Comparative Analysis of Artificial Intelligence for Indian Legal Question Answering (AILQA) Using Different Retrieval and QA Models

Legal question-answering (QA) systems have the potential to revolutionize the way legal professionals interact with case law documents. This paper conducts a comparative analysis of existing artificial intelligence models for their utility in answering legal questions within the Indian legal system, specifically focusing on Indian Legal Question Answering (AILQA) and our study investigates the efficacy of different retrieval and QA algorithms currently available. Utilizing the OpenAI GPT model as a benchmark, along with query prompts, our investigation shows that existing AILQA systems can automatically interpret natural language queries from users and generate highly accurate responses. This research is particularly focused on applications within the Indian criminal justice domain, which has its own set of challenges due to its complexity and resource constraints. In order to rigorously assess the performance of these models, empirical evaluations are complemented by feedback from practicing legal professionals, thereby offering a multifaceted view on the capabilities and limitations of AI in the context of Indian legal question-answering

Machine learning-based Naive Bayes approach for divulgence of Spam Comment in Youtube station

In the 21st Century, web-based media assumes an indispensable part in the interaction and communication of civilization. As an illustration of web-based media viz. YouTube, Facebook, Twitter, etc., can increase the social regard of a person just as a gathering. Yet, every innovation has its pros as well as cons. In some YouTube channels, a machine-made spam remark is produced on that recordings, moreover, a few phony clients additionally remark a spam comment which creates an adverse effect on that YouTube channel.  The spam remarks can be distinguished by using AI (artificial intelligence) which is based on different Algorithms namely Naive Bayes, SVM, Random Forest, ANN, etc. The present investigation is focussed on a machine learning-based Naive Bayes classifier ordered methodology for the identification of spam remarks on YouTub

Machine learning-based Naive Bayes approach for divulgence of Spam Comment in Youtube station

In the 21st Century, web-based media assumes an indispensable part in the interaction and communication of civilization. As an illustration of web-based media viz. YouTube, Facebook, Twitter, etc., can increase the social regard of a person just as a gathering. Yet, every innovation has its pros as well as cons. In some YouTube channels, a machine-made spam remark is produced on that recordings, moreover, a few phony clients additionally remark a spam comment which creates an adverse effect on that YouTube channel.  The spam remarks can be distinguished by using AI (artificial intelligence) which is based on different Algorithms namely Naive Bayes, SVM, Random Forest, ANN, etc. The present investigation is focussed on a machine learning-based Naive Bayes classifier ordered methodology for the identification of spam remarks on YouTub

HR Process Automation: A Bibliometric Analysis

Automation is interpreted as the replacement of manual operations by electronics and computer-controlled systems. Human resource management is an indispensable part of every firm be it the space of retail, healthcare, education or any other sector. Activities such as hiring new workers, training, or making sure that local labour laws are obeyed with HR processes and are a crucial part of every organisation. HR has typically been believed of as an extremely manual department procedure. Employees are accustomed to doing this manually and getting the job done themselves. But everything around the HR processes are changing rapidly. HR Automation is a tool for increasing the efficiency of an employment organisation by freeing employees from tedious repetitive tasks and allowing them to focus on more complex assignments such as decision-making and strategy creation. Automation is interpreted as the replacement of manual operations by electronics and computer-controlled systems. By automating regular and routine HR tasks, organisations may lead to significant savings and resources they expend on manual HR processing and preparation. The HR space is being invaded by automation, and any automation that can be implemented will be implemented very quickly. This article is written with the help of Scopus, Web of Science, Google Scholar and Crossref databases. This article will be useful for upcoming researchers, students and managers in the field of HRM across the world

Impact of India’s Plant Variety Protection Act: Analytical Examination Based on Registrations Under The Act

131-139Mandated by TRIPS, India constructed a sui generis system for protection of plant variety, ‘The Protection of Plant Variety and Farmers Right Act, 2001’. The primary objective of the study is to examine the impact of this Act in strengthening the agriculture ecosystem. The paper addresses this by analysing the different attributes of registrations under this Act i.e. types of crops registered, their registration types, applicants affiliation, etc. It further explores the implications of these registrations. The paper also examines the salient and distinctive aspects of the Act such as provisions for supporting farmer and crop diversity, incentive to breeders. The registrations undertaken under different categories show positive trends in terms of crops registered, involvement of different stakeholders, etc. New crop registration shows promise for introduction of new improved varieties. Active involvement of private entities in registering plant varieties underscores that the Act is providing incentives for them for development of new varieties. India’s agriculture export exhibit linkage with new crop varieties being registered. New crops varieties can create market monopoly and help strengthen India’s agriculture exports. Lack of farmers involvement and the limited role of state agriculture universities in development of new varieties is however a cause of concern

Impact of India’s Plant Variety Protection Act: Analytical Examination Based on Registrations Under The Act

