A Novel Densenet-324 Densely Connected Convolution Neural Network for Medical Crop Classification using Remote Sensing Hyperspectral Satellite Images

In the past few decades, importance of the medicinal Crops is extending to a large extent due to its benefits in treating life-threatening diseases. Medicinal Crop has excellent medicinal properties on its roots, stem, and leaves to prevent human and animal health. Particularly detection and identification of the Crop classes are effectively carried out using hyperspectral images as discrimination of the target feature or objects is simple and it contains rich information containing the spatial and temporal details of underlying the land cover. However, Crop classification using machine learning architectures concerning spectral characteristics obtained on the anatomical features and morphological features. Extracted features towards classification lead to several challenges such as large spatial and temporal variability and spectral signatures similarity between different objects. A further hyperspectral image poses several difficulties with changes in illumination, environment, and atmospheric aspects. To tackle those non-trivial challenges, DenseNet-324 Densely Connected convolution neural network architecture has been designed in this work to discriminate the crop and medical Crop effectively in the interested areas.  Initially, the Hyperspectral image is pre-processed against a large number of noises through the employment of the noise removal technique and bad line replacement techniques. Pre-processed image is explored to image segmentation using the global thresholding method to segment it into various regions based on spatial pieces of information on grouping the neighboring similar pixels intensity or textures. Further regions of the image are processed using principle component analysis to extract spectral features of the image. That extracted feature is employed to ant colony optimization technique to obtain the optimal features. Computed optimal features are classified using Convolution Neural Network with a hyper parameter setup. The convolution Layer of the CNN architecture process spatial, temporal, and spectral feature and generates the feature map in various context, generated feature map is max pooled in the pooling layer and classified into crops and medicinal Crop in the SoftMax layer. Experimental analysis of the proposed architecture is carried out on the Indiana Pines dataset using cross-fold validation to analyze the representation ability to discriminate the features with large variance between the different classes. From the results, it is confirmed that the proposed architecture exhibits higher performance in classification accuracy of 98.43% in classifying the Crop species compared with conventional approaches.&nbsp

Multilayer Feedforward Neural Network for Internet Traffic Classification

Recently, the efficient internet traffic classification has gained attention in order to improve service quality in IP networks. But the problem with the existing solutions is to handle the imbalanced dataset which has high uneven distribution of flows between the classes. In this paper, we propose a multilayer feedforward neural network architecture to handle the high imbalanced dataset. In the proposed model, we used a variation of multilayer perceptron with 4 hidden layers (called as mountain mirror networks) which does the feature transformation effectively. To check the efficacy of the proposed model, we used Cambridge dataset which consists of 248 features spread across 10 classes. Experimentation is carried out for two variants of the same dataset which is a standard one and a derived subset. The proposed model achieved an accuracy of 99.08% for highly imbalanced dataset (standard)

A Scientometric Profile of Science Faculty of Rashtrasant Tukadoji Maharaj Nagpur University, India during 1990-2019

Research productivity of any university is a reflection of its status quo in terms of its position in Higher Education Institution (HEI) ranking framework. This Scientometric study is an attempt to highlight the key features of research productivity of Science Faculty of Rashtrasant Tukadoji Maharaj Nagpur University based on the data collected from Web of Science database over a period of 30 years from 1990 to 2019.This article studied the publication trend by analysis of the 2229 research records. The major objectives of this study were to evaluate the research output in terms of number and type of publications, prolific and productive author, most preferred publication, citation analysis and so on. The complete research output is 2229 with an Annual Growth Rate (AGR) of 6.23 % with 11.4 % of average citations per paper. The period from year 2004 to 2019 was highly productive block with year 2015 contributing highest 7.31 % of the total number of publications during the study period. Physics was found to the core subject in which high level of quantitative as well as qualitative research has been performed by Science Faculty of the university. This paper is an attempt to portray a scientometric profile of the one of the oldest and premier university of India which will be help to highlight appropriate measures required for enhancing the research benchmark of the university to a new high

Design and development of certification compliance tool for airborne systems.

