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Abstract—After a very fast and efficient discriminative broad learning system (BLS) that takes advantage of flatted structure and incremental learning has been developed, here, a mathematical proof of the universal approximation property of BLS is provided. In addition, the framework of several BLS variants with their mathematical modeling is given. The variations include
cascade, recurrent, and broad–deep combination structures. From the experimental results, the BLS and its variations outperform several exist learning algorithms on regression performance over function approximation, time series prediction, and face recognition databases. In addition, experiments on the extremely challenging data set, such as MS-Celeb-1M, are given. Compared with other convolutional networks, the effectiveness and efficiency of the variants of BLS are demonstrated

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ABSTRACT ;
The detection of abnormal behaviour in crowded scenes has to deal with many challenges. This paper presents an efficient method for detection and localization of anomalies in videos. Using fully convolutional neural networks
(FCNs) and temporal data, a pre-trained supervised FCN is transferred into an unsupervised FCN ensuring the detection of (global) anomalies in scenes. High performance in terms of speed and accuracy is achieved by
investigating the cascaded detection as a result of reducing computation complexities. This FCN-based architecture addresses two main tasks, feature representation and cascaded outlier detection. Experimental results on two benchmarks suggest that the proposed method outperforms existing methods in terms of accuracy regarding detection and localization.

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Abstract—In this paper, we focus on the one-example person re-identification (re-ID) task, where each identity has only one labeled example along with many unlabeled examples. We propose a progressive framework which gradually exploits the unlabeled data for person re-ID. In this framework, we iteratively (1) update the Convolutional Neural Network (CNN) model and (2) estimate pseudo labels for the unlabeled data. We split the training data
into three parts, i.e., labeled data, pseudo-labeled data, and indexlabeled data. Initially, the re-ID model is trained using the labeled data. For the subsequent model training, we update the CNN model by the joint training on the three data parts. The proposed joint training method can optimize the model by both the data with labels (or pseudo labels) and the data without any reliable labels. For the label estimation step, instead of using a static
sampling strategy, we propose a progressive sampling strategy to increase the number of the selected pseudo-labeled candidates step by step. We select a few candidates with most reliable pseudo labels from unlabeled examples as the pseudo-labeled data, and keep the rest as index-labeled data by assigning them with the data indexes. During iterations, the index-labeled data are dynamically transferred to pseudo-labeled data. Notably, the
rank-1 accuracy of our method outperforms the state-of-the-art method by 21.6 points (absolute, i.e., 62.8% vs. 41.2%) on MARS, and 16.6 points on DukeMTMC-VideoReID. Extended to the few-example setting, our approach with only 20% labeled data surprisingly achieves comparable performance to the supervised state-of-the-art method with 100% labeled data

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ABSTRACT :

signals recorded in a room are commonly degraded by reverberation. In most cases, both the speech signal and the acoustic system of the room are unknown and time-varying. Eliminating this effect without affecting the original quality of the speech is a challenge of research in present days. The process of DE noising input speech signals is more helpful in the process of providing efficient sound system. Fast adaptive Kalman filter is designed for the removal of the noises in the signals.

Fast adaptive Kalman filtering is employed for the removal of the noises from the signal which is based on the prediction and estimation of the noise level in the signal. The input speech signal is denoised with the help of the Fast adaptive Kalman filter. State transition and observation models need not be linear functions of the state but may instead be non-linear functions. The function can be used to compute the predicted state from the previous estimate and similarly the function can be used to compute the predicted measurement from the predicted state.

The nonlinear functions employed for the prediction and estimation process improves the performance of the adaptive kalman filter process. The non-linear functions defined for the kalman filtering process is designed so that the process is reduced in iterations and more adaptive. And using signal subspace algorithm.

The filtered signal and the original signal is then compared in order to measure the performance of the process. The performance is measured with the help of performance metrics like SNR

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ABSTRACT Machine learning techniques are being widely used to develop an intrusion detection system(IDS) for detecting and classifying cyber-attacks at the network-level and host-level in a timely andautomatic manner. However, many challenges arise since malicious attacks are continually changing and areoccurring in very large volumes requiring a scalable solution. There are different malware datasets available
publicly for further research by cyber security community. However, no existing study has shown thedetailed analysis of the performance of various machine learning algorithms on various publicly available
datasets. Due to the dynamic nature of malware with continuously changing attacking methods, the malware data sets available publicly are to be updated systematically and benchmarked. In this paper, deep neural
network (DNN), a type of deep learning model is explored to develop a flexible and effective IDS to detectand classify unforeseen and unpredictable cyber-attacks. The continuous change in network behaviour and rapid evolution of attacks makes it necessary to evaluate various datasets which are generated over the years through static and dynamic approaches. This type of study facilitates to identify the best algorithm
which can effectively work in detecting future cyber-attacks. A comprehensive evaluation of experiments of DNNs and other classical machine learning classifiers are shown on various publicly available benchmark malware datasets. The optimal network parameters and network topologies for DNNs is chosen through following hyper parameter selection methods with KDDCup 99 dataset. All experiments of DNNs are run till 1,000 epochs with learning rate varying in the range [0.01-0.5]. The DNN model which performed well on KDDCup 99 is applied on other datasets such as NSL-KDD, UNSW-NB15, Kyoto, WSN-DS and
CICIDS 2017 to conduct the benchmark. Our DNN model learns the abstract and high dimensional feature representation of the IDS data by passing them into many hidden layers. Through a rigorous experimental
testing it is confirmed that DNNs perform well in comparison to the classical machine learning classifiers. Finally, we propose a highly scalable and hybrid DNNs framework called Scale-Hybrid-IDS-AlertNet
(SHIA) which can be used in real time to effectively monitor the network traffic and host-level events to proactively alert possible cyber-attacks.

