Deep convolutional neural network to predict ground water level

In contrast to the atmosphere and fresh surface water, which can only briefly store water, the natural water cycle may use groundwater as a “reservoir” that stores water for extended periods. Even though there is a considerable degree of variation and complexity in the subsurface environment, there is a minimal availability of data from the field. Both of these challenges were faced by those who used models that were based on actual reality. Statistical modelling gradually improved the accuracy of the model’s calibration. Groundwater has become an increasingly important resource for supplying the water requirements of a rising global population. The fact that there is such a large stockpile allows it to be used once again, even during dry seasons or droughts. This article presents a deep convolutional neural network-based model for predicting groundwater levels. As part of the experimental setup, 174 satellite pictures of groundwater are included in the input data set. Images are preprocessed using the CLAHE method. The CNN, SVM, and AdaBoost methods make up the classification model. The results have shown that CNN can classify things correctly 98.5 per cent of the time. Precision and Recall rate of Deep CNN is also better for ground water image classification

PET-CT Surveillance versus Neck Dissection in Advanced Head and Neck Cancer

BACKGROUND: The role of image-guided surveillance as compared with planned neck dissection in the treatment of patients with squamous-cell carcinoma of the head and neck who have advanced nodal disease (stage N2 or N3) and who have received chemoradiotherapy for primary treatment is a matter of debate. METHODS: In this prospective, randomized, controlled trial, we assessed the noninferiority of positron-emission tomography–computed tomography (PET-CT)–guided surveillance (performed 12 weeks after the end of chemoradiotherapy, with neck dissection performed only if PET-CT showed an incomplete or equivocal response) to planned neck dissection in patients with stage N2 or N3 disease. The primary end point was overall survival. RESULTS: From 2007 through 2012, we recruited 564 patients (282 patients in the planned-surgery group and 282 patients in the surveillance group) from 37 centers in the United Kingdom. Among these patients, 17% had nodal stage N2a disease and 61% had stage N2b disease. A total of 84% of the patients had oropharyngeal cancer, and 75% had tumor specimens that stained positive for the p16 protein, an indicator that human papillomavirus had a role in the causation of the cancer. The median follow-up was 36 months. PET-CT–guided surveillance resulted in fewer neck dissections than did planned dissection surgery (54 vs. 221); rates of surgical complications were similar in the two groups (42% and 38%, respectively). The 2-year overall survival rate was 84.9% (95% confidence interval [CI], 80.7 to 89.1) in the surveillance group and 81.5% (95% CI, 76.9 to 86.3) in the planned-surgery group. The hazard ratio for death slightly favored PET-CT–guided surveillance and indicated noninferiority (upper boundary of the 95% CI for the hazard ratio, <1.50; P=0.004). There was no significant difference between the groups with respect to p16 expression. Quality of life was similar in the two groups. PET-CT–guided surveillance, as compared with neck dissection, resulted in savings of £1,492 (approximately $2,190 in U.S. dollars) per person over the duration of the trial. CONCLUSIONS: Survival was similar among patients who underwent PET-CT–guided surveillance and those who underwent planned neck dissection, but surveillance resulted in considerably fewer operations and it was more cost-effective. (Funded by the National Institute for Health Research Health Technology Assessment Programme and Cancer Research UK; PET-NECK Current Controlled Trials number, ISRCTN13735240.

Model Based Software Development: Issues & Challenges

One of the goals of software design is to model a system in such a way that it is easily understandable. Nowadays the tendency for software development is changing from manual coding to automatic code generation; it is becoming model-based. This is a response to the software crisis, in which the cost of hardware has decreased and conversely the cost of software development has increased sharply. The methodologies that allowed this change are model-based, thus relieving the human from detailed coding. Still there is a long way to achieve this goal, but work is being done worldwide to achieve this objective. This paper presents the drastic changes related to modeling and important challenging issues and techniques that recur in MBSD

Positron emission tomography with computed tomography (PET-CT) to evaluate the response of bone metastases to non-surgical treatment

A case of solitary bone metastasis from breast cancer, where MRI assessment of treatment response was inaccurate and whole-body fluorodeoxyglucose (18FDG) positron emission tomography with computed tomography (PET-CT) proved more reliable and objective, is presented

Modified Seagull Optimization With Deep Learning for Affect Classification in Arabic Tweets

Arabic is one of the world&#x2019;s most widely spoken languages, and there is a huge amount of digital content available in Arabic. By the categorization of Arabic documents, it becomes easier to search and access specific content of interest. With the increasing quantity of user-generated content on social media platforms and online forums, text classification becomes important for content filtering and moderation. Text classification on an Arabic corpus has broad applications, ranging from information retrieval and content moderation to sentiment analysis and machine translation. It enables efficient organization, analysis, and utilization of Arabic text data, contributing to various industries and domains. Therefore, this study develops a Modified Seagull Optimization with Deep Learning based Affect Classification on Arabic Tweets (MSGODL-ACAT) technique. The goal of the MSGODL-ACAT approach lies in the recognition and categorization of effects or emotions that exist in Arabic tweets. At the preliminary level, the MSGODL-ACAT technique preprocesses the input data to make the Arabic tweets into a meaningful format. Next, the Glove technique is used for the word embedding process. Moreover, the MSGODL-ACAT technique makes use of the deep belief network (DBN) method for affect categorization. At last, the MSGO algorithm is used for the optimal hyperparameter tuning of the DBN method which in turn enhances the classification results. The experimental evaluation of the MSGODL-ACAT approach is evaluated using Arabic tweets databases. The experimental outcomes signify the effectual performance of the MSGODL-ACAT algorithm over other current approaches

Nonlinear Autoregressive Neural Network for Antimicrobial Waste Water Treatment

Antibiotics become an emerging contaminant and receive more interests due to its ecotoxicological and strong stability in water ecosystems. Antibiotic adsorption onto carbon materials are biochars among the wastewater mechanisms. This research used machine learning (ML) techniques to generate general adsorption forecasting model for sulfamethoxazole (SMX) and tetracycline (TC) on CBM. Dirichlet design parameters and a combined combination of Neumann and Dirichlet boundary situation are applied to the system of differential equations. In addition, the proposed method use the learning under supervision technique of a nonlinear autoregressive for estimating the CO2 concentration and flows in units of rate of a reaction characteristics, an exogenous (NARX) neural network model with two activation functions was used (Log-sigmoid and hyperbolic tangent) and for both the findings of a TC and SMX absorption simulations showed the random forest performed support vector tree and nonlinear autoregressive exogenous neural networks and machine learning methods. Their relevance and complete dependency graph evaluation lead reasonable CBM uses for antimicrobial wastewater treatment. Also, machine learning forecasting model with good generalization capability is useful for building effective CBMs with few empirical screens. It evaluates the accuracy, precision, recall, false positive rate (FPR), and false negative rate (FNR) and also reduces the experimental screening