Adaptive Boltzmann Medical Dataset Machine Learning

The RBM is a stochastic energy-based model of an unsupervised neural network (RBM). RBM is a key pre-training for Deep Learning. Structure of RBM includes weights and coefficients for neurons. Better network structure allows us to examine data more thoroughly, which is good. We looked at the variance of parameters in learning on demand to fix the problem. To determine why RBM's energy function fluctuates, we'll look at its parameter variance. A neuron generation and annihilation algorithm is smeared with an adaptive RBM learning method to determine the optimal number of hidden neurons for attribute imputation during training. When the energy function isn't converged and parameter variance is high, a hidden neuron is generated. If the neuron doesn't disrupt learning, it'll destroy the hidden neuron. In this study, some yardstick PIMA data sets were tested

The Concept of the Cryptocurrency and the Downfall of the Banking Sector in Reflecting on the Financial Market

This paper aims to explain the concept of Cryptocurrencies such as Bitcoin, Litecoin and others, discuss their advantages and their disadvantages, and how is reflecting on the financial market and on the other currencies that are adopted in each country, like us dollar and the euro and the pound and so on (fiat currencies). This paper will discuss the major change that would occur if or when cryptocurrencies get adopted worldwide when the cryptocurrency becomes the who will benefit from this change and who will cease to exist, and what would this change mean to the banking sector, and where is it going to leave this sector. Could it destroy it or be a partner with it? It all depends on how this sector will handle this change when it happens because it will happen sooner or later

Encrypted Network Traffic Classification and Resource Allocation with Deep Learning in Software Defined Network

The climate has changed absolutely in every area in just a few years as digitized, making high-speed internet service a significant need in the future. Future Internet is supposed to face exponential growth in traffic, and highly complicated infrastructure, threatening to make conventional NTC approaches unreliable and even counterproductive. In recent days, AI Stimulated state-of-the-art breakthroughs with the ability to tackle extensive and multifarious challenges, and the network community is initiated by considering the NTC prototype from legacy rule-based towards a novel AI-based. Design and execution are applied to interdisciplinary become more essential. A smart home network supports various applications and smart devices within the proposed work, including e-health devices, regular computing devices, and home automation devices. Many devices accessible through the Internet by Home GateWay for Congestion (HGC) in a smart home. Throughout this paper, a Software-Defined Network Home GateWay for Congestion (SDNHGC) architecture for improved management of remote smart home networks and protection of the significant networks SDN controller. It enables effective network capacity regulation, focused on real-time traffic analysis and core network resource allocation. It cannot control the Network in dispersed smart homes. Our innovative SDNHGC expands power across the connectivity network, a smart home network enabling improved end-to-end monitoring of networks. The planned SDNHGC directly will gain centralized device identification by classifying traffic through a smart home network. Several of the current traffic classifications approach, checking deep packets, cannot have this real-time device knowledge for encrypted data to solve this issue

The Impact of Leadership Styles on Employees Productivity in Organizations: A Comparative Study Among Leadership Styles

Successful leaders are facilitators who specifically target skilled and committed workers. Studies in organizational psychology and the research on an organizations actions suggest that management styles and staff motivation are key factors of business performance or failure. This study aimed to study the influence of leadership on the performance of employers in the education ministry and higher education of Somaliland and to define four types, namely, autocratic, transformational, democratic and transactional leadership. This study analyzed the effects of leadership styles on employee efficiency. The final findings have shown that the autocratic leadership model has a detrimental effect on the departments efficiency, which is reflected in high absenteeism, poor morale, decline of job satisfaction, and rotation. The application of egalitarian, transformative and transactional leaders have a positive and important effect on the success of workers assessed by the high morale, efficiency, engagement, and dedication of the employees. The analysis aimed to explore the impact of leadership styles on employee achievement in the Somaliland Ministry of Education and Superior Studies and to define four major styles: autocratic, transformative, democratic and transactional management styles. Consequently, it is possible to assume that democratic, transactional and disruptive leadership has a positive connection to employee performance. In contrast, autocratic models often have a negative relation to employee results. It should also follow and further enhance its powerful aspects of practicing those features of democratic leadership that contribute positively to the companys success

Services on Multinationals Operating in Different Countries in Automation and Performance in Organizations as A New Way of Increasing Profit and Cutting Costs

The thesiss main purpose is to focus shared services on multinationals operating in different countries and take the automation process as a new way of increasing profit and cutting costs. However, on the other hand, the effect of automation on employment will be targeted. The thesis project is focused on papers that detail the above measures. They are combined, and the primary goal of the analysis is to illustrate that technology cannot substitute people. Does the research include the methodology for determining what a study report is? And what are the numerous kinds? Finally, it is shown that automation is efficient for businesses but cannot replace people on the other hand because creativity and the ability to develop new processes can never be at hand. We chose AZADEA for research support. We interviewed the operations manager and HR team semi-structured to show that although the shared service process is being implemented, it is important to keep our staff there

