Maximizing resource utilization by slicing of superscalar architecture

Superscalar architectural techniques increase instruction throughput from one instruction per cycle to more than one instruction per cycle. Modern processors make use of several processing resources to achieve this kind of throughput. Control units perform various functions to minimize stalls and to ensure a continuous feed of instructions to execution units. It is vital to ensure that instructions ready for execution do not encounter a bottleneck in the execution stage; This thesis work proposes a dynamic scheme to increase efficiency of execution stage by a methodology called block slicing. Implementing this concept in a wide, superscalar pipelined architecture introduces minimal additional hardware and delay in the pipeline. The hardware required for the implementation of the proposed scheme is designed and assessed in terms of cost and delay. Performance measures of speed-up, throughput and efficiency have been evaluated for the resulting pipeline and analyzed

Revisting SQL Query Recommender System Using Hierarchical Classification

For analytical purposes, lots of data are gathered which are gathered and explored in data warehouses. Even to handle such a large data is a tough task for expert people. For non-expert users or for users who are not familiar with the database schema, handling such a voluminous data is more difficult task. The aim of this paper is to facilitate this class of users by recommending them SQL queries that they may use. By following the users past behavior and comparing them with other users, these SQL recommendations are selected. Initially, users may not know from where they can start their exploration. Secondly, users may overlook queries which help them to retrieve important data. Using hierarchical classification, the queries are recorded and compared which is then re-ranked according to relevance. Using users querying behavior, the relevant queries are retrieved. To issue a series of SQL queries, users use a query interface which aim to analyze the data and mine it for interesting information. DOI: 10.17762/ijritcc2321-8169.150614

Query Recommender System Using Hierarchical Classification

In data warehouses, lots of data are gathered which are navigated and explored for analytical purposes. Even for expert people, to handle such a large data is a tough task. Handling such a voluminous data is more difficult task for non-expert users or for users who are not familiar with the database schema. The aim of this paper is to help this class of users by recommending them SQL queries that they might use. These SQL recommendations are selected by tracking the users past behavior and comparing them with other users. At first time, users may not know where to start their exploration. Secondly, users may overlook queries which help to retrieve important information. The queries are recorded and compared using hierarchical classification which is then re-ranked according to relevance. The relevant queries are retrieved using users querying behavior. Users use a query interface to issue a series of SQL queries that aim to analyze the data and mine it for interesting information. DOI: 10.17762/ijritcc2321-8169.15067

Robson classification: beyond caesarean rates

Background: Caesarean rates are increasing globally. Unnecessary caesarean sections are a public health concern and are associated with both short term and long-term risks. Robson’s ten group classification is the accepted classification for caesarean section and implementing it is an effective measure in understanding which group should be focussed to reduce the caesarean section rates. The classification can be used as a framework for assessing perinatal and maternal outcome. Methods: All the deliveries with gestational age more than 28 weeks at Gadag Institute of medical sciences, Karnataka, during April 2022 were included. Obstetric characteristics like parity, gestational age, previous caesarean sections, onset of labour, lie, presentation, mode of delivery, indications for caesarean section and foetal and maternal complications were recorded. Results: The caesarean rate was 57.9%. Groups 5, 1 and 2 were the major contributors. Previous LSCS was the most common indication 46%. In groups 1 to 4 foetal distress was the most common indication. The overall proportion of unfavourable foetal outcome among all deliveries was 17.7% caesarean deliveries (20.1%), vaginal deliveries 14.5%. The proportion of unfavourable maternal outcome was 1.6%; 8 women delivered by CS (2.5%) and 1 woman by vaginal delivery (0.4%). Conclusions: Caesarean section should be used appropriately and increase in caesarean section does not ensure favourable maternal or perinatal outcome

A clinical study to evaluate the efficacy of Dahaprashamana Gana in the management of Vasomotor Symptoms in Perimenopause and Menopause

Background: Menopause is a transitional phase marking the finale of the reproductive life of a woman. It’s associated with complex physical and psychological changes. The vasomotor symptoms are the hallmark of menopause, affecting 75% of women and among this 25% are severely affected and are the most common and long-lasting symptoms according to many studies. In Ayurvedic literature “Rajonivrutti” has very little references available. The age of Rajonivruthi is said to be around 50 years as per the classics and it is considered as a premonitory sign of Jara in women. The vasomotor symptoms like hot flushes, night sweats, irritability clearly indicate the involvement of dominant Pitta Dosha associated with Vata. Hence a drug that pacifies Pitta along with Vata, without disturbing Kapha will be ideal for treatment. Dahaprashamana Gana explained in Charaka Samhitha Sutrasthana, denotes a group of medicinal plants, which has been indicated as useful in removing Daha which is a direct manifestation of Pitta. Aim: To analyse the effect of Dahaprashamana Churna in the management of vasomotor symptoms in perimenopause and menopause. Method: A simple randomized open label controlled clinical study thirty subjects fulfilling the diagnostic criteria of Vasomotor symptoms were selected and randomly categorized to Group A and Group B by using lottery method. Result: Dahaprashamana Churna found to be effective in all subjective and objective parameters. Conclusion: Dahaprashamana Churna is more effective than Vitamin E in the management of Vasomotor symptoms in Perimenopause and Menopaus

