Clustering Algorithm for Energy- Efficient Wireless Sensor Network

Wireless networks data aggregation allows in-network processing, reduces packet transmission and data redundancy, and thus helps extend wireless sensor systems to the full duration of their lives. There have been many ways of dividing the network into clusters, collecting information from nodes and adding it to the base station, to extend wireless sensor network life. Certain cluster algorithms consider the residual energy of the nodes when selecting clusterheads and others regularly rotate the selection head of the cluster. However, we seldom investigate the network density or local distance. In this report we present an energy-efficient clustering algorithm that selects the best cluster heads of the system after dividing the network into clusters. The cluster head selection depends on the distance between the base station nodes and the remaining power of this approach.Each node's residual energy is compared to the node count. Our results show that the solution proposed more efficiently extends the life of the wireless sensor network. The algorithm prolongs the life and effectiveness of the Wireless Sensor Network

High-level and Low-level Feature Set for Image Caption Generation with Optimized Convolutional Neural Network, Journal of Telecommunications and Information Technology, 2022, nr 4

Automatic creation of image descriptions, i.e. captioning of images, is an important topic in artificial intelligence (AI) that bridges the gap between computer vision (CV) and natural language processing (NLP). Currently, neural networks are becoming increasingly popular in captioning images and researchers are looking for more efficient models for CV and sequence-sequence systems. This study focuses on a new image caption generation model that is divided into two stages. Initially, low-level features, such as contrast, sharpness, color and their high-level counterparts, such as motion and facial impact score, are extracted. Then, an optimized convolutional neural network (CNN) is harnessed to generate the captions from images. To enhance the accuracy of the process, the weights of CNN are optimally tuned via spider monkey optimization with sine chaotic map evaluation (SMO-SCME). The development of the proposed method is evaluated with a diversity of metrics

A Comprehensive Review and Open Challenges on Visual Question Answering Models

Users are now able to actively interact with images and pose different questions based on images, thanks to recent developments in artificial intelligence. In turn, a response in a natural language answer is expected. The study discusses a variety of datasets that can be used to examine applications for visual question-answering (VQA), as well as their advantages and disadvantages. Four different forms of VQA models—simple joint embedding-based models, attention-based models, knowledge-incorporated models, and domain-specific VQA models—are in-depth examined in this article. We also critically assess the drawbacks and future possibilities of all current state-of-the-art (SoTa), end-to-end VQA models. Finally, we present the directions and guidelines for further development of the VQA models

ELECTRONIC HEALTH RECORDS ANALYSIS OF LEPROSY PATIENTS USING MACHINE LEARNING TECHNIQUES

<p>Abstract</p><p>Electronic Health Records (EHRs) are rapidly being implemented by health care providers in the recent years. This has given rise to increase in the availability and quality of EHR data.  Leprosy is one of the main public health problems and listed among the neglected tropical diseases in India. It is also called Hansen's Diseases (HD), which could be a long-term contamination by microorganisms, mycobacterium leprae. The delay in the diagnosis of leprosy can lead to increase disability rate among various patients. This paper intends to identify type of leprosy by applying Machine Learning based classification techniques on various leprosy cases from the first sign of symptoms recorded in clinical text included in Electronic Health Records (EHRs). Electronic Health Records (EHRs) of Leprosy patients from verified sources have been generated. The clinical notes included in EHRs have been processed through various Natural Language Processing techniques. In order to predict type of leprosy, Rule based classification method has been applied in this paper. Further the classification results of various Machine Learning (ML) algorithms like Support Vector Machine (SVM), Logistic regression (LR), K-nearest neighbor (KNN) and Random Forest (RF) are compared and their performance parameters are analyzed.</p&gt

High-level and Low-level Feature Set for Image Caption Generation with Optimized Convolutional Neural Network

Automatic creation of image descriptions, i.e. captioning of images, is an important topic in artificial intelligence (AI) that bridges the gap between computer vision (CV) and natural language processing (NLP). Currently, neural networks are becoming increasingly popular in captioning images and researchers are looking for more efficient models for CV and sequence-sequence systems. This study focuses on a new image caption generation model that is divided into two stages. Initially, low-level features, such as contrast, sharpness, color and their high-level counterparts, such as motion and facial impact score, are extracted. Then, an optimized convolutional neural network (CNN) is harnessed to generate the captions from images. To enhance the accuracy of the process, the weights of CNN are optimally tuned via spider monkey optimization with sine chaotic map evaluation (SMO-SCME). The development of the proposed method is evaluated with a diversity of metrics

A Comprehensive Review and Open Challenges on Visual Question Answering Models

Users are now able to actively interact with images and pose different questions based on images, thanks to recent developments in artificial intelligence. In turn, a response in a natural language answer is expected. The study discusses a variety of datasets that can be used to examine applications for visual question-answering (VQA), as well as their advantages and disadvantages. Four different forms of VQA models—simple joint embedding-based models, attention-based models, knowledge-incorporated models, and domain-specific VQA models—are in-depth examined in this article. We also critically assess the drawbacks and future possibilities of all current state-of-the-art (SoTa), end-to-end VQA models. Finally, we present the directions and guidelines for further development of the VQA models

The role of centralized reading of endoscopy in a randomized controlled trial of mesalamine for ulcerative colitis

Background & Aims: Interobserver differences in endoscopic assessments contribute to variations in rates of response to placebo in ulcerative colitis (UC) trials. We investigated whether centralized review of images could reduce these variations. Methods: We performed a 10-week, randomized, double-blind, placebo-controlled study of 281 patients with mildly to moderately active UC, defined by an Ulcerative Colitis Disease Activity Index (UCDAI) sigmoidoscopy score ≥2, that evaluated the efficacy of delayed-release mesalamine (Asacol 800-mg tablet) 4.8 g/day. Endoscopic images were reviewed by a single expert central reader. The primary outcome was clinical remission (UCDAI, stool frequency and bleeding scores of 0, and no fecal urgency) at week 6. Results: The primary outcome was achieved by 30.0% of patients treated with mesalamine and 20.6% of those given placebo, a difference of 9.4% (95% confidence interval [CI], -0.7% to 19.4%; P =.069). Significant differences in results from secondary analyses indicated the efficacy of mesalamine. Thirty-one percent of participants, all of whom had a UCDAI sigmoidoscopy score ≥2 as read by the site investigator, were considered ineligible by the central reader. After exclusion of these patients, the remission rates were 29.0% and 13.8% in the mesalamine and placebo groups, respectively (difference of 15%; 95% CI, 3.5%-26.0%; P =.011). Conclusions: Although mesalamine 4.8 g/day was not statistically different from placebo for induction of remission in patients with mildly to moderately active UC, based on an intent-to-treat analysis, the totality of the data supports a benefit of treatment. Central review of endoscopic images is critical to the conduct of induction studies in UC; ClinicalTrials.gov Number, NCT01059344. © 2013 by the AGA Institute