P vs NP: P is Equal to NP: Desired Proof

Computations and computational complexity are fundamental for mathematics and all computer science, including web load time, cryptography (cryptocurrency mining), cybersecurity, artificial intelligence, game theory, multimedia processing, computational physics, biology (for instance, in protein structure prediction), chemistry, and the P vs. NP problem that has been singled out as one of the most challenging open problems in computer science and has great importance as this would essentially solve all the algorithmic problems that we have today if the problem is solved, but the existing complexity is deprecated and does not solve complex computations of tasks that appear in the new digital age as efficiently as it needs. Therefore, we need to realize a new complexity to solve these tasks more rapidly and easily. This paper presents proof of the equality of P and NP complexity classes when the NP problem is not harder to compute than to verify in polynomial time if we forget recursion that takes exponential running time and goes to regress only (every problem in NP can be solved in exponential time, and so it is recursive, this is a key concept that exists, but recursion does not solve the NP problems efficiently). The paper’s goal is to prove the existence of an algorithm solving the NP task in polynomial running time. We get the desired reduction of the exponential problem to the polynomial problem that takes O(log n) complexity.

Statistical Literacy among Practicing Clinicians from India: A Brief Survey

Objectives: To understand statistical literacy among practicing clinicians as well as to take suggestions on its implementation in medical curriculum. Methods: A web based online survey was conducted among practising clinicians of India. The questionnaire consisted of 30questions in three parts. Part A included questions on general information (5 questions), Part B included questions on application of statistical concepts (20 questions), and Part Casked for opinions on integration of biostatistics in medical curriculum (5 questions). Part B questions were rated on a 5-point Likert scale in which 1 indicated no confidence and 5indicated complete confidence. Results: A total of 416 clinicians responded to the questionnaire. Complete confidence in the use of SPSS software was seen in 15.8% whereas 26.7% had no confidence in it. The highest confidence was seen in statistical equations like graphical representation of data (44.7%), and sensitivity and specificity (45.2%) whereas lowest confidence was seen in COX proportional hazard regression (12.9%) and ROC curves (11.7%). Out of 416 clinicians, 136 (32.4%) had done training in statistics at undergraduate level, 128 (30.5%) did self-learning and 152 (37.1%) had received no formal training.

Artificial Excellence - A New Branch of Artificial Intelligence

"Artificial Excellence" is a new field which is invented in this article. Artificial Excellence is a new field which belongs to Artificial Human Optimization field. Artificial Human Optimization is a sub-field of Evolutionary Computing. Evolutionary Computing is a sub-field of Computational Intelligence. Computational Intelligence is an area of Artificial Intelligence. Hence after the publication of this article, "Artificial Excellence (AE)" will become popular as a new branch of Artificial Intelligence (AI). A new algorithm titled "Artificial Satish Gajawada and Durga Toshniwal Algorithm (ASGDTA)" is designed in this work. The definition of AE is given in this article followed by many opportunitiesin the new AE field. The Literature Review of Artificial Excellence field is shown after showing the definition of Artificial Intelligence. The new ASGDTA Algorithm is explained followed by Results and Conclusions.

A Framework to Model Site Reliability Engineering Implementations and its Consolidation

Site Reliability Engineering has opened a pandora’s box of new technological challenges [1],[3],[6],[10]. Trying to condense all of it into one structure or system is one such requirement. We show the interaction between people, processes, and technology as against the levels of maturity on reliability scale of 5.

A Dynamic Level Technical Indicator Model for Oil Price Forecasting

Investment in commodities and stock requires a nearly accurate prediction of price to make profit and to prevent losses. Technical indicators are usually employed on the software platforms for commodities and stock for such price prediction and forecasting. However, many of the available and popular technical indicators have proved unprofitable and disappointing to investors, often resulting not only in ordinary losses but in total loss of investment capital. We propose a dynamic level technical indicator model for the forecasting of commodities’ prices. The proposed model creates dynamic price supports and resistances levels in different time frames of the price chart using a novel algorithm and employs them for price forecasting. In this study, the proposed model was applied to predict the prices of the United Kingdom (UK) Oil. It was compared with the combination of two popular and widely accepted technical indicators, the Moving Average Convergence and Divergence (MACD) and Stochastic Oscillator. The results showed that the proposed dynamic level technical indicator model outperformed MACD and Stochastic Oscillator in terms of profit.

