Determination of Biomass in Shrimp-Farm using Computer Vision

The automation in the aquaculture is proving to be more and more effective these days. The economic drain on the aquaculture farmers due to the high mortality of the shrimps can be reduced by ensuring the welfare of the animals. The health of shrimps can decline with even barest of changes in the conditions in the farm. This is the result of increase in stress. As shrimps are quite sensitive to the changes, even small changes can increase the stress in the animals which results in the decline of health. This severely dampens the mortality rate in the animals. Also, human interference while feeding the shrimps severely induces the stress on the shrimps and thereby affecting the shrimp’s mortality. So, to ensure the optimum efficiency of the farm, the feeding of the shrimps is made automated. The underfeeding and overfeeding also affects the growth of shrimps. To determine the right amount of food to provide for shrimps, Biomass is a very helpful parameter. The use of artificial intelligence (AI) to calculate the farm's biomass is the project's primary area of interest. This model uses the cameras mounted on top of the tank at densely populated areas. These cameras monitor the farm, and our model detects the biomass. By doing so, it is possible to estimate how much food should be distributed at that particular area. Biomass of the shrimps can be calculated with the help of the number of shrimps and the average lengths of the shrimps detected. With the reduced human interference in calculating the biomass, the health of the animals improves and thereby making the process sustainable and economical

Digital Labeling and Narrative Mapping in Mobile Remote Audio Signage: Verbalization of Routes and Generation of New Verbal Route Descriptions from Existing Route Sets

Independent navigation is a great challenge for people with visual impairments. In this project, we have designed and implemented an assisted navigation solution based on the ability of visually impaired travelers to interpret and contextualize verbal route descriptions. Previous studies have validated that if a route is verbally described in sufficient and appropriate manner then VI can use their orientation and mobility skills to successfully follow the route. In this project, we do not consider the issue how the VI will interpret the route descriptions, but we aim to identify and generate new verbal route descriptions from the existing route descriptions. We discuss different algorithms that we have used for extracting the landmarks, building graphs and generation of new route descriptions from existing route info

A Novel Approach for Serving The Web By Exploiting Email Tunnels In Networks

Traffic separating is shabby, compelling, and has little effect on other system administrations and in this way on most by far of clients in the oversight district who are not taking part in circumvention. Another issue with the current oversight circumvention frameworks is that they can't endure fractional bargain. Shockingly, existing oversight circumvention frameworks don't give high accessibility certifications to their clients, as controls can without much of a stretch distinguish, consequently upset, the traffic having a place with these frameworks utilizing the present propelled restriction innovations. In this paper, we propose Serving the Web by Exploiting Email Tunnels (SWEET), an exceedingly accessible restriction safe foundation. SWEET works by embodying an edited client's traffic inside email messages that are continued open email administrations like Gmail and Yahoo Mail. As the activity of SWEET isn't bound to an explicit email supplier, we contend that a blue pencil should square email interchanges all together so as to upset SWEET, which is impossible as email establishes an imperative piece of the present Internet

On Convex Dimensionality Reduction for Classification

Dimensionality reduction (DR) is an efficient approach to reduce the size of data by capturing the informative intrinsic features and discarding the noise. DR methods can be grouped through a variety of categories, e.g. supervised/ unsupervised, linear/non-linear or parametric/non-parametric. Objective function based methods can be grouped into convex and non convex. Non convex methods have the tendency to converge to local optimal solutions which may differ from the desired global solution. To overcome this problem, one can frame a non convex problem as a convex problem. A direct transformation of a non convex formulation into a convex formulation is non trivial. In this project, we chose a non convex, linear, supervised, multi-class, non parametric DR method’s objective, which can be framed using convex formulation. We present the process of convex formulation, different methods of implementation, approaches to increase efficiency and numerical analysis results

