Neural Network With Nlp

This thesis is about neural networks and how their algorithmic systems work. Neural networks are well-suited to aiding people with complex challenges in real-world situations. Thesis topics include nonlinear and complicated interactions between inputs and outputs, as well as making inferences, discovering hidden links, patterns, and predictions, and modeling highly volatile data and variations to forecast uncommon events. Neural networks have the potential to help people make better decisions. NLP is a technique for analyzing, interpreting, and comprehending large amounts of text. We can no longer evaluate the text using traditional approaches due to the massive volumes of text data and the exceedingly unstructured data source, which is where NLP comes in. As a result, the research focuses on what a neural network is and how different types of neural networks are used in natural language processing. NLP (natural language processing) is a method for analyzing, interpreting, and comprehending vast amounts of text. Due to the huge volumes of text data and the extremely unstructured data source, we can no longer analyze the text using standard approaches, which is where NLP comes in. As a result, the study concentrates on what a neural network is and how various types of neural networks are used in natural language processing. Due to their exceptional success in numerous NLP tasks, BERT in particular has gotten a lot of attention. Google\u27s Bidirectional Encoder Representations from Transformers (BERT) is a Transformer-based machine learning methodology for pre-training in natural language processing (NLP)

Adjoining Internet of Things with Data Mining : A Survey

The Interactive Data Corporative (IDC) conjectures that by 2025 the worldwide data circle will develop to 163ZB (that is a trillion gigabytes) which is ten times the 16.1ZB of information produced in 2016. The Internet of Things is one of the hot topics of this living century and researchers are heading for mass adoption 2019 driven by better than-expected business results. This information will open one of a kind of user experience and another universe of business opening. The huge information produced by the Internet of Things (IoT) are considered of high business esteem, and information mining calculations can be connected to IoT to extract hidden data from information. This paper concisely discusses the work done in sequential manner of time in different fields of IOT along with its outcome and research gap. This paper also discusses the various aspects of data mining functionalities with IOT. The recommendation for the Challenges in IOT that can be adopted for betterment is given. Finally, this paper presents the vision for how IOT will have impact on changing the distant futur

Squential Step Towards Pattern Warehousing

With the massive increase in the data, the demand by the analysts hyped for the proper repositories where they could analyse the concerned specific data patterns in order to make smart and quick decisions for the welfare and benefit of the business, organization or some social work. Pattern warehouse proved to be the best solution. This paper focuses on the discussion of existing architecture and moreover on the algorithms that is needed for retrieving the optimal patterns from the pattern warehouse. It also includes the detailed study about the sequential emergence of association rule algorithms which initially derive out patterns and later on those patterns are being optimized according to the interest of the analyst

ANTIDEPRESSANT ACTIVITY OF THYMOQUINONE POSSIBLY THROUGH INVOLVEMENT OF CORTICOTROPIN RELEASING FACTOR

  Objective: Present study aimed to evaluate the antidepressant-like activity of thymoquinone (TQ) in unstressed and stressed condition and to explore the possible underlying mechanism for this activity.Methods: TQ (5, 10, and 20 mg/kg) and fluoxetine per se were administered to the unstressed and stressed mice; immobility periods were observed using forced swim test (FST) and tail suspension test (TST). Effect of corticotropin-releasing factor (CRF)-1 antagonist on antidepressant-like activity was also evaluated. The mechanism of action was also explored by measuring plasma corticosterone levels.Results: TQ (20 mg/kg) and fluoxetine per se significantly decreased immobility periods in stressed mice indicating significant antidepressant-like activity under stress. There was no significant effect on locomotor activity of the mice on treatment with TQ and fluoxetine per se. It significantly decreased plasma corticosterone level. Antalarmin (a CRF-1 receptor antagonist) significantly attenuated TQ induced the antidepressant-like effect in both FST and TST.Conclusion: TQ significantly produced antidepressant-like activity in mice possi‑bly through inhibiting CRF activity and decreasing plasma corticosterone levels.Â

PREPARATION AND CHARACTERIZATION OF ALGINATE CHITOSAN CROSSLINKED NANOPARTICLES BEARING DRUG FOR THE EFFECTIVE MANAGEMENT OF ULCERATIVE COLITIS