Mandated by TRIPS, India constructed a sui generis system for protection of plant variety, ‘The Protection of Plant Variety and Farmers Right Act, 2001’. The primary objective of the study is to examine the impact of this Act in strengthening agriculture ecosystem. The paper addresses this by analysing the different attributes of registrations under this Act i.e. types of crops registered, their registration types, applicants affiliation, etc. It further explores the implications of these registrations. The paper also examines the salient and distinctive aspects of the Act such as provisions for supporting farmer and crop diversity, incentive to breeders. The registrations undertaken under different categories show positive trends in terms of crops registered, involvement of different stakeholders, etc. New crop registration shows promise for introduction of new improved varieties. Active involvement of private entities in registering plant varieties underscores that the Act is providing incentives for them for development of new varieties. India’s agriculture export exhibit linkage with new crop varieties being registered. New crops varieties can create market monopoly and help strengthen India’s agriculture exports. Lack of farmers involvement and the limited role of state agriculture universities in development of new varieties is however a cause of concern

Semantic Segmentation of Legal Documents via Rhetorical Roles

Legal documents are unstructured, use legal jargon, and have considerable length, making them difficult to process automatically via conventional text processing techniques. A legal document processing system would benefit substantially if the documents could be segmented into coherent information units. This paper proposes a new corpus of legal documents annotated (with the help of legal experts) with a set of 13 semantically coherent units labels (referred to as Rhetorical Roles), e.g., facts, arguments, statute, issue, precedent, ruling, and ratio. We perform a thorough analysis of the corpus and the annotations. For automatically segmenting the legal documents, we experiment with the task of rhetorical role prediction: given a document, predict the text segments corresponding to various roles. Using the created corpus, we experiment extensively with various deep learning-based baseline models for the task. Further, we develop a multitask learning (MTL) based deep model with document rhetorical role label shift as an auxiliary task for segmenting a legal document. The proposed model shows superior performance over the existing models. We also experiment with model performance in the case of domain transfer and model distillation techniques to see the model performance in limited data conditions.Comment: 19 pages, Accepted at Natural Legal Language Processing Workshop, EMNLP 202

Preparation and Evaluation of Sodium Alginate Microparticles using Pepsin

Aim: The main aim of this article is to prepare and evaluate sodium alginate microparticles and evaluate on the basis of their characterization. The drug is dissolved, encapsulated or attached to a microparticles matrix. Depending upon method of preparation microparticles were obtained. Microparticles were developed as a carrier for vaccines and other disease like rheumatoid arthritis, cancer etc. Microparticles were developed to increase the efficacy of active pharmaceutical ingredient to a specific targeted site. Material and Method: Microparticles of Sodium Alginate, Pepsin and Calcium Chloride were prepared in six batches (A-F) with different ratio of sodium alginate and calcium chloride respectively i.e. (0.25:2.5), (0.25:5), (0.25:7.5), (0.5:2.5), (0.5:5), (0.5:7.5) by using a homogenizing method. Microparticles were evaluated for particle size distribution, zeta potential and morphology. Result and Discussion:  The normal particle size of each of the six batches were analyzed by Zeta Sizer (Delsa C Particle Analyzer) and it was found that the Batch B (0.25:5) delivered the best microparticles with size distribution of 1.2731 (µm). All batches were seen under Motic magnifying microscope by using the Sulforhodamine B (M.W. 479.02) color as staining dye. Microparticles was found to be semi spherical in shape. Conclusion: Results of all the six batches was contrasted based on particle size investigation, zeta potential and morphology. Batch B (0.25:5) was considered as the best formulation. Key words:  Micro Particle, Pepsin, Sodium Alginate and Calcium Chloride, Sulforhodamine B, Zeta Sizer

Search for new particles in events with energetic jets and large missing transverse momentum in proton-proton collisions at root s=13 TeV

A search is presented for new particles produced at the LHC in proton-proton collisions at root s = 13 TeV, using events with energetic jets and large missing transverse momentum. The analysis is based on a data sample corresponding to an integrated luminosity of 101 fb(-1), collected in 2017-2018 with the CMS detector. Machine learning techniques are used to define separate categories for events with narrow jets from initial-state radiation and events with large-radius jets consistent with a hadronic decay of a W or Z boson. A statistical combination is made with an earlier search based on a data sample of 36 fb(-1), collected in 2016. No significant excess of events is observed with respect to the standard model background expectation determined from control samples in data. The results are interpreted in terms of limits on the branching fraction of an invisible decay of the Higgs boson, as well as constraints on simplified models of dark matter, on first-generation scalar leptoquarks decaying to quarks and neutrinos, and on models with large extra dimensions. Several of the new limits, specifically for spin-1 dark matter mediators, pseudoscalar mediators, colored mediators, and leptoquarks, are the most restrictive to date.Peer reviewe