Recycling is one of the most important approaches to safeguard the environment since it aims to reduce waste in landfills while conserving natural resources. Using deep Learning networks, this group of wastes may be automatically classified on the belts of a waste sorting plant. However, a basic set of connected layers may not be adequate to give satisfactory accuracy for such multi output classifier tasks. To optimize the gradient flow and enable deeper training for network design with multi label classifier, this study suggests a residual-based deep learning convolutional neural network. For network training, ten classes have been explored. The Directed Acyclic Graph (DAG) is a structure with hidden layers that have inputs, outputs, and other layers. The DAG network's residual-based architecture features shortcut connections that bypass some levels of the network, allowing gradients of network parameters to travel freely among the network output layers for deeper training. The methodology includes: 1) preparing the data and creating an augmented image data store; 2) defining the main serially-connected branches of the network architecture; 3) defining the residual interconnections that bypass the main branch layers; 4) defining layers, and finally; 5) creating a residual-based deeper layer graph. The concept is to split down the multiclass classification problem into minor binary states, where every classifier performs as an expert by concentrating on discriminating between only two labels, improving total accuracy. The results achieve (2.861 %) training error and (9.76 %) a validation error. The training results of this classifier are evaluated by finding the training error, validation error, and showing the confusion matrix of validation data

One of the most important topics in the last decade is the Big Data (BD) and how to link it and benefit from its consumption in different fields, included as the introduction in this research analysis of the BD belonging to devices of the Internet of Things. The concept of managing objects and exploring devices is connected to the Internet and sensors deployed in the world, all these devices are pumping a lot of data through the Internet of Things (IoT) into the world. In order to make the right decisions for people and things, BD using data mining techniques and machine language algorithms help make decisions. The Internet of Things that insert large amounts of data need to be studied, analysed and disseminated in order to access valuable, useful and bug-free information for the purpose of making the right decision and avoiding problems. In this paper, two clustering algorithms simple K-means and self-organising map (SOM) in IoT are presented. Next, comparing the clustering models’ output in the IoT data set that improved the SOM is better than K-means, but it is slower in creating the model.   Keywords: Internet of things (IoT), big data, machine learning, filtered cluster, K-means, SOM.

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Internet of things (IoT) becomes the most popular term in the recent advances in Healthcare devices. The healthcare data in the IoT process and structure is very sensitive and critical in terms of healthy and technical considerations. Outlier detection approaches are considered as principal tool or stage of any IoT system and are mainly categorized in statistical and probabilistic, clustering and classification-based outlier detection. Recently, fuzzy logic (FL) system is used in ensemble and cascade systems with other ML-based tools to enhance outlier detection performance but its limitation involves the false detection of outliers. In this paper, we propose a fuzzy logic system that uses the anomaly score of each point using local outlier factor (LOF), connectivity-based outlier factor (COF) and generalized LOF to eliminate the confusion in classifying points as outliers or inliers. Regarding human activity recognition (HAR) dataset, the FL achieved a value of 98.2 %. Compared to the performance of LOF, COF, and GLOF individually, the accuracy increased slightly, but the increase in precision and recall indicates an increase in correctly classified data and that neither true nor abnormal data is classified wrongly. The results show the increase in precision and recall which indicates an increase in correctly classified data. Thus, it can be confirmed that fuzzy logic with input of scores achieved the desired goal in terms of mitigating cases of false detection of anomalous data. By comparing the proposed ensemble of fuzzy logic and different types of local density scores in this study, the outcomes of fuzzy logic presents a new way of elaborating or fusing the different tools of the same purpose to enhance detection performance

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