Search

Shredded document recovery is an important research sub-field of information security. The paper document is broken into a large number of similar shreds by the shredder. Usually these shreds imply some important information. If they are restored, it will have an important impact on judicial investigation, military command, and archaeological research. Therefore, it has a great value in research of shredded document recovery. In consideration of large differences between languages and great demands for Chinese document recovery, our study will focus on the shredded Chinese document. In this paper, a method of shredded document recovery based on character stroke feature is proposed. We use horizontal strokes to match Chinese characters. Finally the shredded document is restored. In the test of actual sample, the method proposed in this paper has achieved good recovery result. The average accuracy is 13.96% higher than the traditional algorithm.

The development of the Internet of Things over the last decade has led to large amounts of data being generated at the network edge. This highlights the importance of local data processing and reasoning. Machine learning is most commonly used to automate tasks and perform complex data processing and reasoning. Collecting such data in a centralized location has become increasingly problematic in recent years due to network bandwidth and data privacy concerns. The easy-to-change behavior of edge infrastructure enabled by software-defined networking (SDN) allows IoT data to be gathered on edge servers and gateways, where federated learning (FL) can be performed: creating a centralized model without uploading data to the cloud. In this paper, we analyze the use of edge computing and federated learning, a decentralized machine learning methodology that increases the amount and variety of data used to train deep learning models. To the best of our knowledge, this paper reports the first use of federated learning to help the Microgrid Energy Management System (EMS) predict load and obtain promising results. Simulations were performed using TensorFlow Federated with data from a modified version of the Dataport site.

Conventionally, majority physicians used manual-based approach to determine heart sound parameters by manually printing heart sound signal waveform on the paper and trace heart sound parameters duration. Towards modern medical diagnostic technologies, this paper presents a modified framework of automated heart sound signal segmentation and parameters identification for deployment in computer aided auscultation. Envelope-based approach with reference information by abrupt changes and peak detection algorithm are proposed for PASCAL Classifying Heart Sounds Challenge database. Noted that the performance of segmentation process was measured by calculating the accuracy and F1-score while the heart sound parameters were identified by computing its mean and standard deviation. The findings yield F1-score and accuracy of proposed approach for segmentation and identification of normal heart sound signal at 95.29% and 91.0%, respectively. Heart sound parameters such as first sound, S1 duration, second sound, S2 duration, systole duration, diastole duration, heart cycle duration and ratio of systole and diastole were also determined. This proposed approach is suitable to be applied on two prominent heart sound signal peaks of S1 and S2, which continuously varies among samples due to the different auscultatory sites.

Recently, two types of common sensors, LiDAR and Camera, show significant performance on all tasks in 3D vision. LiDAR provides accurate 3D geometry structure, while camera captures more scene context and semantic information. The fusion of two different sensor becomes a fundamental and common idea to achieve better performance. To give a thorough cognition of the complementary and boosting about two kind of sensors. This paper briefly reviews the fusion and enhancement systems between both two sensors in the field of depth completion, 3D object detection, 2D\3D semantic segmentation and 3D object tracking. Meanwhile, the state of art fusion algorithms is quantitatively demonstrated, in this paper, based on the in KITTI widely-used public dataset. Furthermore, the technical challenge and the future potential of LiDAR and camera fusion are also discussed.

With the rapid development of deep learning and structures of neural networks, parameter settings of trained neural networks become new products from knowledge. Since supervised learning is used in 85% of machine learning to train the neural networks, well-labeled training data becomes new knowledge capital. In order to stimulate the knowledge sharing and protect the Intelligent Property (IP) right and privacy of knowledge capital at the same time, we need a framework to conduct training of neural networks with certain encrypted data and exchange training results with certain rewards. Federated learning provides a framework to train the networks without disclose the privacy and a block-chain based transaction system can organize a group of participants under certain consensus and reward mechanism. Their combination is an effective solution to manage knowledge capital, called knowledge capital bank in this paper. This paper defines the economic meaning of knowledge capital and all behaviors of related participants. Then we propose a block-chain based federated learning algorithm for secured knowledge sharing with IP and privacy protection. Finally, a complete design for a use case of this algorithm called Knowledge capital bank is demonstrated.