Certification compliance check for airborne software is very critical as it aids in the certification of the software. Since this compliance check is performed manually which is time-consuming and erroneous, an in-house developed Certification Compliance Tool (CCT) helps in checking the compliance as per RTCA DO-178B/C and generate artifacts depicting the magnitude of compliance. In order to generate the magnitude of compliance for the artifacts with respect to the Civil Aerospace Certification standard, RTCA DO-178B/C, an effective parsing technique is required to be incorporated to parse the artifact/s and generate compliance metric for the artifact/s. In this paper we propose a novel approach used in the design and development of an effective and efficient parsing technique incorporated in the indigenous software tool CCT used for compliance check. The tool checks the ratio of compliance of the artifacts generated across various phases of Software Development Life Cycle (SDLC) process involved in the development of Safety-Critical software as per RTCA DO-178B/C. The indigenous tool accepts these artifacts as inputs and based on the software criticality level, it analyzes the compliance of these artifacts with the guidelines provided and recommended by RTCA DO-178B/C. The output of the tool provides the percentage of compliance of the artifacts that helps in accessing the Certification capabilities of the developed software. The percentage of compliance predicts the acceptance or rejection probabilities of the software being certified by the Certification Agency. The certification parser is developed using Python modules like Pywin32, Pypdf parsers and different approaches for Natural language processing using Python Natural Language Toolkit (NLTK). The in-house tool replaces the manual effort by an individual/s which may be erroneous and impact the time-schedule, which compromises the software safety. The integration of the tool with commercial tools will help in analyzing the report/ documentation content with respect to the certification

A Study on Thandaga Vatham (Lumbar spondylosis)