Objective: Delivery of anticancer molecule to the liver remains a “holy grail” in molecular medicine and nanobiotechnology with conventional therapy, as conventional cancer chemotherapy does not prove effective as drug molecule does not reach to the target site at therapeutic concentration. Tumor vasculature differs from the vasculature of normal tissue both in morphology and biochemistry. Most of these differences appear too related to angiogenesis (formation of new blood vessels from pre-existing ones). For the present study nanoparticles (NPs) were chosen as a delivery system, because they have many advantages, e. g. they can pass through the smallest capillary vessels because of their ultra-tiny volume, can penetrate cells and tissue gap to arrive at, pH, ion and/or temperature sensitivity of materials, can improve the utility of drugs and reduce toxic side effects. Methods: PLGA (poly lactide co glycolic acid) was used for the preparation of NPs because of its biodegradability and biocompatibility. It degrades by hydrolysis of ester linkages in the presence of water in to two monomers lactic acid and glycolic acid. There are a number of ligands available for hepatic delivery, among them lactobionic acid (containing galactose moiety) was selected for present work. Preparation of plain nanoparticles was carried out using emulsification–diffusion method. Optimization of the polymer concentration is the first step during the study and it was performed by varying the polymer concentration where as keeping other variables constant. The prepared formulation was optimized on the basis of particle size and polydispersity index. Amount of drug was optimized on the basis of particle size and percentage entrapment efficiency. Results: Particle size and zeta potential of the nanoparticle were determined by zetasizer showed that particles are in nano range (blow 200 nm) and have acceptable range of zeta potential. Shape and surface morphology were determined by TEM and SEM analysis. The conjugation of lactobionic acid with PLGA polymer was proved by FTIR. The in vitro release profiles of entrapped drug from formulations were determined using dialysis membrane. For stability studies, the LDNPs (conjugated NPs) are stored at the temperatures 4±1 °C and room temperature. Human hepatoma cell line HepG2 by SRB assay was selected and it clearly suggests a dose dependent cytotoxicity response i.e. decrease in cell survival fraction with increasing concentration of drug. The in vivo study are important in evaluating the targeting efficacy of designed dosage form and also helps in establishing the correlation between the results obtained from in vitro experimentation to that from in vivo studies. The formulations were administered by tail vein to mice of four groups Group I: PBS 7.4 (control); Group II: 5-FU solution; Group III: DNPs; Group IV: LDNPs. Conclusion: The proposed targeting strategy is expected to enhance the therapeutic index of conventional anticancer drug as well as reduce its cytotoxic effects to normal cells

A PRECISE STUDY ON “BERBERIS ARISTATA

Indian barberry is a very common plant with miraculous therapeutic activity. This plant was being used from very ancient times. It generally has Habitat of North-western Himalayas, Nilgiris, Kulu, and Kumaon. Propagation of this herb is carried out during the spring season. It is listed in “Ayurveda” for the treatment of various dysenteries. It is well known as “Daruhaldi” in Ayurveda because it is having properties like turmeric. It is also used in different systems of medication like in Ayurveda, Siddha, and Yunani. Twigs are either white or pale yellowish-brown in colour. The bark is having a pale brown appearance on the outside and has deep yellowish colour inside. Leaves are obovate, with reticulated venation, and are arranged in tufts of five to eight. These leaves are having a glossy dark green appearance on the outside and a light green colour inside. Flowers are yellow and are usually bisexual. The fruits are bright red. The stem is subterete, pale brownish yellow. It chiefly contains “berberine” as an active phytoconstituent that belongs to alkaloids. Other than berberine it also contains contain barbamine, oxyberberine, palmatine, and taxilamine. Roots of Berberis aristata contain berberine, barbamine, Jatrorrhizine, columbamine and oxyberberine. This plant is having various therapeutic activities like; antibacterial, antiperiodic, antidiarrheal, antipyretic, antidiabetic, and anticancer activities. Several research works were performed for this plant with the use of systemic animal models. A significant result was obtained. Investigations suggested that it can be used as an antimalarial, antioxidant, anti-inflammatory, hypoglycaemic, and hepatoprotective

IMPACT OF COVID-19 AND ONLINE EDUCATION ON THE MENTAL HEALTH OF MEMBERS OF EDUCATIONAL SPHERE-A CASE STUDY

Coronavirus disease 2019(COVID-19), the enormously transmissible disease resulting due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as the causative agent, instigated a dreadful outcome ensuing worldwide emergency with its rapid spread and greater mortality rate resulting in grievous disruptions. It arose as the greatest substantial world-wide health catastrophe ever since the period of influenza pandemic of 1918, causing more than 3.7 million deaths worldwide. The influence of this pandemic was ascertained in every arena of life on a worldwide level. COVID-19 has devastated many countries, thrashing our health care system besides having a major impact on the academic sector encompassing an enormous number of students, teachers along with staff members. With the implementation of the lock-down the offline classes were substituted for the online mode not only in India but globally. This has chiefly prompted an effect on the mental health of people apart from their physical health. Mental well-being has a vital significance and the spread of pandemic has accelerated a series of mental disorders ranging from anxiety, stress to depressive disorders. This review, based on questionnaires prepared using the perceived stress scale method compiles the response data of how COVID-19 has affected the mental health of students and members of the educational sphere. Not only this but it shows a contrast between the offline and the new tech-friendly online classes. Thus, this survey study reflects on creating a framework for the academic sector to aid in resolving and helping people manifested with mental health issues so as to lead a normal healthy lifestyle

MEGA: Multilingual Evaluation of Generative AI

Generative AI models have shown impressive performance on many Natural Language Processing tasks such as language understanding, reasoning, and language generation. An important question being asked by the AI community today is about the capabilities and limits of these models, and it is clear that evaluating generative AI is very challenging. Most studies on generative LLMs have been restricted to English and it is unclear how capable these models are at understanding and generating text in other languages. We present the first comprehensive benchmarking of generative LLMs - MEGA, which evaluates models on standard NLP benchmarks, covering 16 NLP datasets across 70 typologically diverse languages. We compare the performance of generative LLMs including Chat-GPT and GPT-4 to State of the Art (SOTA) non-autoregressive models on these tasks to determine how well generative models perform compared to the previous generation of LLMs. We present a thorough analysis of the performance of models across languages and tasks and discuss challenges in improving the performance of generative LLMs on low-resource languages. We create a framework for evaluating generative LLMs in the multilingual setting and provide directions for future progress in the field.Comment: EMNLP 202