The purpose of current paper is to create a smart and effective tool for telemedicine to early detect and diagnose COVID-19 disease and therefore help to manage Pandemic Crisis (MCPC) in Sultanate of Oman, as a tool for future pandemic containment. In this paper, we used tools to create robust models in real-time to support Telemedicine, it is Machine Learning (ML), Deep Learning (DL), Convolutional Neural Networks using Tensorflow (CNN-TF), and CNN Deployment. These models will assist telemedicine, 1) developing Automated Medical Immediate Diagnosis service (AMID). 2) Analysis of Chest X-rays image (CXRs). 3) Simplifying Classification of confirmed cases according to its severity. 4) Overcoming the lack of experience, by improving the performance of medical diagnostics and providing recommendations to the medical staff. The results show that the best Regression among the five Regression models is Random Forest Regression. while the best classification among the eight classification models and Recurrent Neural Network using Tensorflow (RNNTF) is Random Forest classification, and the best Clustering model among two Clustering models is K-Means++. Furthermore, CNN-TF model was able to discriminate between those with positive cases Covid-19 and those with negative cases.

Natural Language Processing (NLP) has witnessed considerable advances in textual understanding through statistical and rule-based techniques, which have now yielded to developments in the field of neural networks and deep learning. The paper surveys research relevant to the topic of Reading Comprehension and Question Answering (QA) implementations. The initial focus of the paper is on Attention and Transformer models. A brief description of the architectures is presented highlighting the essence of the ‘Attention is all you Need’ paper wherein the authors of Bidirectional Encoding Transformer (BERT) have elucidated the significant departure from the recurrence concept of Recurrent Neural Networks (RNN). Subsequently the trends in Open Domain Question Answering (ODQA) which mark the progression from the passage- based question answering is presented. Of particular interest is Haystack which is an end-to-end open-source framework for Question Answering & Neural search. This field seems a promising avenue for a more intelligent form of ‘search’. In a nutshell, the paper weaves through RNNs, Long Short-Term Memory (LSTM), and the currently trending Attention based Transformer models in NLP. Finally, we dwell on more contemporary pieces of research such as ODQA, Multi-Hop QA, evaluation using Adversarial Networks.

Iris and face are two most popular biometric traits. Unfortunately, their performance can be degraded when these systems encountered with non-ideal images. The primary motivation of this paper is to analyze the usability of a new feature rich biometric trait known as periocular region (nearby region around the eye) for two different non-ideal scenarios 1) Matching of images with different pose variation and 2) Matching of different side of periocular images (Left eye to Right eye & Right eye to Left eye). In this paper, seven different off-the-shelf deep learning based Convolutional Neural Networks (CNN) using transfer learning approach are implemented on UBIPr database. Experimental results demonstrated that for images with pose variation VGG 19 obtained maximum recognition accuracy while for matching of two different side of periocular region Res Net 18 outperform others. As an additional contribution, a novel approach for the use of mirror images to improve the matching accuracy of two different side of periocular region is also presented.

Sentiment Analysis deals with analysing the reviews stated by its consumers for any product. If such analysis is performed at a deeper level, it enables us to identify the consumer’s sentiment towards each feature of the product. The sentiment expressed may not be same towards each feature. The analysis of this sort is called Aspect Based Sentiment Analysis (ABSA) and it has been sub-divided into four subtasks. In this paper, the detailed study of the approaches used for the first subtask of ABSA, i.e. Aspect Term Extraction (ATE) is presented. This paper discusses how ATE can be performed for the reviews in a rich morphological language, like Hindi. The models proposed for ATE of Hindi Reviews are Conditional Random Field (CRF) and Bidirectional Long-Short-Term-Memory (Bi-LSTM) models with novel architecture. The CRF based approach with novel feature, ‘Cluster-id’ improved F-measure from 41.07% to 42.71%. However, with 5-fold cross-validation, the CRF model attained an F-measure of 44.54%. By using our proposed Bi-LSTM based model with PoS vector, the F-measure obtained is 44.49%.