The disease Thandagavatham is extremely common in now-a-days because of its degenerative mechanism. So the author had chosen this disease and attempts search for a perfect remedy for the same. For this study Twenty patients of either sex were selected as inpatients and twenty patients of either sex were selected as out patients. The patients were administered with the trial medicine “vathathirkku chooranam – 2gms” with hot water twice a day during the whole study period. Scientific analysis such as bio-chemical and pharmacological studies was carried out for the trial medicine. At the end of the trial study, 77.5% of patients were improved and 22.5% of patients were moderately improved. AIM OF THE STUDY: To evaluate the clinical efficacy of the trail drug VATHATHIRKKU CHOORANAM in treating Thandaga Vatham. OBJECTIVES: • To analyse Thandaga Vathagam in view of its etiology, pathophysiology, symptoms as said in our literature. • To analyse lumbar spondylosis in relation with Thandaga vatham. • To have a literary review about Thandaga Vatham • To analyse the trial drug biochemically and pharmacologically. • To utilize both siddha and modern parameters in the diagnostic purpose of the disease. SUMMARY: The disease Thandagavatham is taken for the clinical study with reference in Yugi Vaithiya Chinthamani-800. The disease Thandaga Vatham is corrected with the modern term Lumbar Spondylosis. The clinical diagnosis was done on the basis of clinical features which is described in Yugi Vaithiya Chinthamani – 800. The trial drug chosen for the clinical study is Vathathirkku chooranam – internal 2 gms twice a day with hot water after food. The aetiology, pathology, pathogenesis, clinical features, classification and prognosis of the disease were collected from a number of literatures both in siddha aspect and in modern aspect. 40 patients of both sexes clinically diagnosed and selected for this study. In this twenty patients were diagnosed admitted in the IN patients ward and treated with trial medicines. Another twenty patients were treated as out – patients were followed as out- patients. The course of clinical study, selection of patients, management of patients during the study was carried out under the supervision of Professor, Reader and Assistant lecturer of P.G Pothu Maruthuvam department,Government siddha medical college, palayamkottai. A case sheet proforma was prepared with suitable references focussing Siddha and modern clinical parameters. IN patients were maintained with separate case sheets and their vital signs, signs and symptoms were monitored and the datas are recorded daily. The patients were treated with Vathathirkku chooranam (internal). This disease predominantly affects females than males which was clearly inferred from the clinical data. The maximum incidence of age for this disease was between 51 – 60 i.e in Pitha kaalam. Various factors along with signs and symptoms mentioned in the case sheet were elaboratly discussed in the previous chapter. To follow the prognosis of the patients the routine blood examination estimation of blood sugar, blood urea, serum cholesterol and investigation of urine, were done before and after treatment. The disease was diagnosed on the basis of ENVAGAI THERVUGAL especially with naadi and neikuri. The efficacy of the medicine ‘Vathathirkku chooranam’ (internally) was studied and observed during the period of this research. At the end of the treatment there was marked reduction of clinical symptoms like back pain with stiffness (which is the classical symptoms of Thandaga vatham) with sense of well being. In haemotological study there was decreased ESR, cholesterol level with mild increase in haemoglobin level was observed at the end of the course of treatment trial drug is efficient in curing the disease and maintaining the normal health conditions also. The patients taken for the clinical study were observed for a period of 30 days during and after the course of treatment, no signs of complications were reported. Clinically the trial medicines were used only after careful purification process. The pharmacological evaluation of a) “Vathathirkku chooranam” showed moderate analgesic action, significant anti - inflammatory action, and significant anti – pyretic actions. No acute toxic effects were noted. b) Bio – chemical analysis of Vathathirkku chooranam showed presence of calcium, sulphate, chlorides, starch, ferrous iron and unsaturated compound. On the basis of symptoms relieved and results observed during the study, the clinical improvement was graded as improved and moderately improved. The IN patients were discharged only after satisfactory clinical improvement and they are adviced to follow up the OUT patients ward. The improvement was observed only clinically and there was no changes in radiological findings. CONCLUSION: The clinical trial revealed good prognosis among patients suffering from Thandagavatham. The trial medicine Vathathirkku chooranam had not produced any toxic, adverse or side effects. The trial medicine has significant anti inflammatory action, moderate analgesic and antipyretic effects. The uppu suvai of the trial medicine propably helps in the management of degenerative manifestation. Further making the trial medicine available in the market would be economic and satisfactory in the management of Thandaga vatham

