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

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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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Migration, Human Development, Unemployment, Marginalized Sections,

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

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

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Introduction: Adverse drug reactions (ADRs) represent a significant concern for patient safety and necessitate robust monitoring and reporting mechanisms. This systematic literature review delves into the landscape of ADR reporting, with a specific focus on leveraging the electronic health record (EHR) as a surveillance tool. Despite existing guidelines, variability in reporting standards persists among healthcare facilities worldwide. Methods: A comprehensive search encompassing PubMed and the Cochrane Database of Systematic Reviews was conducted, targeting original articles and reports from reputable organizations. The review explores challenges encountered in ADR reporting, including under-reporting rates and interinstitutional variability. Potential strategies for improvement, such as direct reporting by consumers and enhanced healthcare provider education, are examined. Results: Despite concerted efforts to promote ADR reporting, persistent barriers remain, including inadequate knowledge among healthcare professionals and logistical challenges in establishing robust reporting systems. Direct consumer reporting initiatives have shown promise in certain countries but warrant further investigation. Additionally, while the EHR presents promising opportunities for ADR monitoring, issues such as lack of standardization and alert fatigue hinder its effectiveness in practice. Discussion: Addressing barriers to ADR reporting is imperative to strengthen pharmacovigilance systems and uphold patient safety standards. The review underscores the importance of standardized reporting practices, technological advancements, and targeted educational interventions to optimize ADR monitoring and reporting efficacy. Conclusion: By embracing targeted interventions and capitalizing on technological innovations, healthcare systems can bolster their capacity to detect, report, and mitigate ADRs effectively. This proactive approach not only enhances patient safety but also optimizes pharmacotherapy outcomes, ultimately advancing public health objectives.

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Keywords: Adverse drug reactions (ADRs), Pharmacovigilance, Electronic health record (EHR), Under-reporting, Healthcare provider education, Patient safety

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Automatic text summarization plays a crucial role in information retrieval and natural language processing tasks. This research project focuses on developing a web-based application for multilanguage text summarization. The application utilizes the Flask framework and leverages the capabilities of the SpaCy library for language processing. The application allows users to input text in multiple languages, including English, Hindi, Kan- nada, and Malayalam, and generates a concise summary of the provided text. The summarization process involves tokenizing the input text, removing stop words and punctuation, and identifying key sentences for the summary. The system employs different SpaCy language models based on the selected language for accurate language-specific processing. The application supports an intuitive user interface that enables users to interact with the system easily. Users can input their text, select the desired language, and obtain a summary of the input text. The system provides valuable assistance in quickly extracting important information from documents and texts, saving time and effort for users who need to process large volumes of information. The evaluation of the application demonstrates its effectiveness in generating informative summaries across multiple languages. The performance of the system is evaluated based on various metrics such as summary length, similarity to the source text, and overall coherence. The results indicate that the application produces concise and meaningful summaries, making it a valuable tool for information extraction in diverse linguistic contexts. Overall, this research project showcases the development of a web-based multi-language text summarization application that effectively summarizes text in English, Hindi, Kannada, and Malayalam languages. The application provides a user-friendly interface and demonstrates promising performance in generating accurate and concise summaries. Future enhancements may involve expanding language support, refining the summarization algorithms, and incorporating advanced natural language processing techniques to improve the overall summarization quality and user Page 1 experience

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 Keywords

Multilingual NLP ,Text Summarization, Spacy, Flask ,Natural Language Processing, Web Application ,ROUGE Evaluation, Language Processing, Machine Learning, Python ,Web Development

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ABSTRACT Introduction: Pregnancy is the term used to describe the period in which a fetus develops inside a woman's womb or uterus.The third stage of labor has traditionally been defined as the time between the birth ofthe baby and the delivery of the placenta and membranes. It is the third stage that is the most perilous for the woman because of the risk of postpartum hemorrhage (PPH). Methods: A Quasi Experimental was carried out on 100 b.sc nursing 3rd year students to assess the effectiveness of self instructional module. Assessment of knowledge among students regardingactive management of third stage of labor was conducted through questionnaire before Intervention, then self instructional module was administered for 15days. Post test was conducted on 16th day.Result: study result revealed that self instructional module was effective to increase the knowledge of students. Conclusion:As per findings of the present study, self-instructional module was valuable in increasing knowledge regarding active management of 3rd stage of labor among B.Sc. 3rd year nursing students. Key words: knowledge, self instructional module, active management of third stage of labour.

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Key words: knowledge, self instructional module, active management of third stage of labour.