With the rapid development of Unmanned Aerial Vehicle (UAV), stealth warship and stealth tank, the mission of radar has become more difficult. The modern radar system must detect and track target through strong clutter from many aspect, such as ground and sea. Radar detection for low slow small （LSS）targets has gradually become a new focus. LSS target has a small Radar Cross Section (RCS) because of its small size, making the SNR of the echo signal very low. At the same time, the Doppler frequency of slow target is very low, making it difficult for conventional signal processing methods to separate the Doppler information from the clutter spectrum. Thus, it does make sense to study how to detect the LSS target in strong clutter efficiently. In this paper, the distribution characteristics of radar clutter and coherent accumulation algorithm for LSS target detection in complex electromagnetic environment are studied. On the base of that, a double-threshold target detection method based on coherent accumulation is proposed. To check out the efficiency of the detection method, an experiment has been conducted, and the data collected was used to test the detection algorithm, and the result has been shown in this paper.

Assisting table tennis coaching using modern technologies is one of the most trending researches in the sports field. In this paper, we present a methodology to identify and recognize the wrong strokes executed by players to improve the training experience by the usage of an IR depth camera. The proposed system focuses mainly on the errors in table tennis player’s strokes and evaluating them efficiently and based on the analysis and classification of the data obtained from an IR depth camera using multiple algorithms. This paper is a continuation of our previous work [10], focusing more on identifying common wrong strokes in table tennis by utilizing IR depth camera classification algorithms. The classification of the mistakes that took place while playing can be classified based on each player dependently or independently for all players.

Visual tracking is an art of tracking a moving object over video frames using non-stationary cameras, for which feature descriptors of the target are computed and applied to a motion model. In this paper, SURF is used to compute the feature descriptors and Mean-Shift algorithm as a motion model. Mean-shift algorithm is fundamentally a logical approach to track the object on an image frame where the appearance is described by histograms. Mean-Shift tracking can directly be applied to SURF features but there is a big constraint of availability of an adequate number of feature keypoints for a given object. To get over this limitation, the re-projection technique is implemented in this paper for tracking an object in any video recorded from a mobile or stationary camera. Re-projection, online updates the histogram of object template for every frame by superimposing the feature keypoints from upcoming frames to the first frame. The object can be tracked using SURF descriptors without using any secondary information (color, texture, optical flow, gradient, etc.) regarding the object, which makes it computationally inexpensive to be used in real-time systems. Also, the SURF feature is calculated only for the target object that is to be tracked, this further reduces computational complexity. The simulation results prove the effectiveness of the work.

In today’s world of internet, a massive amount of data is getting generated every day and content-based classification of images is becoming an essential aspect for efficient retrieval of images and have attracted application in several fields and one of such field is sports. Sport is an integral part of everybody’s daily life and it is very important to play the sport with the right posture and environment otherwise it can lead to medical issues. This paper presents a robust framework for classifying the sport images based on the environment and related surroundings. In this paper, our approach is based on the use of the Inception V3 for the extraction of features and Neural Networks for the classification of various sport categories. Six categories rugby, tennis, cricket, basketball, volleyball, and badminton have been used for analysis and classification. To validate the effectiveness of the framework and Neural Networks, comparisons have been done with other classifiers like Random Forest, K-Nearest Neighbors (KNN) and Support Vector Machine (SVM). Our framework has successfully achieved an average accuracy of 96.64 % over six categories which demonstrate the effectiveness of the framework and can be used for the detection and classification of various sport activities in an efficient manner.

Analyzing sensor data from a production environment is quite challenging because of the high-dimensional nature of the data. In addition, the generated data is in the form of time-series, where the sequence of registrations may be of utmost significance. One of the main goals of the paper is to determine if the given time-series of feature combinations is normal or rare. This goal could successfully be achieved by combining multiple machine learning models. In this paper, a sliding window based ensemble method is proposed to detect outliers in a streaming fashion. The proposed method uses a combination of clustering algorithms to construct subgroups (clusters) representing different data structures. These structures are later used in a one-class classification algorithm to identfy the outliers. Thus, if a pattern does not belong to any of the common structures or clusters, it is an outlier. Further, based on the rare pattern classification, machine failures could be predicted in advance.

Image registration is the process of overlaying two or more multi sensor or multi temporal or multi resolution images of the same scene. Image registration is very crucial preprocessing step in any remote sensing image processing application. This paper presents a brief review of evolution of different image registration methodologies with challenges involved in it. Image registration basically aligns the two images (reference and sensed image) geometrically. In the first part of this paper, classical methods will be discussed which are mainly based on area based (Intensity based) approach and feature based approach. Ample amount of work has already been done in both methods in manual and automatic manner. In the second part all recent approaches will be discussed and compared with results, on the basis of registration accuracy, number of correct correspondence. In recent studies Convolutional Neural Network with deep learning algorithms are used in combination with traditional methods to boost the overall performance.

