Volume 12 | Issue 2 |

 Abstract

The human teeth are the hardest structures in the body, with enamel as their outermost layer. Speech is greatly influenced by teeth, which typically emerge by the age of 13. However, by their mid-30s, more than 80% of people in the United States have at least one cavity, making tooth decay one of the most prevalent chronic diseases affecting individuals of all ages. Dental decay, which takes the form of pit and fissure, root, or smooth surface decay, can be avoided with good oral hygiene practices and regular dental cleanings. Periapical lesions can develop due to various causes, such as trauma, inflammation, or differences in the tooth's surrounding tissue, with treatment options varying depending on the lesion's size or location. Endodontics, or root canal therapy, is a dental technique used to treat infections in the tooth's pulp, allowing the preservation of the tooth that might otherwise need to be entirely extracted. Dental disorders are often diagnosed using X-ray imaging, and the segmentation and categorization of dental abnormalities using neural networks are proving beneficial to the dental field. Various machine learning algorithms are used to categorize and identify dental abnormalities, with CNNs being the preferred network architecture for recognizing objects in tasks like image recognition and pixel data processing. In summary, good oral hygiene practices and regular dental check-ups can help prevent tooth decay, while endodontics can save a tooth from extraction. The use of neural networks for dental abnormality diagnosis is proving beneficial to the dental field, with CNNs being the preferred network architecture for recognizing dental abnormalities.

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 Keywords

Tooth anomaly, Root canal, Periapical lesions, Endodontic therapy, Convolutional neural networks, X-ray images, Dental cavities.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Human migration is the movement of people from one place to another with intentions of settling temporarily or permanently at a new location in the country or outside the country. The movement is often occurs over long distances which is the dominant form of human migration globally. It has a high potential to improve human development, and some studies confirm that migration is the most direct route out of poverty. The migration rate with in India between 2020-2021 was almost 29 percent, which means about 26.5 percent of the population in the rural areas of the country were migrants, while this was about 35 percent in urban areas. Females recorded a higher share of migration rate of 47.9%; with 48% in rural and 47.8% in urban areas. Migration rate for males was 10.7%, with 5.9% in rural and 22.5% in urban areas. Migration in India is on the rise because the unemployment rate in India is high (15 per cent), and there are not enough jobs for people who want a job. In Telangana also as per 2011 census nearly 30% or 307 million of population are migrants, majority of them are marginalized sections of the society i.e. SC/ST/OBC/Women who migrates to Hyderabad city in search of employment. This paper attempts to study the impact of MGNREGS on Rural-Out migration among Gond, Koya, Chenchu and Lambada tribes from selected villages in Telangana for the year 2015-16.

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 Keywords

Migration, Human Development, Unemployment, Marginalized Sections,

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Due to their fragility, high expense, and difficulty in performing procedures, brain disorders are among the hardest diseases to cure. However, since the outcomes are unknown, the procedure itself need not be successful.Adults suffering from one of the most prevalent brain diseases, hypertension may experience varying degrees of forgetfulness and memory issues.Because of these factors, it's critical to diagnose Parkinson's disease using brain MRI scans, as well as to assess the patient's degree of deterioration and memory loss.We address techniques and strategies for using deep learning to the diagnosis of Parkinson's in this research.The proposed method is applied to improve patient care, save costs, and facilitate rapid and precise analysis in large-scale studies. Parkinson's diseases progressive neuro degenerative disorder that affects millions of people worldwide. A brain MRI scan has demonstrated potential as a technique for early Parkinson's disease detection and early detection can improve patient outcomes. The analysis of brain MRI scans for Parkinson's disease has been using more and more deep learning techniques, like convolutional neural networks (CNNs), in recent years. An investigation of Parkinson's disease using brain MRI scans is suggested in this research using a CNN-based network. The suggested system consists of multiple steps: preprocessing the data, extracting features, training the CNN model, and assessing the model's performance on a test set.The proposed work outcomes shows how well the suggested CNN-based method performs in correctly identifying Parkinson's from brain MRI data. The suggested method may enhance Parkinson'sdisease early diagnosis and surveillance, which would benefit patients' outcomes.

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Deep Learning ,Parkinson Disease

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 Abstract

This paper presents a groundbreaking approach to classifying lung and colon cancer from high-dimensional histopathological images, employing an advanced hybrid deep learning model. Our dataset encompasses 20,000 high-resolution images across five distinct classes, posing significant challenges in terms of computational efficiency and model accuracy. The dual-CNN structure of our model ensures a comprehensive extraction of both fine-grained and global features, crucial for accurate cancer classification. Through innovative feature reduction techniques, we effectively mitigate the curse of dimensionality, enhancing the model's computational efficiency and robustness. The integration of global average pooling and dense layers with dropout regularization further contributes to the model's performance, preventing overfitting and ensuring generalizability. Our approach achieves an impressive classification accuracy of 99%, demonstrating the model's capability to handle high-dimensional datasets with precision. This work marks a significant contribution to medical image analysis, providing a reliable and efficient solution for cancer classification and setting a new standard in the field.