Utilizing Index‑Based Periodic High Utility Mining to Study Frequent Itemsets

The potential employability in diferent applications has garnered more signifcance for Periodic High-Utility Itemset Mining (PHUIM). It is to be noted that the conventional utility mining algorithms focus on an itemset’s utility value rather than that of its periodicity in the transaction. A MEAN periodicity measure is added to the minimum (MIN) and maximum (MAX) periodicity to incorporate the periodicity feature into PHUIM in this proposed work. The MEAN-periodicity measure brings a new dimension to the periodicity factor and is arrived at by dividing itemset’s period value by the total number of transactions in that dataset. Further, an algorithm to mine Index-Based Periodic High Utility Itemset Mining (IBPHUIM) from the database using an indexing approach is also proposed in this paper. The proposed IBPHUIM algorithm employs a projectionbased technique and indexing procedure to increase memory and execution speed efciency. The proposed model avoids redundant database scans by generating sub-databases using an indexing data structure. The proposed IBPHUIM model has experimented with test datasets, and the results drawn show that the proposed IBPHUIM model performs considerably better

Multilabel land cover aerial image classification using convolutional neural networks

Classifying the remote sensing images requires a deeper understanding of remote sensing imagery, machine learning classification algorithms, and a profound insight into satellite images’ know-how properties. In this paper, a convolutional neural network (CNN) is designed to classify the multispectral SAT-4 images into four classes: trees, grassland, barren land, and others. SAT-4 is an airborne dataset that captures the images in 4 bands (R, G, B, infrared). The proposed CNN classifier learns the image’s spectral and spatial properties fromthe ground truth samples provided. The contribution of this paper is three-fold. (1) A classification framework for feature extraction and normalization is built. (2) Nine different architectures of models are built, and multiple experiments are conducted to classify the images. (3) A deeper understanding of the image structure and resolution is captured by varying different optimizers inCNN. The correlation between images of varying classes is identified. The experimental study shows that vegetation health is predicted most accurately by the proposed CNN models. It significantly differentiates the grassland vegetation from tree vegetation, which is better than other classical methods. The tabulated results show that a state-of-the-art analysis is done to learn varying landcover classification models

The empirical results of conditional analysis of principals' reasons in bullying teachers

Starting from the bullying of teachers by principals, this paper elaborates (a) how incompetency of management favours its emergence, (b) how teachers can see it, and (c) whether this problem affects the performance of teachers or not. The empirical results show that motivation, a positive workplace, and not being bullied or agitated by principals increase teachers' performance. The findings show that the teachers consider management's incompetency the major factor to be blamed. Collaboration between teachers can have a role in limiting this abuse. Because management is the key obligation for clearing the ethos and function of the company and clarifying the translation of words into the organization, there is some laggard of management in the way the control extracted from the structured authority is confused. Success strain!! In the light of the short- and long-term priorities and plans

The Empirical Results of Conditional Analysis of Principals Reasons in Bullying Teachers

Starting from the bullying of teachers by principals, this paper elaborates (a) how incompetency of management favours its emergence, (b) how teachers can see it, and (c) whether this problem affects the performance of teachers or not. The empirical results show that motivation, a positive workplace, and not being bullied or agitated by principals increase teachers performance. The findings show that the teachers consider managements incompetency the major factor to be blamed. Collaboration between teachers can have a role in limiting this abuse. Because management is the key obligation for clearing the ethos and function of the company and clarifying the translation of words into the organization, there is some laggard of management in the way the control extracted from the structured authority is confused. Success strain!! In the light of the short- and long-term priorities and plans, colleges and organized entity made up of people operating, supervised and operationally, can operate every phase so the activities can be orchestrated and integrated in an equilibrated manner

IoT Based Virtual E‑Learning System for Sustainable Development of Smart Cities

Globally, cities are emerging into Smart Cities (SC) as a result of sustainable cities and the adaption of recent Internet of Things (IoT) technology. It is becoming essential to involve students in sustainability as engineering and technology are crucial elements in fixing the past adverse effects on our globe. Engineering e-learners are being educated on the sustainable development of SC in many Smart e-learning Tools (SeT) and infrastructure faculties around the world, especially in developing Asian countries such as India. This research paper presents an advanced solution for interactive Smart Learning Environment (SLE) systems based on new emerging technological trends of the IoT. The IoT-Ve- LS method used in the design and implementation allows flexible usage and integration of the online courses by SLE. The impacts of empirical E-learning evaluation on implementing IoT techniques in online tutoring have been analysed to find out its research hypothesis. Our IoT-sensor-based Reservoir Computing allows the classification of short-term learning language sentences relatively quickly, highlighting the minimal training time and optimized solution of real-time cases for controlling temporal and sequential signals at the cloud computing level. The triangulation analysis in information gathering endorses the theoretical models that use computable and personalized approaches