Cause Effect Analysis of Ecological Pollutants on Internal Physique of Human Subjects Using Radial Recurrent Neural Network ApproachPimpale, Y; Gupta, S; Kanday, R
doi: 10.1088/1742-6596/2327/1/012066pmid: N/A
Globally, ecosystems are changing at an unprecedented rate. Ecosystem management include natural resources and the biophysical environment, but it also requires consideration of all anthropogenic aspects, including social, economic, and cultural factors. Environmental factors are thought to be responsible for almost half of the worldwide burden of disease. Ecosystem changes are increasingly recognized as having an impact on human health and playing an important part in the onset and re-emergence of an expanding variety of diseases. Ecological and environmental Imbalances negatively affect on human health, food security and global economic geopolitical stability. In this study, a cohort-based data set of Ecological pollutants and Physiological signals such as ECG and anthropogenic data of human subjects were extracted from Maharashtra. A hazard ratio based on neural networks was developed and found to be deplorable in both the unhealthy and healthy categories of human individuals. This research is crucial in shedding insight on the influence of interactions between natural and anthropogenic variables on human health. Such initiatives might contribute to a better knowledge of the human health consequences of accelerated environmental change, as well as better decision-making in the fields of environmental conservation, public health policy, and new management framework designs.
An Approach to Fabric Defect Detection using Statistical Methods for Feature ExtractionPatil, Girish; Deshmukh, S. M.
doi: 10.1088/1742-6596/2327/1/012033pmid: N/A
Quality control in any commodity at every stage of production not only is desired but its reproduction day in and day out is essential in this fast changing world especially when we see that shopping from a digital screen is the new trend in this era of pandemic. Quality check, control and inspection demands manual checking where in the monotonous work can make a human negligible towards work and lead to reduction in quality control. The world demands replacement of such laborious work which can only be promised by machinery custom tailored from the needs of providing accuracy, time reduction in quality checks as well as production and most importantly reduction in labour cost when commodity manufacturing is concerned. One such commodity that we cover in this paper is the textile industry where in automated fabric defect detection is harnessed as the research trend across the world with the help of image processing combined with machine learning algorithms is concerned. This area of research is challenging as the algorithm needs to cover robustness, efficiency and particularly the variety of classifiable defects in terms of surety and complexity. Out of the various techniques utilised for fabric defect detection we have utilised a threshold based image processing technique that can provide an automated stepping stone for atomization in the fabric defect detection for quality control.
Detection of Lung cancer using Digital image Processing techniques and Artificial Neural NetworksReddy, Badireddygari Anurag; Mandal, Danvir
doi: 10.1088/1742-6596/2327/1/012078pmid: N/A
In today’s life digital image processing plays keys role. Digital Image processing using in medical field changes the medical world drastically. Medical imaging is growing speedily due to developments in image processing techniques including image recognition, analysis and enhancement. Now a day’s different types diseases that have facing humans in daily life. In this Lung cancer is one of the major diseases that show devastating in humans heavily. Lung cancer is one of the major causes of death in humans. Lung cancer is normally carried out by trained professionals and these are majorly helpful in early stage of detection. This detection method introduces the possibility of human errors which consequences an automated process. In this paper it is mainly aims to detect the lung cancer at an early stage through an automated process and reduces the human errors and gets the accurate results. There are different types of processing techniques are currently using. Artificial neural network processing plays important role to identify the cancer tissues which are in lungs. Early stage of detection will save the human life.
Wireless sensor networks for forest fire monitoring: Issues and ChallengesSalaria, Anshika; Singh, Amandeep; Sharma, Kamal Kumar
doi: 10.1088/1742-6596/2327/1/012030pmid: N/A
Forest fires have recently been the most critical issue faced by the world. The huge environmental, economic, and societal damages caused by wildfires are hindrances to social development. The increasing figures of such instances since the last few years make it the need of the hour to start considering it on priority and take appropriate actions. For this, intense knowledge is required about the nature of forest fires, their causes and appropriate technology to be used. Wireless sensor networks have always been a preferable technology in such kind of disaster management and environmental monitoring scenarios. However, there are yet some prominent issues that affect the overall performance of wireless sensor networks, especially in harsh terrains like forests. In this paper, information has been provided about the causes, damages and aftereffects of forest fires. Moreover, a study has been conducted on various articles highlighting the issues and challenges being faced in wireless sensor networks. The paper further provides a simple and easily understandable analysis highlighting the types and priority of challenges. This would benefit the researchers in identifying the current research gaps in the field of wireless sensor networks, especially in applications like forest fire monitoring or environmental monitoring and disaster management.
Understanding and Visualization of Different Feature Extraction Processes in Glaucoma DetectionKrishna, Nanditha; Nagamani, K
doi: 10.1088/1742-6596/2327/1/012023pmid: N/A
In the recent years the usage of mobile phone is increased and it is the major reason for cause of vision loss in several people. The continuous usage increases pressure inside optic nerve head and it leads to glaucoma disease. Also, there are lot of other reasons which leads to the cause of glaucoma. The purpose of this paper is to determine the importance of feature extraction process in glaucoma detection and implementation of different techniques for extracting convenient features for training machine learning model using pre-processed OCT (Optical Coherence Tomography) images. The two major feature extraction techniques narrated in this paper are convolutional neural network (CNN) model-based feature extraction and image processing model-based feature extraction. A performance analysis was conducted to find best feature extraction technique and both techniques performed well.
