Businesses are the driving force behind economic systems and are the lifeblood of the community. A business shares striking similarity to a living organism, including birth, infancy, rising, prosperity, and falling. The success of a business is not only important to the owners, but is also critical to the regional/domestic economic system, or even the global economy. Recent years have witnessed many new emerging businesses with tremendous success, such as Google, Apple, Facebook etc., yet millions of businesses also fail or fade out within a rather short period of time. Finding patterns/factors connected to the business rise and fall remains a long lasting question puzzling many economists, entrepreneurs, and government officials. Recent advancement in artificial intelligence, especially machine learning, has lend researchers powers to use data to model and predict business success. However, due to data driven nature of all machine learning methods, existing approaches are rather domain-driven and ad-hoc in their design and validations. In this paper, we propose a systematic review of modeling and prediction of business success. We first outline a triangle framework to showcase three parities connected to the business: Investment-Business-Market (IBM). After that, we align features into three main categories, each of which is focused on modeling a business from a particular perspective, such as sales, management, innovation etc., and further summarize different types of machine learning and deep learning methods for business modeling and prediction. The survey provides a comprehensive review of computational approaches for business performance modeling and prediction.

Melanoma is a type of skin cancer that usually develops rapidly and can spread to other parts of the body, causing death or complication of treatment to a large population. Early detection of Melanoma is the key to increase the chances of patients' survival. While accurate Melanoma diagnosis can be performed through clinical examination and dermatology tests, such procedures are usually time-consuming, costly, and severely delayed due to patients' emotional aspects or other obstacles. Recently, machine learning approaches, deep learning, in particular, have shown great potential in diagnosing Melanoma using images captured through the camera. Accurate detection of Melanoma using low-end images with machine learning delivers a solution for rapid screening of Melanoma without clinical visits or experts. Although many deep learning methods can be applied to Melanoma detection, there exists a large variance between their performance, given that their parameters, such as learning rates, optimizers, batch size, etc., always differ. In this paper, we carry out a systematic study to validate a more precise deep learning framework for detecting Melanoma and other types of skin lesions. A generic Convolutional Neural Network (CNN) is performed, and transfer learning using a pre-trained framework is proved to help improve the detection accuracy. In addition, data augmentation is applied, which improves the model performance. A series of parameters for the learning rates, batch size, and optimizers etc., are tested within the models. Our study shows tremendous improvement in Melanoma detection with higher accuracy, which can be very useful for medical experts to provide efficient Melanoma detection to patients.

The success of the business directly contributes towards the growth of the nation. Hence it is important to evaluate and predict whether the business will be successful or not. In this study, we use the company’s dataset which contains information from startups to Fortune 1000 companies to create a machine learning model for predicting business success. The main challenge of business success prediction is twofold: (1) Identifying variables for defining business success; (2) Feature selection and feature engineering based on Investor-Business-Market interrelation to provide a successful outcome of the predictive modeling. Many studies have been carried out using only the available features to predict business success, however, there is still a challenge to identify the most important features in different business angles and their interrelation with business success. Motivated by the above challenge, we propose a new approach by defining a new business target based on the definition of business success used in this study and develop additional features by carrying out statistical analysis on the training data which highlights the importance of investments, business, and market features in forecasting business success instead of using only the available features for modeling. Ensemble machine learning methods as well as existing supervised learning methods were applied to predict business success. The results demonstrated a significant improvement in the overall accuracy and AUC score using ensemble methods. By adding new features related to the Investor-Business-Market entity demonstrated good performance in predicting business success and proved how important it is to identify significant relationships between these features to cover different business angles when predicting business success.

A paradigm for high-performance computing services, cloud computing integrates the latest developments in distributed computing, virtualization, load balancing, parallel processing, network storage, and hot backup redundancy. In light of the fact that there is currently no reliable method for DAG task scheduling that guarantees a balanced distribution of resources across nodes, this study suggests an approach for LB algorithms in cloud computing. This research presents a new paradigm for resource selection and job scheduling, which is abstracted from swarm intelligence such as PSO, SA, Cuckoo search, etc. The LBA tackles critical issues with the system under investigation, such as system overhead or resource limits, with the goal of achieving lowest makespan and execution time while concurrently improving resource usage. This component is incorporated in the CloudSim simulation environment where the framework measures performance under different cloud models, task intensity and resource provisioning. Experimental outcomes prove that an LBA is better than an existing algorithm, where it gives an average makespan of 894.85ms, execution time of 614.88ms, and resource utilization was 69%. Comparative analysis with PSO and CSSA confirms the superior efficiency of LBA in maximizing resource allocation. These results highlight the possibility of optimization methods derived from nature to improve cloud performance by means of efficient scheduling and load balancing.