GENERATYWNY MODEL Z DEEP FAKE AUGUMENTATION DLA SYGNAŁÓW Z FONOKARDIOGRAMU ORAZ ELEKTROKARDIOGRAMU W STRUKTURACH LSGAN ORAZ CYCLE GAN

In order to diagnose a range of cardiac conditions, it is important to conduct an accurate evaluation of either phonocardiogram (PCG) and electrocardiogram (ECG) data. Artificial intelligence and machine learning-based computer-assisted diagnostics are becoming increasingly commonplace in modern medicine, assisting clinicians in making life-or-death decisions. The requirement for an enormous amount of information for training to establish the framework for a deep learning-based technique is an empirical challenge in the field of medicine. This increases the risk of personal information being misused. As a direct result of this issue, there has been an explosion in the study of methods for creating synthetic patient data. Researchers have attempted to generate synthetic ECG or PCG readings. To balance the dataset, ECG data were first created on the MIT-BIH arrhythmia database using LS GAN and Cycle GAN. Next, using VGGNet, studies were conducted to classify arrhythmias for the synthesized ECG signals. The synthesized signals performed well and resembled the original signal and the obtained precision of 91.20%, recall of 89.52% and an F1 score of 90.35%.W celu zdiagnozowania szeregu chorób serca, istotne jest przeprowadzenie dokładnej oceny danych z fonokardiogramu (PCG) i elektrokardiogram (EKG). Sztuczna inteligencja i diagnostyka wspomagana komputerowo, oparta na uczeniu maszynowym stają się coraz bardziej powszechne we współczesnej medycynie, pomagając klinicystom w podejmowaniu krytycznych decyzji. Z kolei, Wymóg ogromnej ilości informacji do trenowania, w celu ustalenia platformy (ang. framework) techniki, opartej na głębokim uczeniu stanowi empiryczne wyzwanie w obszarze medycyny. Zwiększa to ryzyko niewłaściwego wykorzystania danych osobowych. Bezpośrednim skutkiem tego problemu był gwałtowny rozwój badań nad metodami tworzenia syntetycznych danych pacjentów. Badacze podjęli próbę wygenerowania syntetycznych odczytów diagramów EKG lub PCG. Stąd, w celu zrównoważenia zbioru danych, w pierwszej kolejności utworzono dane EKG w bazie danych arytmii MIT-BIH przy użyciu struktur sieci generatywnych LSGAN i Cycle GAN. Następnie, wykorzystując strukturę sieci VGGNet, przeprowadzono badania, mające na celu klasyfikację arytmii na potrzeby syntetyzowanych sygnałów EKG. Dla wygenerowanych sygnałów, przypominających sygnał oryginalny uzyskano dobre rezultaty. Należy podkreślić, że uzyskana dokładność wynosiła 91,20%, powtarzalność 89,52% i wynik F1 – odpowiednio 90,35%

A Generative Adversarial Network Based Approach for Synthesis of Deep Fake Electrocardiograms

Analyzing the data from an electrocardiogram (ECG) can reveal important details about a patient's heart health. A key component of modern medicine is the use of AI and ML-based computer-aided diagnosis tools to aid in making life-or-death decisions. It is common practice to use them in cardiology for the automatic early diagnosis of a variety of potentially fatal illnesses. The machine learning algorithm's need for a large amount of training data to build the learning model is an empirical challenge in the medical domain. To address this challenge, study into methods for creating synthetic patient data has blossomed. There is a higher risk of privacy invasion due to the need for massive amounts of training data for deep learning automated medical diagnostic systems that may help assess the state of the heart from this signal. To combat this issue, researchers have tried to create artificial ECG readings by analyzing only the statistical distributions of the accessible authentic training data.The primary goal of this study is to learn how generative adversarial networks can be used to create artificial ECG signals for use as training data in a classification task. In this study, we used both GAN and WGAN for generation of artificial ECG signals

Investigation of Optimal Image Inpainting Techniques for Image Reconstruction and Image Restoration Applications