To evaluate the performance of machine learning algorithms in predicting student dropout on MOOC platformsKumar, Gaurav; Singh, Amar; Sharma, Ashok
doi: 10.1088/1742-6596/2327/1/012063pmid: N/A
Online learning using Massive Open Online courses(MOOCs) has gained a lot of hype in recent years due to its great potential in having the widest reach in delivering the state-of-the-art resources to the unlimited number of online learners without limiting itself to any geographical boundary. Along with gaining popularity, MOOCs have been facing challenges like high attrition or dropout rate since its birth. The main motivating factor behind the study is to fill the gap which has been there because of very limited literature available there to find the real cause behind these challenges. The current study tries to find the solution of the said challenges by finding the significant contributing factors which highly affect the target variable in the study which is number of certified students in this case. The dataset used in this paper is publicly available in dataverse repository of Harvard university. The dataset is a compilation of student clickstream log data consisting of 641138 instances of enrolled students in various MOOC courses of Harvard and MIT. The study evaluates machine learning models like logistic regression, decision tree, random forest, K-Nearest Neighbor to determine their efficiency in predicting the student dropout. The results of this study can be used to create a framework for recommending necessary actions to the at-risk students to reduce the dropout rate.
Retraction: Brain Tumor Detection Using Artificial Convolutional Neural Networks (J. Phys.: Conf. Ser. 2327 012077)doi: 10.1088/1742-6596/2327/1/012080pmid: N/A
This article has been retracted by IOP Publishing following an allegation that the work contains tortured phrases [1].IOP Publishing has investigated and agrees the article contains a number of nonsensical phrases that feature throughout the paper, to the extent that the article makes very little sense. This casts serious doubt over the legitimacy of the article and/or expertise of the authors in this topic.It suggests the article may have been created at least partly by artificial intelligence or translation software. IOP Publishing wishes to credit PubPeer commenters [2] for bringing the issue to our attention.The authors have neither agreed nor disagreed with this retraction.[1] Cabanac G, Labbe C, Magazinov A, 2021, ‘Tortured phrases: A dubious writing style emerging in science. Evidence of critical issues affecting established journals‘, arXiv:2107.06751v1[2] https://pubpeer.com/publications/A14E1032D10D92F0D1188D56DA0CDBRetraction published: 03 February 2023
A Critical Review on Hand Gesture Recognition using sEMG: Challenges, Application, Process and TechniquesKumar, Davinder; Ganesh, Aman
doi: 10.1088/1742-6596/2327/1/012075pmid: N/A
Hand gesture recognition systems are gaining popularity these days due to the ease with which humans and machines can communicate. The goal of hand gesture development is to improve interactions between humans and computers for the purpose of transmitting ideas. In a typical HGR systems, the main steps followed are, data collection, pre-processing, feature extraction and classification. For every stage, a significant number of techniques are available with various other sub steps. This study gives an overview of modern hand gesture recognition techniques, its Physiological and Anatomical Background, working and challenges faced by these systems. Moreover, the role of artificial intelligence in optimizing the performance of HGR systems is also delineated in this paper. Also, the precision and accuracy of the HGR approaches gets affected by the complexity and diversity of various hand movements, therefore, the need for implementing AI based ML and DL methods keeps on rising. Keeping this in mind, the performance of various ML algorithms in recognizing the visual and sensor-based hand gestures is investigated. Moreover, the commonly utilized framework in detecting hand gestures has been explored in numerous standard datasets.
An Approach to Pattern Recognition for Identification of Devnagari Script Based on Fingertips and PalmTantarpale, Ms. Sharvari; Deshmukh, C. N.
doi: 10.1088/1742-6596/2327/1/012032pmid: N/A
In this paper, we present an finger point based signed language symbol to text identification and classification algorithm that is based upon RGB image datasets. The palm sized images based upon different sizes, backgrounds, orientation are captured to be preprocessed as per the requirements of developing a convolution neural network based algorithm. This algorithm utilizes Alexnet for the preprocessing requisites where in 47 symbols of Devanagari script are augmented based on the reference rulebook created for our requirements as highlighted in the paper. At the primary level this algorithm provides an excellent classification rate which promises upliftment for our research in the upcoming future. We have provided detailed steps and discussion on the classification parameters considered for our algorithm which is implemented on MATLAB platform with the help of machine learning solution libraries.
Performance Investigation of Back-Compatible Integrated TWDM/GPON System Using MDM And Pulse ShapesSingh, Ajmer; Bhogal, Rosepreet Kaur
doi: 10.1088/1742-6596/2327/1/012036pmid: N/A
Optical access networks are the prominent and promising cutting edge technology to provide high speed, and cost effective operation for meeting the requirements of ever-increasing demands of users. Multiple access techniques such as Wavelength and time division in passive optical networks are important innovations to provide high capacity. A mode division multiplexed back compatible integrated next generation PON2 (TWDM PON) and GPON is demonstrated with triple play supportability in this work. Proposed system offer pay as you grow feature and also provide enhanced performance by suppressing the inter-channel interference in TWDM-PON2. System successfully works on same optical distribution network unit for both PON standards without extra arrangements and has potential to support 62.5 Gbps in full duplex mode (Symmetrical downlink/uplink). Moreover, demonstrated back compatible system is analyzed by incorporating diverse modulations such as NRZ-DPSK, RZ-DPSK, RZ-DQPSK, NRZ-DPSK and linecodings NRZ, RZ. Received power, Optical signal to noise ratio (OSNR), BER and Q factor are noted at diverse link lengths and it is noteworthy that LP modes with NRZ in TWDM PON provide outstanding results.