A Comprehensive Survey of Regression Based Loss Functions for Time Series Forecasting

Time Series Forecasting has been an active area of research due to its many applications ranging from network usage prediction, resource allocation, anomaly detection, and predictive maintenance. Numerous publications published in the last five years have proposed diverse sets of objective loss functions to address cases such as biased data, long-term forecasting, multicollinear features, etc. In this paper, we have summarized 14 well-known regression loss functions commonly used for time series forecasting and listed out the circumstances where their application can aid in faster and better model convergence. We have also demonstrated how certain categories of loss functions perform well across all data sets and can be considered as a baseline objective function in circumstances where the distribution of the data is unknown. Our code is available at GitHub: https://github.com/aryan-jadon/Regression-Loss-Functions-in-Time-Series-Forecasting-Tensorflow.Comment: 13 pages, 23 figure

A Bibliometric Survey of Fashion Analysis using Artificial Intelligence

In the 21st century, clothing fashion has become an inevitable part of every individual human as it is considered a way to express their personality to the outside world. Currently the traditional fashion business models are experiencing a paradigm shift from being an experience-based business strategy implementation to a data driven intelligent business improvisation. Artificial Intelligence is acting as a catalyst to achieve the infusion of data intelligence into the fashion industry which aims at fostering all the business brackets such as supply chain management, trend analysis, fashion recommendation, sales forecasting, digitized shopping experience etc. The field of “Fashion AI\u27\u27 is still under research progress because the fashion data is a multifaceted entity which is available in any of the forms like an image, video, text and numerical values. Therefore, it becomes a challenging research arena. There is a paucity of a common study which can provide a bird’s eye view about the research efforts and directions. In this paper, the authors represent a bibliometric survey of the AI based fashion analysis domain based on the Scopus database. The study was conducted by retrieving 581 Scopus research papers published from 1975-2020 and analysed to find out critical insights such as publication volume, co-authorship networks, citation analysis, and demographic research distribution. The study revealed that significant contribution is made via concept propositions in conferences and some papers published in the journal. However, there is a scope of lots of research work in the direction of improving fashion industry with AI techniques

A Bibliometric Survey of Fashion Analysis using Artificial Intelligence

In the 21st century, clothing fashion has become an inevitable part of every individual human as it is considered a way to express their personality to the outside world. Currently the traditional fashion business models are experiencing a paradigm shift from being an experience-based business strategy implementation to a data driven intelligent business improvisation. Artificial Intelligence is acting as a catalyst to achieve the infusion of data intelligence into the fashion industry which aims at fostering all the business brackets such as supply chain management, trend analysis, fashion recommendation, sales forecasting, digitized shopping experience etc. The field of “Fashion AI\u27\u27 is still under research progress because the fashion data is a multifaceted entity which is available in any of the forms like an image, video, text and numerical values. Therefore, it becomes a challenging research arena. There is a paucity of a common study which can provide a bird’s eye view about the research efforts and directions. In this paper, the authors represent a bibliometric survey of the AI based fashion analysis domain based on the Scopus database. The study was conducted by retrieving 581 Scopus research papers published from 1975-2020 and analysed to find out critical insights such as publication volume, co-authorship networks, citation analysis, and demographic research distribution. The study revealed that significant contribution is made via concept propositions in conferences and some papers published in the journal. However, there is a scope of lots of research work in the direction of improving fashion industry with AI techniques

Design of Microstrip Patch Antenna using Ads Tool

The investigation of microstrip fix reception apparatuses has gained incredible ground as of late. Contrasted and regular receiving wires, microstrip fix radio wires have more preferences and better prospects. They are lighter in weight, low volume, minimal effort, low profile, littler in measurement and simplicity of creation and congruity. Also, the microstrip fix recieving wires can give double and roundabout polarizations, double recurrence operation, recurrence deftness, expansive band-width, encourage line adaptability, pillar filtering omnidirectional designing. In this paper we examine the microstriprecieving wire, sorts of microstrip radio wire, nourishing strategies and utilization of microstrip fix reception apparatus