Volume 10 | Issue 12

 Abstract

Sulfasalazine is anti-inflammatory drug that was used in Rheumatoid arthritis and Ulcerative colitis. Pharmaceutical impurities are existing along with drug substances. Hence it is mandatory requirement to do safety studies of each impurity. The present study was performed to evaluate the mutagenic potential of Sulfasalazine Impurity (2-hydroxy-5-[N-(4-{[4-pyridine-2-ylsulfamoyl) phenyl]sulfamoyl}phenyl)diazen-1-yl]) benzoic acid in the Bacterial Reverse Mutation Assay using five tester strains. In this experiment there was no significance results were reported to establish the impurity as Mutagenic nature.

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 Keywords

: Bacterial Reverse Mutation Assay, Reverent Colonies, Mutation, Toxicology, sulfasalazine, and (2-hydroxy-5-[N-(4-{[4-pyridine-2-ylsulfamoyl) phenyl]sulfamoyl}phenyl)diazen-1-yl]) benzoic acid.

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As network services become more widely utilized, security becomes one of the network's primary and most pressing challenges. Several computers connecting to the network serve critical roles in business and other applications that deliver network services. Consequently, we must look for the most effective measures to protect the system, even though several studies employ deep learning (DL)-based intrusion detection algorithms to identify infiltration. Changes in network traffic may result in reduced accuracy of deep learning-based models regarding network assaults. Deep learning has a plethora of strategies for network attacks. The primary goal of this work is to compare and assess the performance of deep learning algorithms on the 1-D data set. The suggested application uses the gated recurrent units(GRU)1-Dimensional Convolutional Neural Network(1-DCNN) hybrid model to identify network attack rates. The preprocessed dataset is trained and tested with the models to provide notable results that increase prediction accuracy. The experiment was conducted using the KDD99 dataset. The deep learning-based hybrid model could reach an accuracy of about 97 percent. On this standard dataset, the GRU-1DCNN model now outperforms the previous model.

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 Keywords

Network Traffic, Attacks, Deep Learning, GRU, 1DCNN, KDD99 dataset

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A complex condition known as chronic kidney disease (CKD) affects 13% of people worldwide. Renal failure and the progression to end-stage kidney disease as well as cardiovascular disease can be brought on over time by renal disorder. The speed at which the disease progresses and the risk of cardiovascular morbidities may both be accelerated by CKD complications. Early stages of CKD are asymptomatic, and symptoms only appear later on when the disease develops problems like declining kidney function and the presence of other concomitant conditions. Patients can only be treated with dialysis or a kidney transplant in advanced stages of the disease when kidney function has been severely compromised. The incidence of CKD is predicted to increase due to the disease's comorbidities, which are also increasing in prevalence along with the senior population. The present difficulties and unmet patient needs in CKD are discussed in this review.

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Renal Disorder, Chronic Kidney Disease, Kidney, Nephron, Urinary System, Dialysis, Kidney Transplant.

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 Abstract

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British, Empire, Dutch, Impressive, Rebellion, Significant, Witnessed.

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Khelo India, sports, culture, positively influence

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Betel leaf (BL) plays an important role since ancient culture. Its use in India dates back to 400 BC. Importance of BL has been described in ancient books of Ayurveda. the quantitative determinati the calibration, validation and analysed data sets. These errors amounted Explored for their unique medicinal properties, the leaves of Piper betel , an evergreen perennial vine, are a reservoir of phenolics with antimutagenic, antitumor and antioxidant activities. Abutilon indicum is known as "Atibala" in Sanskrit. Literally "Ati" means very and "Bala" means powerful, referring to the properties of this plant as very powerful. Abutilon indicum is a hairy herb or under shrub distributed throughout the tropica. In traditional systems of medicine, various plant parts such as roots, leaves, flowers, bark, seeds, and stems have been used as antioxidant, demulcent, laxative, diuretic, analgesic, anti-inflammatory and antiulcer agents. The leaves are reported to be used by traditional practitioners in cases of inflammatory joint disorders as folklore remedy. The Abutilon genus of the Malvaceae family comprises of about 150 annual or perennial herbs, shrubs or even small trees widely distributed in the tropical and subtropical countries of America, Africa, Asia and Australial. It needs only heat and sun and grows even in dry and poor soils. It is quite common in India on road sides and waste places, growing usually after the rains and flowering during winter. In Poland, it is also cultivated for its ornamental value.

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Banana leaf, Betil leaf, Abutilon indicum.

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Saliva is a complex diagnostic fluid, which contains a variety of enzymes, hormones, antibodies, antimicrobial constituents and growth factors. In our body fluids like blood, saliva, tears, sweat and urine are crucial resources of pathological diagnosis. Changing in level of total cholesterol level in human female saliva was investigated in 60 women volunteers during various stages of reproduction (pre-pubertal, parous, non-parous, menopausal and diabetogenic). According to findings, level of total cholesterol level is significantly increased during ovulatory phase, which was due to hormonal metabolic changes in the period of menstrual cycle.