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Plant-derived herbal remedies provide an effective means for treating diseases difficult to treat by other systems of medicines; hepatic disease is one such disease. Exploring and establishing the scientific basis for the therapeutic action of herbal plant medicines is essential. In this research work, the stembark of Bombax ceiba Linn. was evaluated for hepatoprotective activity. The selection of the plant was based on its previously reported literature of antioxidant potentials due to flavonoid contains. Dried stem bark was extracted by various solvents in a soxhlet apparatus and evaluated for pharmacognostic profile. To estimate hepatoprotective potential, stem bark extract of Bombax ceiba carbon tetra chloride induced liver narcosis rat model was selected, and the acetone extract of B. ceiba was administered orally. Radical scavenging potential, total bilirubin count, serum enzyme levels, and histopathological study of the liver were selected as evaluating parameters. Results showed a significant reversal of (p<0.05) hepatotoxicity compared to diseased animals. However, no significant difference was observed in animals treated with the standard Sylimarin . Study concludes the hepatoprotective potential of acetone extract of B. ceiba stembark.

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Keywords: B.ceiba, shalmali, hepatoprotective, flavonoids, antioxidant

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Over the passage of time, there has been an enormous growth in the rise of E-Commerce platforms in India as it has brought numerous benefits, such as better consumer access to a wide range of goods and services with the convenience of online shopping. Hence, it has become imperative for businesses to protect their brand in the digital world through trademark protection. As the buying and selling of goods and services takes place via electronic means thus, it is being termed as e-commerce. It creates a direct link between the manufacturers/sellers and consumers, making it convenient for distribution and ensuring free and continuous transfers. However, the prevalence of counterfeit products, parallel importation of goods, sale of defective products, etc., has led to trademark infringements in these direct transactions which has in turn hindered the growth of e-commerce in India and is gravely affecting the digital economy as well as the overall integrity of the online marketplace. Counterfeited goods refer to the act of producing emulated goods that provide a much degraded quality and are manufactured and sold under another company's trademark without any prior consent or approval. This is done to deceive consumers into believing that they are purchasing genuine products. When such goods are sold on e-commerce portals, they bear unauthorized reproductions of the registered trademarks. This plays with the distinctiveness of the original brands and puts the consumers in a dilemma leading to trademark infringements. The anonymity of these online transactions makes it a challenge to identify the parties and hold them accountable for their actions. E-commerce portals provide a vast range of access and ease for such producers to set up online shops, which not only reduces the verification process but targeting audiences also get easy by way of e-commerce platforms which in turn, amplifies the impact of their illicit acts. While the 2009 amendment to Information Technology Act, 2000 clarified intellectual property rights issues in the cyber law, the unresolved issue of secondary liability for a third-party trademark infringement poses a significant challenge. Hence, by way of a non- doctrinal research the researcher aims to delve into the concept of secondary liability of e-commerce platforms in India, the US and the UK by doing a comparative analysis of the judicial pronouncements given in this matter, to identify the responsibilities and liabilities of these e-commerce portals when it comes to selling and offering of counterfeited products. Collaboration between the e-commerce portals, rights holders and government agencies can prove to be very effective in curbing counterfeiting. With the help of growing AI, bots can detect and prevent the listing of such counterfeit goods. By way of these technologies, seller behaviors and product specifications can be studied and analyzed, thereby decreasing the chances of trademark infringement. The research explains the twisted challenges of counterfeiting, shedding light on the complexities of protecting trademark rights in the online marketplace. Through this study, proposed amendments to existing legislation will be examined to curb trademark infringement on e-commerce portals. The result underscores the need for a solidly built enforcement mechanism, associated efforts between the stakeholders, law enforcement agencies, intellectual property rights holders, etc., and the enhanced consumer awareness campaigns to control this rapidly growing menace.

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 Keywords

Trademark Infringement, Counterfeited goods, E-commerce, Secondary Liability, Intellectual Property Rights Holders.

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This study assessed the degree of environmental safeguarding on the practices, obstacles, and scope of implementation in combating the dumping and illegal trafficking of electronic trash. The study employed a descriptive survey method to examine the practices, challenges, and level of implementation regarding e-waste management (specifically dumping and illicit trade). The research involved 45 participants who were employed in the City Environmental and Natural Resources Office (CENRO), Environmental Management Bureau (EMB), and Metro Manila Development Authority (MMDA). The study revealed that efforts to combat the disposal of electronic trash were frequently successful, whereas the illegal trade of electronic garbage was occasionally successful. Assessing the composite of difficulties related to e-waste dumping and illicit trade yielded positive results. Furthermore, on the level of enforcement against the dumping and illegal trafficking of e-waste, both received consistent verbal affirmation. In summary, the behavior of individuals in building new social norms for e-waste trade, use, and dumping can be influenced by personal qualities and the perceived effectiveness of the outcomes. The researcher suggests the establishment of e-waste facilities and environmental systems and aligning extension activities with research findings utilizing qualitative or mixed design.