Prognóstico de exploração no Chat GPT com ética de inteligência artificial

Natural language processing innovations in the past few decades have made it feasible to synthesis and comprehend coherent text in a variety of ways, turning theoretical techniques into practical implementations. Both report summarizing software and sectors like content writers have been significantly impacted by the extensive Language-model. A huge language model, however, could show evidence of social prejudice, giving moral as well as environmental hazards from negligence, according to observations. Therefore, it is necessary to develop comprehensive guidelines for responsible LLM (Large Language Models). Despite the fact that numerous empirical investigations show that sophisticated large language models has very few ethical difficulties, there isn't a thorough investigation and consumers study of the legality of present large language model use. We use a qualitative study method on OpenAI's ChatGPT3 to solution-focus the real-world ethical risks in current large language models in order to further guide ongoing efforts on responsibly constructing ethical large language models. We carefully review ChatGPT3 from the four perspectives of bias and robustness. According to our stated opinions, we objectively benchmark ChatGPT3 on a number of sample datasets. In this work, it was found that a substantial fraction of principled problems are not solved by the current benchmarks; therefore new case examples were provided to support this. Additionally discussed were the importance of the findings regarding ChatGPT3's AI ethics, potential problems in the future, and helpful design considerations for big language models. This study may provide some guidance for future investigations into and mitigation of the ethical risks offered by technology in large Language Models applications.Las innovaciones en el procesamiento del lenguaje natural en las últimas décadas han hecho posible sintetizar y comprender textos coherentes en una variedad de formas, transformando las técnicas teóricas en implementaciones prácticas. Ambos informan que el software extenso y las industrias como la de los creadores de contenido se han visto significativamente afectadas por el modelo de lenguaje extensivo. Sin embargo, un modelo de lenguaje enorme podría mostrar evidencia de sesgo social, dando riesgos morales y ambientales por negligencia, según las observaciones. Por lo tanto, es necesario desarrollar lineamientos completos para los LLM (Modelos de Lenguaje Grandes) responsables. A pesar de que numerosas investigaciones empíricas muestran que los modelos sofisticados de lenguaje amplio tienen muy pocas dificultades éticas, no existe una investigación exhaustiva y un estudio del consumidor sobre la legalidad del uso actual de modelos de lenguaje amplio. Usamos un método de estudio cualitativo en ChatGPT3 de OpenAI para enfocarnos en resolver los riesgos éticos del mundo real en los modelos actuales de lenguaje amplio para guiar aún más los esfuerzos en curso en la construcción responsable de modelos éticos de lenguaje amplio. Analizamos cuidadosamente ChatGPT3 desde las cuatro perspectivas de sesgo y robustez. De acuerdo con nuestras opiniones expresadas, comparamos ChatGPT3 objetivamente en múltiples conjuntos de datos de muestra. En este trabajo se encontró que una fracción sustancial de los problemas de principios no son resueltos por los marcos actuales; por lo tanto, se han proporcionado nuevos ejemplos de casos para respaldar esto. Además, se discutió la importancia de los hallazgos sobre la ética de la IA de ChatGPT3, los problemas potenciales en el futuro y las consideraciones de diseño útiles para modelos de lenguaje grandes. Este estudio puede proporcionar algunas pautas para futuras investigaciones y mitigación de los riesgos éticos que ofrece la tecnología en grandes aplicaciones de Language Models.As inovações de processamento de linguagem natural nas últimas décadas tornaram possível sintetizar e compreender textos coerentes de várias maneiras, transformando técnicas teóricas em implementações práticas. Ambos relatam que softwares resumidos e setores como criadores de conteúdo foram significativamente afetados pelo extenso modelo de linguagem. Um enorme modelo de linguagem, no entanto, poderia mostrar evidências de preconceito social, dando riscos morais e ambientais por negligência, de acordo com as observações. Portanto, é necessário desenvolver diretrizes abrangentes para LLM (Large Language Models) responsáveis. Apesar do fato de numerosas investigações empíricas mostrarem que modelos sofisticados de linguagem ampla têm muito poucas dificuldades éticas, não há uma investigação completa e estudo de consumidores sobre a legalidade do uso atual de modelos de linguagem ampla. Usamos um método de estudo qualitativo no ChatGPT3 da OpenAI para focar na solução os riscos éticos do mundo real nos atuais modelos de linguagem ampla, a fim de orientar ainda mais os esforços contínuos na construção responsável de modelos éticos de linguagem ampla. Analisamos cuidadosamente o ChatGPT3 a partir das quatro perspectivas de viés e robustez. De acordo com nossas opiniões declaradas, comparamos objetivamente o ChatGPT3 em vários conjuntos de dados de amostra. Neste trabalho, constatou-se que uma fração substancial dos problemas de princípios não é resolvida pelos referenciais atuais; portanto, novos exemplos de casos foram fornecidos para apoiar isso. Além disso, foram discutidas a importância das descobertas sobre a ética de IA do ChatGPT3, possíveis problemas no futuro e considerações de design úteis para grandes modelos de linguagem. Este estudo pode fornecer algumas orientações para futuras investigações e mitigação dos riscos éticos oferecidos pela tecnologia em grandes aplicações de Modelos de Linguagem