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Saliva, Total cholesterol, prepubertal, parous, non-parous, menopausal and Diabetogenic.

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Human Rights, Terrorism, Fundamental Rights, Counter-Terrorism, Legal Framework, Psychological oppression of Terrorism, Terrorism in India, Counter Terrorism measures of India, Public safety, Protection of Human Rights

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Abstract The Scheduled tribes, since ages, have been victims of socio-economic exploitation and have been relegated to low income generating occupations, inferior trades, unhealthy environment and unclean unskilled occupations. Although clear untouchable practices may be declining in many parts of the country, caste rigidities continue to confine many Schedule Tribe workers in demeaning occupations, which put them at a disadvantage when compared to other communities. The forces of urbanization, social and protective legislations, positive discrimination and other measures taken by the Government, have led to gradual improvement in occupational mobility and living standards over the years, but the living conditions of the majority of Scheduled Tribes continue to show socio-economic backwardness. The introduction of money economy has left them to the tender mercies of money-lenders leading to their chronic indebtedness. However, these broad generalisations may not be applicable to all the tribes of a vast country like India. Therefore, a study of the impact of institutional finance on the economic development of tribals at a micro level, confined to a district, seem to be most appropriate. The micro study can mark out areas of success as well as failure and also point out the deficiencies in the formulation of policy and in the implementation of the programme. The present study attempts to examine the distribution of borrowers of the tribals of the Visakhapatnam district in Andhra Pradesh.

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Adivasi, indebtedness, credit, borrowings, shifting cultivation.

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The perfect, accurate and robust detection of the audio has been widely grown as the speech technology in the area of audio researches, audio forensics, speech recognition, and so on. However, in real time, it is a challenge to deal with the massive data arriving from distributed sources. Thus, the study introduces a method that effectively deals with the data from the different sources using the audio classification method. This method uses anywhere for our general purpose projects, Voice services are rapidly gaining popularity, with the use of virtual assistants such as Siri, Alexa, and Google Assistant becoming increasingly common. These systems rely on audio classification to understand and respond to user requests. Audio classification is the process of using machine learning algorithms to classify audio data into different categories or classes. This is a crucial component of voice services, as it allows the system to understand and interpret the user's spoken words

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Audio classification, Voice-based services, Voice recognition, Speaker identification, Natural language processing, Speech recognition, Language translation, Machine learning, Artificial intelligence, Human-computer interaction

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Exodontia or extraction of the tooth is defined as the painless removal of the entire tooth or the root with minimum trauma to the investing tissues, so that the wound heals normally and no postoperative prosthesis problems arise. A proper History of the patient is necessary before extraction for evaluating the patient. Many systemic diseases have direct or indirect relation with Exodontia. So, it is necessary for the practitioner to understand the indications, contraindications and management of the patient in case of emergency of these patients during extraction. The survey is conducted among the undergraduate students.

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Anticoagulant therapy, complications, Extraction, oral manifestations, oral-systemic connection.

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Machine learning is an emerging technique for building analytic models for machines to "learn" from data and be able to do predictive analysis. The ability of machines to "learn" and do predictive analysis is very important in this era of big data and it has a wide range of application areas. For instance, banks and financial institutions are sometimes faced with the challenge of what risk factors to consider when advancing credit/loans to customers. For several features/attributes of the customers are normally taken into consideration, but most of these features have little predictive effect on the credit worthiness or otherwise of the customer. Furthermore, a robust and effective automated bank credit risk score that can aid in the prediction of customer credit worthiness very accurately is still a major challenge facing many banks. In this paper, we examine a real bank credit data and conduct several machine learning algorithms on the data for comparative analysis and to choose which algorithms are the best fit for learning bank credit data. The algorithms gave over 80% accuracy in prediction. Furthermore, the most important features that determine whether a customer will default or otherwise in paying his/her credit the next month are extracted from a total of 23 features. We then applied these most important features on some selected machine learning algorithms and compare their predictive accuracy with the other algorithms that used all the 23 features. The results show no significant difference, signifying that these features can accurately determine the credit worthiness of the customers. Finally, we formulate a predictive model using the most important features to predict the credit worthiness of a given customer.

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Machine Learning algorithms

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 Abstract

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Neural Networks, Skin Melanoma Detection

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With the popularization of automobile and the progress of computer vision detection technology, intelligent license plate detection technology has gradually become an important part of intelligent traffic management. License plate detection is used to segment vehicle image and obtain license plate area for follow-up recognition system to screen. It is widely used in intelligent traffic management, vehicle video monitoring and other fields. In this paper, two license plate detection methods are studied, one is based on Sobel edge detection and the other is based on morphological gradient detection. Basing on OpenCV and PYTHON under Windows system, two methods of license plate detection are implemented, and the two algorithms are compared in detail from the aspects of license plate detection accuracy. These methods have high efficiency and good interactivity, which provide a reference for later license plate recognition.