Exam Management System at Notre Dame University Bangladesh

Exam Management System (EMS) at Notre Dame University Bangladesh (NDUB) is an effective system for man- aging exam and course-related activities for NDUB. EMS auto- mates the conventional system of managing exams. EMS helps NDUB faculties, students, and other academic officers to manage the exam processing very easily and effectively and EMS can maintain mobility of these activities better than conventional manual systems. The main intentions of EMS are to conduct the exam and course-related activities very easily and rapidly. Comprehensively EMS has many interesting features altogether situated in a fair intended web application. EMS is designed using recent technologies like ‘Laravel 7.0’, ‘Bootstrap’, ‘JavaScript’, ‘jQuery’, ‘MySQL’. EMS’s working speed is very rapid and its database can take a massive user load at a time. EMS can be very useful for the exam and course-related operations of Notre Dame University Bangladesh.

Development of a Portable IP-Based Remote Controlled System for Mobile Robot

The use of Mobile Robots to interact with objects in remote locations has proved to be useful in areas not easily accessible or too dangerous for humans. Various means have been used to remotely operate or control Mobile Robots. These range from wired connection to Wireless connection like radio frequency signal and more recently internet controlled Mobile Robot using the TCP/IP protocol stack. However, the problem of remote control dependence on the Mobile Robot Platform or configuration has made it difficult to switch controllers between Mobile Robots. In this work, a portable IP based remote control system has been designed and implemented to remove the constraint imposed by the Mobile Robot's platform in choosing the control interface. The system developed was built on three loosely coupled components working together to ensure a high degree of Control interface portability. The Mobile Robot Gateway component was used to receive and send data from the Mobile Robot.

Artificial Heart Neural Networks – An Idea

Artificial Neural Networks Field (ANN Field) is an exciting field of research. ANN field took its inspiration from Human Brain. The heart and Brain are very important for the survival of Humans. Research Scientists published many articles by giving importance to Brain. But scientists have not yet explored much on the Heart which is another important part in addition to the Brain. The primary purpose of publishing this article is to show a path to ANN field Research Scientists by introducing the concept of “Heart” into Artificial Neural Networks. In this paper, we coined and defined “Artificial Heart Neuron,” which is the basic part of Artificial Heart Neural Networks Field (AHNN Field) in addition to Artificial Neuron. This work takes its inspiration from both Heart and Brain.

Detailed Analysis and Identification of Key Factors Resulting in Motor Accidents Across the UK

Motor accidents across the globe amount to a large number of deaths every year. The collisions result in not just the personal injury to people involved but also in the loss of money to the motor insurance companies, trauma to the people involved, and added pressure on the emergency services. With the help of data analytics techniques, this project aims to identify critical factors that might contribute to the accidents. Upon investigating the temporal features and geo-spatial features of the motor accident locations, we tried to establish a correlation between the accident intensity and its key factors. For this exploratory analysis, we also considered weather conditions and daily average traffic flow data. We then trained Supervised learning models on the data to find out the best performing multi-label classification model.

An Ambigramic Image File Format

We propose an image file format that can be read in two ways, where two images are recorded in a single file as a bit sequence, and the forward reading the bit sequence makes one of the two images visible, or the backward reading makes another image visible. Such a way of looking at a binary data in two ways resembles that of an ambigram, which is a piece of calligraphy that can be read in two ways by rotating it or introducing other perspectives. The proposed ambigramic image file format is compared with the graphics interchange format (GIF) experimentally, and the results show the better quality of the ambigramic images than that of GIF images.