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Abstract—Online social networks provide relevant information on users’ opinion about different themes. Thus, applications, such as monitoring and recommendation systems (RS) can collect and analyze this data. This paper
presents a knowledge-based recommendation system (KBRS), which includes an emotional health monitoring system to detect users with potential psychological disturbances, specifically, depression and stress. Depending onthe monitoring results, the KBRS, based on ontologies and
sentiment analysis, is activated to send happy, calm, relaxing, or motivational messages to users with psychological disturbances. Also, the solution includes a mechanism to send warning messages to authorized persons, in casea depression disturbance is detected by the monitoringsystem. The detection of sentences with depressive andstressful content is performed through a convolutional neural network and a bidirectional long short-term memory - recurrent neural networks (RNN); the proposed method reached an accuracy of 0.89 and 0.90 to detect depressed and stressed users, respectively. Experimental results show that the proposed KBRS reached a rating of 94% of very satisfied users, as opposed to 69% reached by a RS
without the use of neither a sentiment metric nor ontologies. Additionally, subjective test results demonstrated that the proposed solution consumes low memory, processing, and energy from current mobile electronic devices.

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ABSTRACT Malicious software or malware continues to pose a major security concern in this digital age as computer users, corporations, and governments witness an exponential growth in malware attacks. Current malware detection solutions adopt Static and Dynamic analysis of malware signatures and behaviour patterns that are time consuming and ineffective in identifying unknown malwares. Recent malwares use polymorphic, metamorphic and other evasive techniques to change the malware behaviours quickly and to generate large number of malwares. Since new malwares are predominantly variants of existing malwares, machine learning algorithms (MLAs) are being employed recently to conduct an effective malware analysis. This requires extensive feature engineering, feature learning and feature representation. By using the advanced MLAs such as deep learning, the feature engineering phase can be completely
avoided. Though some recent research studies exist in this direction, the performance of the algorithms is biased with the training data. There is a need to mitigate bias and evaluate these methods independently
in order to arrive at new enhanced methods for effective zero day malware detection. To fill the gap in literature, this work evaluates classical MLAs and deep learning architectures for malware detection, classification and categorization with both public and private datasets. The train and test splits of public and private datasets used in the experimental analysis are disjoint to each others and collected in different timescales. In addition, we propose a novel image processing technique with optimal parameters for MLAs and deep learning architectures. A comprehensive experimental evaluation of these methods indicate that deep learning architectures outperform classical MLAs. Overall, this work proposes an effective visual
detection of malware using a scalable and hybrid deep learning framework for real-time deployments. The visualization and deep learning architectures for static, dynamic and image processing based hybrid
approach in a big data environment is a new enhanced method for effective zero-day malware detection. INDEX TERMS Cyber Security, Cybercrime, Malware detection, Static and Dynamic analysis, Artificial Intelligence, Machine Learning, Deep Learning, Image processing, Scalable and Hybrid framework
I. INTRODUCTION
In this digital world of Industry 4.0, the rapid advancement
of technologies has affected the daily activities in businesses
as well as in personal lives. Internet of Things (IoT) and
applications have led to the development of the modern
concept of the information society. However, security concerns
pose a major challenge in realising the benefits of
this industrial

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ABSTRACT :

Effective plant growth and yield prediction is an essential task for greenhouse growers and for agriculture in general. Developing models which can effectively model growth and yield can help growers improve the environmental control for better production, match supply and market demand and lower costs. Recent developments in Machine Learning (ML) and, in particular, Deep Learning (DL) can provide powerful new analytical tools. The proposed study utilises ML and DL techniques to predict yield and plant growth variation across two different scenarios, tomato yield forecasting and Ficus benjamina stem growth, in controlled greenhouse environments. We deploy a new deep recurrent neural network (RNN), using the Long Short-Term Memory (LSTM) neuron model, in the prediction formulations. Both the former yield, growth and stem diameter values, as well as the microclimate conditions, are used by the RNN architecture to model the targeted growth parameters. A comparative study is presented, using ML methods, such as support vector regression and random forest regression, utilising the mean square error criterion, in order to evaluate the performance achieved by the different methods. Very promising results, based on data that have been obtained from two greenhouses, in Belgium and the UK, in the framework of the EU Interreg SMARTGREEN project (2017-2021), are presented.

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ABSTRACT

In present educational systems, student performance prediction is getting worsen day by day. Predicting student performance in advance can help students and their teacher to keep track of progress of a student. Many institutes have adopted continuous evaluation system today. Such systems are beneficial to the students in improving performance of a student. The purpose of continuous evaluation system is to help regular students.
In recent years, Neural Networks have seen widespread and successful implementations in a wide range of data mining applications, often surpassing other classifiers. This study aims to investigate if Neural Networks are a fitting classifier to predict student performance from
Learning Management System data in the context of Educational Data Mining. To assess theapplicability of Neural Networks, we compare their predictive performance against six other classifiers on this dataset. These classifiers are Naive Bayes, k-Nearest Neighbors, Decision Tree, Random Forest, Support Vector Machine and Logistic Regression and will be trained on data obtained during each course. The features used for training originate from LMS data obtained during the length of each course, and range from usage data like time spent on each course page, to grades obtained for course assignments and quizzes. After training, the Neural
Network outperforms all six classifiers in terms of accuracy and is on par with the best classifiers in terms of recall. We can conclude that Neural Networks outperform the six other algorithms tested on this dataset and could be successfully used to predict student performance.

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