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Nowadays, Data Mining is used everywhere for extracting information from the data and in turn, acquires knowledge for decision making. Data Mining analyzes patterns which are used to extract information and knowledge for making decisions. Many open source and licensed tools like Weka, RapidMiner, KNIME, and Orange are available for Data Mining and predictive analysis. This paper discusses about different tools available for Data Mining and Machine Learning, followed by the description, pros and cons of these tools. The article provides details of all the algorithms like classification, regression, characterization, discretization, clustering, visualization and feature selection for Data Mining and Machine Learning tools. It will help people for efficient decision making and suggests which tool is suitable according to their requirement.

Emotions are critical in people's daily lives since their decision making, interaction, intelligence, and perception are all influenced by the emotions they display. Emotion recognition with machine learning based on EEG signals has been an exciting topic and employed in several areas, such as health care, social security, and safe driving. In this paper, a review on emotion recognition using EEG signals employing machine learning is carried out based on various factors, such as the stimulus used, equipment, modalities, filters, features, classifiers, and detected emotions, along with the limitations. This paper identifies the basic methodology used in the emotion recognition process with various tools and technologies utilized in it. Finally, it gives the issues and challenges for future research directions.

With the increase in real‐time latency‐sensitive Internet of Things (IoT) applications, a huge amount of data is generated in the Fog‐IoT paradigm. There is a need to schedule and execute this huge workload over Fog devices efficiently to support the increasing demand of these applications. But, Fog devices are resource‐constrained in terms of processing/computing power, bandwidth as well as storage capacity which makes tuple scheduling a challenging problem. Moreover, due to the rise in IoT devices per application, a sharp increase in service response time, network congestion, and inefficiency in terms of energy consumption, and execution cost has been observed. Consequently, an efficient tuple scheduling algorithm is desirable that can reduce latency and network usage and optimize energy consumption and cost. Therefore, in this work, CaPTS scheduler: A Context‐aware Priority Tuple Scheduling for Fog computing paradigm is designed and proposed. It takes into consideration various context‐aware parameters such as task load of application, networking requirement, and data flow rate to set the priority of tuples and schedule them across Fog computing nodes while ensuring quick service response time and satisfying quality of service requirements of end‐users. The CaPTS scheduler is implemented and evaluated using iFogSim toolkit on various performance metrics such as latency, network usage, energy consumption, and cost. Its performance is validated through a case study on the smart mining industry system. The results show that on an average the latency and network usage are minimized by 35.93% and 44.20%, while energy consumption and cost are optimized by 4.55% and 30.92%, respectively, in comparison with baseline techniques.