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 Keywords

Feature reduction,DL,ML,PCA,Rationale,Feature,F1-Score,Recall,Accuracy

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 Abstract

Stiff Person Syndrome (SPS) is a rare neuroimmunology disorder characterized by severe muscle spasms and stiffness, predominantly affecting axial and limb muscles. While the pathophysiology remains unclear, the presence of antibodies against glutamic acid decarboxylase (GAD) is commonly associated with SPS, although the exact role of these antibodies in disease development is still debated. Current treatment options, including pharmacological interventions and immunotherapy, aim to alleviate symptoms and modify the immune response. However, for patients refractory to conventional therapy, hematopoietic stem cell transplantation (HSCT) has emerged as a promising alternative. Autologous HSCT (AHSCT) involves resetting the immune system using the patient's own stem cells and has shown success in improving symptoms and quality of life in SPS patients. This review provides an overview of the epidemiology, clinical manifestations, diagnosis, immunopathogenesis, and current treatment strategies for SPS. Additionally, it discusses the significance and potential of HSCT, particularly AHSCT, in the management of refractory SPS. Further research is warranted to elucidate the long-term efficacy and safety of HSCT in SPS, as well as to explore its role in pediatric-onset cases and its economic implications compared to conventional therapies.

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 Keywords

1. Stiff Person Syndrome (SPS) 2. Neuroimmunological disorder 3. Muscle spasms 4. Glutamic acid decarboxylase (GAD) antibodies

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 Abstract

With growing environmental concerns, renewable energy sources are crucial in the production of electric power. Power electronics converters are used to interconnect renewable energy sources in order to enhance power quality at the common coupling point (CCP). Distribution Static synchronous compensators (DSTATCOM) is creatively used in this study, an interface unit between the grid and renewable energy source, and a technique for real power exchange between the dynamic load system, grid, and renewable energy source. This paper, titled "Analysis of PV-Based DSTATCOM" aims to investigate and evaluate the effectiveness of a Solar integrated Distribution Static Compensator (DSTATCOM) using the ICOS? method. The study will focus on the performance of the DSTATCOM in reducing problems with power quality such as voltage regulation, reactive power compensation power factor improvement and harmonics while integrating renewable energy sources. Through matlab simulation and analysis, this research will offer insightful information on the practical implementation of DSTATCOM systems with PV integration, contributing to improved power quality and the efficient utilization of renewable energy sources in the distribution network.

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 Keywords

Photovoltaic Cell (PV),Synchronous reference frame(SRF), PV-DSTATCOM, power quality (PQ), reference dc link voltage, MPPT technique,Voltage Source Converter

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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Abstract: This chapter delves into the critical role of civil society in bolstering democratic governance. It explores the various mechanisms through which civil society organizations contribute to the vitality and resilience of democratic systems. Drawing on theoretical frameworks and empirical evidence, the chapter highlights the significance of civil society in promoting civic engagement, fostering social cohesion, and holding governments accountable. Additionally, it examines the challenges faced by civil society actors and offers recommendations for enhancing their effectiveness in advancing democratic ideals.

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Keywords: Civil society, democracy, civic engagement, social cohesion, accountability, transparency, challenges, opportunities, recommendations.

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 Abstract

The Rainbow trout (Oncorhynchus mykiss) holds significance in both Indian and global aquaculture. In India, rainbow trout production has witnessed steady growth, particularly in regions with suitable environmental conditions, such as Himachal Pradesh and Jammu and Kashmir. Aquaculture production of rainbow trout is being carried out in several parts of the world (South America, Europe, Asia, Africa and Oceania) outside its native range in North America. According to FAO FishStat, more than 75 countries are officially represented in the Rainbow trout production statistics.Technological advancement in aquaculture practices, selective breeding for imoproved traits, and sustainable farming methods have contributed to increased production efficiency worldwide.

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 Keywords

Rainbow trout; Trout Farming; Trout Production

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In major power systems, the internet of things (IoT) significantly affects raising the quality and efficiency of electricity. Using a unique approach, smart voltage and current monitoring systems have been developed. The integrated design measures voltage and current sensor findings and transmits the processed data to an Android phone using an Arduino Uno microcontroller and a Wi-Fi module. The global systems for mobile communication WI-FI module is necessary for communication. An Android app can be used to alert users via email or text message when something goes wrong and assist them in taking the appropriate action. Logical programming strategies in Arduino allow for easy load times and remote device monitoring and control. This gadget monitors and safeguards the voltage, current, and temperature of a single-phase system. Inadequate monitoring and control can result in equipment failure caused by overloading, overheating, and high input voltage. An electromagnetic relay is triggered to shut down the entire device if any of these values exceed the limit. This relay is activated when the parameters exceed the predetermined threshold levels. In addition, the relay serves as a circuit breaker, cutting off the main power supply. This device provides protection and monitoring for the voltage, current, and temperature of a single-phase system. When equipment is not adequately monitored and managed, it can malfunction due to overloading, overheating, and high input voltage. An electromagnetic relay is triggered to isolate the entire unit in the event that any of these values exceed the threshold. This relay is turned on when the parameters exceed the predetermined threshold levels. Additionally, the relay shuts off the primary power source by acting as a circuit breaker.

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 Keywords

IoT(Internet of Things),Microcontroller, Monitoring, Control, Real time data, Sensor Integration, Voltage monitoring, Current monitoring.

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Creative Commons Attribution 4.0 and The Open Definition