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Image Processing, OPENCV, Number Plate Detection

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Aim and objectives: - To investigate the sample rejection rate of pre analytical errors in a clinical laboratory tertiary care centre. To identify the types, frequency of pre analytical error and to detect the percentage of pre analytical errors. Materials and methods: - This was the retrospective analytical study by collecting the data over the period from November 2020 to February 2022 at clinical biochemistry laboratory at Mahatma Gandhi Medical College and Research Institute. Data analysis was done on samples were received and tested which includes sample like blood, urine and other fluids. Types of errors were as follows: - inappropriate labelled samples, improper collection tubes, clotted sample, hemolysis, insufficient volumes, lipemic samples. Results: - Out of 2,39,327 total samples received from both OPD and IPD, 891 samples were found to be unsuitable for further processing. The majority of the sample rejected due to hemolysis (43%), the second most frequent pre analytical error encountered in insufficient volume (21%), clotted sample (16.2%), mislabelling of samples by the laboratory personnel was seen as caused of rejection is 9.9%, lipemic samples were also rejected is 5.3%, inappropriate container accounted for faulty results in 3.3% of samples. Conclusion: - The concept of TQM encompasses all the steps involved in sample processing, beginning from test ordering to the final interpretation of results by clinicians to reduce or eliminate the errors that may rise during various steps. The dependence on accurate lab results for diagnosis makes it mandatory for labs to ensure accountability and accuracy of results for diagnosis as a consequence of faulty reporting.

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Key words: Preanalytical errors, Covid-19, Total Quality Management, Total testing Process, Turnaround Time.

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 Abstract

Using renewable types of energy and Electric Cars (EVs) can reduce energy prices and minimize radiation to a substantial extent. The intermittent nature of renewable energy sources and their sporadic use, integrating renewable energy sources into the electric grid is a challenging process. If the supply of renewable energy is not adequate to fulfill the requirement, it is expensive to pull more energy from the power grid. This may also reduce the energy efficiency of the charging process. The focus of this study is to present methods for optimizing a controller for the charging of electric vehicle batteries using renewable energy sources for energy management. The output of the Dynamic Programming optimization and a variety of other approaches used in energy optimizing software are compared to determine which one would accurately minimize the cost of energy required for charging and yet fulfill the aggregate battery charge maintaining criteria. Based on this study it is evident that the Dynamic Programming based optimization strategy provides the highest level of accuracy while maintaining a consistently high level of fuel savings in EVs.

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Electric Cars, Battery, Electric Vehicles, Dynamic Programming, Renewable Energy Sources

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Polycystic ovary syndrome is a heterogeneous disorder. It is caused due to chronic anovulation. The symptoms of this PCOS disease occurs during the puberty years. Both normal female pubertal development and pcos are characterized by irregular menstrual cycle, anavulation and acne. Olegomenorrhea (less than 9 period for year) and Amenorrhea (no menustrual period for at least six months) are features of PCOS and can be regarded as' 'as risk for PCOS'.so there are some medication for management of PCOS disease such as progestin therapy, spironalactone, clomiphene, letrozole , Gonadotropins,Eflornithin.

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Menstrual Cycle, Anovulation, Olegomenorrhea, Amenorrhea, Hyperandrogonism.

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 Abstract

The concept of fast dissolving drug delivery system emerged from the desire to provide patient with more conventional means of taking their medication. It is difficult for many patients to swallow tablets and hard gelatine capsules. Hence, they do not comply with prescription, which results in high incidence of non-compliance and ineffective therapy. In some cases such as motion sickness, sudden allergic attacks or coughing, swallowing conventional tablets may be difficult. Such problems can be resolved by means of orodispersible tablets. These tablets disintegrate and dissolve rapidly in mouth. Hence no need to swallow the dosage form, which is highly convenient feature for patients. Hence preparing orodispersible tablet would provide a rapid action during these physiological conditions which may occur any time and a quick therapy being required for them.

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Conventional, orodispersible, tablet, disintegrate, dissolve rapidly

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This article examines the factors driving and hindering Indian foreign labor to Malaysia, especially in Subang, Selangor. The arrival of foreign workers to this country from the British era until now has never stopped. The influx of foreigners into this country is evident in economic sectors such as the agricultural sector in the colonial era and now for all sectors. Without foreigners, Malaysia's economy cannot be driven at the present time, especially in some sectors such as the plantation, construction, and fishing sectors. The need for foreign labor in this country's economic sector is very important.

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Foreign labor, Indian labor, Economic sector, Obstructive factors, Driving factors

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HPLC, Azilsartan Medoxomil and Cilnidipine

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