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Environmental Protection, Dumping and Illicit Trade of Electronic Waste, Practices, Implementation, Challenges

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Marketing of Handloom fabrics in Manipur is done by weavers themselves, master weavers, middlemen, co-operative societies and also by marketing organizations, Handloom houses of Apex, All India Handlooms Fabric Marketing Co-operative Societies and sales and showrooms of Handicrafts and Handloom Export Corporation etc., The handloom sector plays an important role in the economic development and livelihood of the rural poor in the state. It contributes significantly by generating more employment opportunities and providing bread to the rural poor. Mostly in the state handloom market is done in the local markets. The market is an unorganized one with no control over buying, selling and pricing of the product. There is almost no promotion activity for the handloom products, which is also one of the reasons why the market is not expanding to a wider scale. The article is taken up in order to study about some of the commonly produced handloom fabrics and the present marketing system adopted by the weavers in the local markets of the State.

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handloom, marketing, Manipur, weavers, product

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Creative Commons Attribution 4.0 and The Open Definition

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In the domain of distributed relational databases, efficient query optimization stands as a cornerstone for achieving peak performance. As data spans across multiple nodes, the intricacy of query handling escalates, emphasizing the pivotal role of optimization methods in curtailing execution time and resource consumption. This paper presents a thorough examination of query optimization strategies within distributed relational databases, encompassing both centralized and distributed approaches. We explore a spectrum of techniques, such as cost-based optimization, parallel query processing, and adaptive query processing, elucidating their advantages, constraints, and suitability across diverse distributed database frameworks. Moreover, we address emerging trends and hurdles in query optimization within distributed systems, offering insights into potential paths for future research and advancement.

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Query Optimization, Relational Databases, Partitioning, Challenges

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This analysis delves into the myriad qualities exhibited by Moringa oleifera, commonly recognized by its colloquial name, the drumstick tree. The plant, rich in essential phytochemicals, is native to Africa and Asia, thriving in tropical and sub-tropical regions. Renowned for its nutritional richness, Moringa is abundant in vitamins, minerals, and antioxidants, demonstrating its efficacy in various beneficial roles, including anti-inflammatory, antidiabetic, antimicrobial, and antihypertensive activities. The chemical constituents of Moringa oleifera include Vitamins, Carotenoids, Tannins, Saponin, Phenolic acid, and Minerals, contributing to its health-promoting properties. Examining pharmacological aspects, Moringa oleifera demonstrates potential in Anti-inflammatory, Antidiabetic, Antimicrobial, Antihypertensive, Analgesic, Anticancer and Antispasmodic activities. These findings suggest a broad spectrum of health benefits, though further research is crucial for real-world applications. Understanding the plant's versatility offers insights into its potential contributions to various industries and health domains.

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Moringa oleifera, Chemical consituent, Antioxidant, Anti-inflammatory, Health

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Creative Commons Attribution 4.0 and The Open Definition

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This review paper explores the comprehensive integration of advanced artificial intelligence (AI) techniques into online marketplaces, with a primary focus on addressing challenges encountered by businesses, particularly small enterprises. Motivated by the imperative need for a tool that enables small businesses to scale effectively through the incorporation of the latest technological advancements, the project seeks to democratize access to AI. Employing a multifaceted approach, it strategically tackles the long-tail problem pervasive in online commerce. Techniques such as Product Sentiment Analysis, Opinion Mining, Sales Prediction, Dynamic Pricing, Product Recommendation, and Customer Segmentation collectively contribute to mitigating challenges associated with diverse product catalogs, enhancing overall efficiency, and improving the user experience of online marketplaces. Customer Segmentation employs K-means Clustering to group customers based on similar characteristics, enabling tailored marketing strategies, and enhancing personalization. Dynamic pricing, addressed through Linear Regression, models relationships influencing pricing dynamics, facilitating optimal dynamic pricing based on relevant input variables. Opinion Mining for Products leverages RoBERTa, a state-of-the-art natural language processing model, to extract nuanced opinions and sentiments from product reviews. Product Recommendation utilizes K-nearest neighbors with Means to provide a personalized and user-centric recommendation system, contributing to an enhanced customer experience. Sales Prediction employs Time Series analysis to analyze historical sales data, identify patterns, and make informed predictions about future sales trends, ultimately improving decision- making processes and optimizing inventory management. The integration of these diverse AI modules into a cohesive backend infrastructure ensures seamless communication and interoperability. The creation of an admin panel for real-time statistics serves as a centralized hub for stakeholders to visualize key metrics and make data-driven decisions, facilitating efficient oversight and management of AI-driven functionalities within the online marketplace. In conclusion, this transformative project represents a novel approach to empower small businesses through the strategic integration of AI techniques, effectively addressing critical challenges and leveraging advanced methodologies to revolutionize online commerce and make AI-driven optimization accessible and impactful for businesses navigating the complexities of the digital landscape.

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Deep Learning, Machine Learning, E-Commerce, Artificial Intelligence, Sentiment Analysis, Opinion Mining, Product Recommendation, Dynamic Pricing, Sales Prediction, Market Segmentation, Customer Segmentation, Prescriptive AI, Business Intelligence, Neural Networks, Support Vector Machines, Transformers, K-Means Clustering, Time-Series Forecasting, Time-Series Analysis, Linear Regression.

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Creative Commons Attribution 4.0 and The Open Definition