Nowadays, standard intake of healthy food is necessary for keeping a balanced diet to avoid obesity in the human body. In this paper, we present a novel system based on machine learning that automatically performs accurate classification of food images and estimates food attributes. This paper proposes a deep learning model consisting of a convolutional neural network that classifies food into specific categories in the training part of the prototype system. The main purpose of the proposed method is to improve the accuracy of the pre-training model. The paper designs a prototype system based on the client server model. The client sends an image detection request and processes it on the server side. The prototype system is designed with three main software components, including a pre-trained CNN model training module for classification purposes, a text data training module for attribute estimation models, and a server-side module. We experimented with a variety of food categories, each containing thousands of images, and through machine learning training to achieve higher classification accuracy.

This paper introduces a new research area called Explainable Robotics, which studies explainability in the context of human-robot interactions. The focus is on developing novel computational models, methods and algorithms for generating explanations that allow robots to operate at different levels of autonomy and communicate with humans in a trustworthy and human-friendly way. Individuals may need explanations during human-robot interactions for different reasons, which depend heavily on the context and human users involved. Therefore, the research challenge is identifying what needs to be explained at each level of autonomy and how these issues should be explained to different individuals. The paper presents the case for Explainable Robotics using a scenario involving the provision of medical health care to elderly patients with dementia with the help of technology. The paper highlights the main research challenges of Explainable Robotics. The first challenge is the need for new algorithms for generating explanations that use past experiences, analogies and real-time data to adapt to particular audiences and purposes. The second research challenge is developing novel computational models of situational and learned trust and new algorithms for the real-time sensing of trust. Finally, more research is needed to understand whether trust can be used as a control variable in Explainable Robotics.

This paper presents the use of a class of Deep Neural Networks for recognizing indoor home scenes so as to aid Intelligent Assistive Systems (IAS) in performing indoor services to assist elderly or infirm people. Identifying exact indoor location is important so that objects associated with particular tasks can be located speedily and efficiently irrespective of position or orientation. In this way, IAS developed for providing services may become more efficient in accomplishing designated tasks satisfactorily. There are many Convolutional Neural Networks (CNNs) which have been developed for outdoor scene classification and, also, for interior (not necessarily indoor home) scene classification. However, to date, there are no CNNs which are trained, validated and tested on indoor home scene datasets as there appears to be an absence of sufficiently large databases of home scenes. Nonetheless, it is important to train systems which are meant to operate within home environments with the correct relevant data. To counteract this problem, it is proposed that a different type of network is used, which is not very deep (i.e., a network which does not have too many layers) but which can attain sufficiently high classification accuracy using smaller training datasets. A type of neural network likely to help achieve this is a Capsule Neural Network (CapsNet). In this paper, 20,000 indoor home scenes were used for training the CapsNet, and 5000 images were used for testing it. The validation accuracy achieved is 71% and testing accuracy achieved is 70%.

Epigraphy refers to the study of Epigraphs, or inscriptions. Estampage or stamping, is a term that is commonly used in epigraphy in order to get an exact copy of an inscription. An estampage is typically derived by applying wet paper onto the rock face, over which any ink material is wiped. This process also results in the appearance of unwanted noise which arises due to the texture of the rock, human errors or a variety of different factors. In this paper, we propose CCD: Connected Component Denoising, a novel approach for denoising and character segmentation via connected component analysis and multi scale template matching. A dynamic threshold is obtained via histogram density projections to effectively remove noisy areas. A comparative study is then performed between the different approaches (MSTM, CC, Hybrid). Inspection of the results of the hybrid approach by the HVS(Human Visual System) shows that it outperforms all the previous techniques.