Anti-Adjacency Matrices of Certain Graphs Derived from Some Graph Operations

If we go through the literature, one can find many matrices that are derived for a given simple graph. The one among them is the anti-adjacency matrix which is given as follows; The anti-adjacency matrix of a simple undirected graph $G$ with vertex set   $V (G) \,= \,\{\,v_1,\,v_2,\\ \dots, v_n\}$   is an $n \times n$ matrix $B(G) = (b_{ij} )$, where $b_{ij} = 0$ if there exists an edge between $v_i$ and $v_j$ and $1$ otherwise. In this paper, we try to bring out an expression, which establishes a connection between the anti-adjacency matrices of the two graphs $G_1$ and $G_2$ and the   anti-adjacency matrix of their tensor product, $G_1 \otimes G_2$. In addition, an expression for the anti-adjacency matrix of the disjunction of two graphs, $G_1\lor G_2$, is obtained in a similar way. Finally, we obtain an expression for the anti-adjacency matrix for the generalized tensor product and generalized disjunction of two graphs.  Adjacency and anti-adjacency matrices are square matrices that are used to represent a finite graph in graph theory and computer science. The matrix elements show whether a pair of vertices in the graph are adjacent or not

IN VITRO ASSESSMENT OF ANTIOXIDANT AND ANTIBACTERIAL ACTIVITY OF GREEN SYNTHESIZED SILVER NANOPARTICLES FROM DIGITARIA RADICOSA LEAVES

Green metallic nanoparticles were creating a new era in the field of green nanotechnology and its applications. Methanolic leaf extract of rare, endemic medicinally important herb, Digitaria radicosa used for the synthesis of silver nanoparticles (SNPs). UV Visible spectrophotometric analysis confirmed the synthesis of green silver nanoparticles indicated by the peak observed at 442nm due to the excitation of surface plasmon resonance in the silver nanoparticles. FT-IR spectroscopic analysis showed the availability of functional groups which may involve in the silver nanoparticles synthesis. X-Ray Diffraction pattern illustrated the characteristic peaks of (111), (122), (231) facets of the centre crystalline and cubic face centred nature of silver nanoparticles. SEM analysis showed that synthesized green silver nanoparticles were of spherical in shape and size of around 90 nm. The free radical scavenging activity of silver nanoparticles were evaluated in vitro by using DPPH scavenging activity, metal chelating activity, reducing power assay and hydrogen peroxide scavenging assays. The antibacterial activity against food borne pathogens such as S. aureus and E .coli were determined by disc diffusion method. The results confirmed that these synthesized green silver nanoparticles identified to have significant in vitro antioxidant potential and good antibacterial activity.Keywords:Green Silver nanoparticles, SNPs, Digitaria radicosa leaf extract, UV Visible spectrophotometry, XRD, FT IR, SEM, in vitro antioxidant assays, antibacterial activity

Gene Therapy for Dopamine Dyshomeostasis: From Parkinson's to Primary Neurotransmitter Diseases

Neurological disorders encompass a broad range of neurodegenerative and neurodevelopmental diseases that are complex and almost universally without disease modifying treatments. There is, therefore, significant unmet clinical need to develop novel therapeutic strategies for these patients. Viral gene therapies are a promising approach, where gene delivery is achieved through viral vectors such as adeno-associated virus and lentivirus. The clinical efficacy of such gene therapies has already been observed in two neurological disorders of pediatric onset; for spinal muscular atrophy and aromatic L-amino acid decarboxylase (AADC) deficiency, gene therapy has significantly modified the natural history of disease in these life-limiting neurological disorders. Here, we review recent advances in gene therapy, focused on the targeted delivery of dopaminergic genes for Parkinson's disease and the primary neurotransmitter disorders, AADC deficiency and dopamine transporter deficiency syndrome (DTDS). Although recent European Medicines Agency and Medicines and Healthcare products Regulatory Agency approval of Upstaza (eladocagene exuparvovec) signifies an important landmark, numerous challenges remain. Future research will need to focus on defining the optimal therapeutic window for clinical intervention, better understanding of the duration of therapeutic efficacy, and improved brain targeting. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society