People in today's society take a lot of pictures with their smartphones and also make an effort to keep their old photographs safe, but with time, those photographs deteriorate. Image inpainting is the art of reconstructing damaged or missing parts of an image. Repairing scratches in photographs or film negatives, or adding or removing elements like stamped dates or "red-eye," are all possible through inpainting. In order to restore the image many techniques have been developed, significant techniques include exemplar based inpainting, coherent based inpainting and method for correction of non-uniform illumination. The four main applications of these image inpainting techniques are scratch removal, text removal, object removal and image restoration. However, all the four image inpainting applications cannot be implemented using a single technique. According to the literature, there has been relatively less work done in the field of image inpainting applications. Investigation has been carried out to find the suitability of these three techniques for the four above mentioned image inpainting applications based on two performance metrics

Performance of T-shaped skirted footings resting on sand

A series of plate load tests were performed on a model T-shaped skirted footing by varying the normalized skirt depth and relative density of sand from 0.25 to 1.5 and 30 % to 60 %, respectively. The findings revealed that, regardless of the roughness condition, the observed peak in the pressure settlement ratio corresponding to relative densities of 30%, 40%, 50%, and 60% gradually vanished as the normalized skirt depth was increased from 0.25 to 1.5. The results further revealed that at a given pressure, a lesser settlement ratio was observed for a skirted footing than the footing without a skirt. The most significant benefit of providing a skirt to the footing was obtained when the base and skirt were partially rough and the relative density of sand was kept at 30%. In all the cases, the observed bearing capacity ratio for the present skirted footing was higher than the H-shaped skirted footing reported in the literature. Finally, an empirical equation was proposed to predict the bearing capacity ratio and settlement reduction factor for a given skirt depth and sand relative density

Generative Adversarial Networks as a Data Augmentation Tool for Handwritten Digits

In the field of data processing, handwritten digit recognition (HDR) has proven to be of great use. However, due to the vast differences in how different people write, accurate recognition of such characters from images is a challenging job. The labelled samples necessary for supervised learning methods are not always easy to come by. For instance, a lot of labelled examples are needed to train a model in deep learning approaches, where all the feature extraction steps are learned within the artificial neural network. To get around this problem, data augmentation methods can be used to fill in the gaps using variations in an example's label that are already known. The Generative Adversarial Network (GAN) is able to generate random samples from the latent space that are statistically indistinguishable from the training set's actual examples. In this study, we leverage the powerful features of GAN to learn from the MNIST data set and produce digital images of handwriting

Soft Computing Based Prediction of Unconfined Compressive Strength of Fly Ash Stabilised Organic Clay

The current study uses machine learning techniques such as Random Forest Regression (RFR), Artificial Neural Networks (ANN), Support Vector Machines Ploy kernel (SVMP), Support Vector Machines Radial Basis Function Kernel (SVMRBK), and M5P model tree (M5P) to estimate unconfined compressive strength of organic clay stabilized with fly ash. The unconfined compressive strength of stabilized clay was computed by considering the different input variables namely i) the ratio of Cao to Sio2, ii) organic content (OC), iii) fly ash (FAper) content, iv) the unconfined compressive strength of organic clay without fly ash (UCS0) and v) the pH of soil-fly ash (pHmix). By comparing the performance measure parameters, each model performance is evaluated. The result of present study can conclude the random forest regression (RFR) model predicts the unconfined compressive strength of the organic clay stabilized with fly ash with least error followed by Support Vector Machines Radial Basis Function Kernel (SVMRBK), Support Vector Machines Ploy kernel (SVMP), Artificial Neural Networks (ANN) and M5P model tree (M5P). When compared to the semi-empirical model available in the literature, all of the model predictions given in this study perform well. Finally, the RFR and SVMRBK sensitivity analyses revealed that the CaO/SiO2 ratio was the most relevant parameter in the prediction of unconfined compressive strength