This paper mainly deals with Bio Inspired which means biological structures. Bio Inspired easily handle failure which determines the real-world complicated engineering issues. It continually finds the optimum resolution to unravel its drawback maintaining good balance among its parts. Widen the range and growth of Bio Inspired Algorithms analyze new fields of application. A disaster is a sudden, disastrous event that seriously disrupts the functioning of a community or society and causes human, material, and economic or environmental losses that exceed the community’s or society’s ability to cope using its own resources. This paper presents comparative study between ant colony optimization algorithm and fireflies algorithm to find shortest route during disaster. ACO is inspired by behavior of ants. The specialty of ACO algorithm is to find finest route with respect to each road’s length and damage degree. ACO provides reliable paths and also the decision is based on pheromone level released by ants. One of the recent developed swarm intelligence algorithm is firefly algorithm introduced by Yang. This algorithm inspired by behavior of insects. It works based on three rule: They are unisex, attractiveness and brightness. So these two algorithms helps in finding the shortest path between origin point and destination point.

Healthcare industries generate enormous amount of data, so called big data that accommodates hidden knowledge or pattern for decision making. The huge volume of data is used to make decision which is more accurate than intuition. Exploratory Data Analysis (EDA) detects mistakes, finds appropriate data, checks assumptions and determines the correlation among the explanatory variables. In the context, EDA is considered as analysing data that excludes inferences and statistical modelling. Analytics is an essential technique for any profession as it forecast the future and hidden pattern. Data analytics is considered as a cost effective technology in the recent past and it plays an essential role in healthcare which includes new research findings, emergency situations and outbreaks of disease. The use of analytics in healthcare improves care by facilitating preventive care and EDA is a vital step while analysing data. In this paper, the risk factors that causes heart disease is considered and predicted using K-means algorithm and the analysis is carried out using a publicly available data for heart disease. The dataset holds 209 records with 8 attributes such as age, chest pain type, blood pressure, blood glucose level, ECG in rest, heart rate and four types of chest pain. To predict the heart disease, K-means clustering algorithm is used along with data analytics and visualization tool. The paper discusses the pre-processing methods, classifier performances and evaluation metrics. In the result section, the visualized data shows that the prediction is accurate.

South Asian subcontinent comprises of 08 different countries varying hugely in their size, population and diversity. While India stands to be the biggest among all of them, other countries like Maldives and Nepal are relatively much small. However, all these countries face similar challenges in providing quality technical education to its large base of students. This paper accounts for the quantitative analysis of count of research paper publications, their citations (that examines the quality), average citations/paper and growth of research work, in much sought after engineering domain now a days viz. Artificial Intelligence. In this regard, the important research-oriented details have been gathered from SCImago. Interpretation of the collected data reveals that India contributed to 90% of South Asia’s publications on AI during 2013-2018 but, it trailed the top northern Asian country i.e., China. It was evident from estimated data that China published 66517 articles in the AI domain, whereas, India just have published 25277 articles. Moreover, on the basis of another benchmark i.e., citation per AI document, India trailed other Asian countries like China, South Korea, Taiwan, Singapore, Hong Kong, Malaysia and Pakistan. Artificial Intelligence research in the Southern Asian region still requires large scale national funding investment and international collaboration to solve economic and societal challenges.

A bacterial infection named Tuberculosis is a persistent disease that has caused a large number of deaths worldwide according to WHO’s data. This disease is caused by a particular bacteria called Mycobacterium which usually affects the lungs. If early treatments are not provided to the patients it may result in fatality. So, early diagnosis and treatment are crucial. In this paper, we present a new and better way of automated detection of tuberculosis using a Deep Learning method known as the Antialiased Convolution Neural Networks proposed by Richard Zhang in his Research Paper titled “Making Convolution Neural Networks Shift-Invariant Again”. The objective of this task is the detection of tuberculosis lesions in the lungs using Image Segmentation and then using the trained models and the metadata the patient cases were classified to tuberculosis of low or high severity. The data in consideration here is the ImageCLEF med Tuberculosis 2019 data set. The data set comprises of 3D CT images. So, here the 3D images were split into 2D slices and segmentation was applied to each slicing using UNet, FPN, and LinkNet architecture. For better accuracy and predictions Antialiased Convolution Neural Networks based on shift-invariant pooling is applied instead of the conventional Convolution Neural Networks. Here the Antialiased Convolution Neural Networks provided better results. This is a generalized attempt on our end which can be applied for other similar and related datasets as well.

