over 400 km from northwest Australia, palynomorphs (pollen and spores) are relatively common in this section, sourced via aeolian (during the dry winter) and benthic transportation processes and sediment plumes (during the summer monsoon). Detailed palynological analyses of this flora in the upper part of this core reveals intermittent snap shots of environmental and climate change over the last 300 kyrs. Interglacial stages are interpreted to be characterised by palynomorph-rich turbidite and calcareous ooze deposition whereas palynomorph-poor slowly accumulating siliceous oozes (deposited below the Calcium Carbonate Compensation Depth) are present during glacials. The dominance of Poaceae sourced from the Australian mainland in interglacial periods suggests that vegetation during these periods was similar to today. Interglacial palynofloral assemblages suggest a more intense wet season (Australian monsoon) with higher rainfall that allowed more active erosion and deposition onto the shelf. The presence of Indonesian sourced pollen and fern spore taxa, as well as warm water dinoflagellate species suggest enhanced Leeuwin Current and monsoonal intensity during interglacials times. The youngest part of the core is dominated by siliceous ooze, likely deposited during the Last Glacial Maximum and the early Holocene. The lack of calcareous ooze near the top of the core is likely caused by Holocene to Recent erosive processes or core disturbance. The presence of common charcoal in all samples over the last 300 kyrs shows that fire was a constant feature of the landscape in northwest Australia prior to human occupation of the region 65,000 years ago.