In natural language, the same meaning can be expressed by different texts. The process of determining the inference relationship occurring between a text T and a hypothesis H is called Natural Language Inference (NLI). The NLI task aims to provide a generic framework that captures, in a unifying manner, the inference across Natural Language Processing applications such as question answering, summarization, information retrieval, and machine translation. Many tasks and datasets have been created to support the development and evaluation of the ability of the NLI task in different languages. For the Arabic language, interest in this field is gradually increasing. This paper aims to provide an overview of state-of-the-art NLI approaches for Arabic, the relevant knowledge resources and the used tools in order to support a better understanding of this growing field. Moreover, this paper points to classify the proposed approaches for Arabic NLI and compare existing NLI systems.

This research article proposes an efficacious and remarkable strategy for image processing. A Google satellite image ascertaining by manipulating image normalized cross-correlation, grayscale image, and image thresholding todetermine the percentage of a green portion (trees) within the total area has been developed in this paper. This exertion exploited 2D image normalized cross-correlation to scrutinize a Google satellite image to trace out the green spots in it. In this proposed work an image of the University of Asia Pacific, Green Road, Dhaka, Bangladesh from Google satellite has been exerted. In this maneuver, a template image has been coincided with its prototypical image. Subsequently, correlate the template image with the main image employing image normalized cross-correlation to recognize if the template image exists in the main image or not. As a result of correlation, it has been discerned that the template image exists amid the main image. Latterly convert the main image into a grayscale image to determine the total area. The consequential step is to exert image thresholding. Then the area under two extents is enumerated along with the percentage of a green portion (trees) within the total area has been detected. Due to defective pixel quality of satellite image, it’s arduous to study satellite image hence to overcome this hindrance as well as to trace out any definite spot and its area this endeavor is proposed in this paper. This algorithm is executed in MATLAB R2016.

Nowadays, it is still important to develop methods for processing data, in particular medical texts, in Russian. In this paper, we checked how each stage of text pre-processing affects the result of the classifier. The paper analyzed 269923 records of allergic anamnesis of patients, 11670 of which were placed for further processing. We consider the main stages of pre-processing: tokenization, deletion of stop words, error correction, document cropping, normalization, class harmonization, and vectorization. To vectorize the data, we have selected the Bag-of-Words. The method of logistic regression was chosen for classification, since it has easy reproducibility and interpretation. Precision, recall and F-measure were selected as evaluation metrics. The results (F = 88.12%) showed that the most effective was the stage of normalization and error correction.

In the paper we investigate an application of the General Shape Analysis approach to the coarse classification of teeth shapes. The main purpose of the General Shape Analysis is to find one or a few general templates with the highest similarity for an investigated object. This allows to obtain the most basic information about an object and is useful in the case of large amount of data, missing data or when classes are not evident. In this paper various shape description algorithms are applied to the problem of General Shape Analysis, namely the Two-Dimensional Fourier Descriptor, Generic Fourier Descriptor, UNL-Fourier Descriptor, Point Distance Histogram and Zernike Moments. The original description vectors are also investigated after reducing with the use of various approaches, among which are the Principal Component Analysis and Discrete Cosine Transform. We examine some methods using an original database for the General Shape Analysis. Then the selected methods are applied for the recognition of teeth shapes.

Today is the era of paperless office and governance. It comes with numerous advantages like increased productivity and efficiency, pervasiveness, storage optimization, robustness and eco-friendliness. Hence there is a need of converting paper documents into machine editable form. This leads to development of OCR (Optical Character Recognition). OCR is a technique to convert, mechanically or electronically an image, photo or scanned document of a handwritten text (HCR-Handwritten Character Recognition) or printed text (PCR- Printed Character Recognition) into digital text. HCR is a form of OCR that is specifically designed to recognize the handwritten text whereas PCR focuses on recognition of printed text. HCR is more challenging as compared to PCR due to diversity in human writing styles, size, curve, strokes and thickness of characters. Based on data acquisition mode, the OCR can either be online or offline. Offline recognition is performed in two ways: handwritten and printed [1]. In offline mode, the characters are on paper and captured using scanner or high-resolution camera whereas in online mode the pixel values of characters are captured by movement of cursor, pen or stylus. The HCR systems are readily available for foreign languages and many of the Indian languages like Bangla, Devanagari and Gurumukhi but for Gujarati language the HCR development is still in its infancy stage. This study focuses on development of an artificial intelligence based offline HCR system for Gujarati language. Important contribution of this study is data collection, of size 10,000 images from 250 number of people, of different age groups, of different professions. This paper describes a supervised classifier approach based on CNN (Convolutional Neural Networks) and MLP (Multi-Layer Perceptron) for recognition of handwritten Gujarati characters. A success rate of 97.21% is obtained using CNN and 64.48% using MLP. Lot of work has been done at character level, but very few has been done at word level recognition. Major focus of this study was on creating a continuous workflow for image to text conversion at word level.

The new theory of education suggests that learner should be in the center of the learning process and the instructor playing an advising and facilitating role. Building smart learning environments supported by e-learning platforms is an important area of research. The rapid and continuous development of technology that has brought new learning skills for students forces the educational system to enter into a new era. This change is further justified by some unprecedented events that force students to learn remotely. QLearn is an e-learning platform developed as a web based application which provides quizzes for students enrolled at Advanced Programming Methods course from Babes Bolyai University (Romania) as part of their formative and summative assessment. The existing set of quizzes has been proposed by students throughout the last three iterations of the course in a collaborative manner with course instructor, and this data set is expanding continuously with every new generation of students. QLearn is a smart learning environment offering the students valuable feedback and a good preparation for the exam. Some metrics that quantify the coverage rate of the course syllabus attained by students or their understanding level of knowledge are provided by QLearn. The Artificial Intelligent component of QLearn application uses these measures to make predictions for students’ outcomes at the exam, to find out which topics need to be practised more and to recommend learning plans according to students’ individual needs. The contribution of the paper is therefore twofold. Firstly, we propose a new learning design, based on students’ involvement in a collaborative manner. The second contribution of the paper is QLearn, a software application that provides support (implementation) for the proposed learning process design. Not only does QLearn platform provide a smart learning environment for students, but it also ensures the knowledge transfer from instructors to students in an efficient and effective way.

This paper attempts to bring new ideas regarding the use of artificial intelligence in language learning apps. Artificial intelligence has seen a dramatic increase and its utilization in various aspects of data science, information science and various platforms is unprecedented. Its ubiquitousness is evident in smart cities, in online marketing, and data mining, however, its utilization in language learning apps is still somehow neglected. There are a few questions, such as why it is so, etc., this paper attempts to address. The research focuses on several most used language learning apps and the presence of artificial intelligence in them. The findings of the paper are as follows: basically none of the analysed apps uses any kind of machine learning, artificial intelligence or deep learning, and they are mostly based on predefined algorithms that do not utilize the full potential of the computational power we have currently available. The paper also suggests possible solutions and brings practical advice on how to implement artificial intelligence in these apps. The paper is important for any education innovation at the beginning of the 21 century. Without his innovative approach, education will lack sustainability and competitiveness, which will present a serious threat to the whole educational system.

The ubiquitous nature of social media platforms resulted into generation of large amount of multimedia data in social networks. The openness and unrestricted way to share the information on social media platforms fosters information spread across the network regardless of its credibility. Such kind of spreading the misinformation happens usually in the context of breaking news. Due to unverified information, such misinformation, also known as rumors may cause severe damages. Despite overwhelming use, uncontrolled nature of social media platforms usually results in generation and unfold of rumors. Therefore, automatically detecting the rumors from social media platforms is one of the highly sought-after research area in the domain of social media analytics. Motivated by the same, this paper focuses on detailed discussion of datasets and state-of-the-art approaches of rumor detection. Moreover, this paper sheds light upon supervised and unsupervised methods and deep learning approaches for rumor detection.

The paper presents the application of artificial intelligence techniques-artificial neural network (ANN) and fuzzy logic to the problems of stock market prediction, sales forecasting and market segmentation. Back propagation algorithm has been used to train the neural network for the first two problems, while Self-Organizing Maps (SOM) have been employed for the clustering problem that Market Segmentation presents. The Fuzzy model discussed in the paper uses the state values or the input-output data pairs. Mean Square Error has been used as the performance index to compare ANN and Fuzzy logic for stock prediction and sales forecasting.

House Price Index (HPI) is commonly used to estimate the changes in housing price. Since housing price is strongly correlated to other factors such as location, area, population, it requires other information apart from HPI to predict individual housing price. There has been a considerably large number of papers adopting traditional machine learning approaches to predict housing prices accurately, but they rarely concern about the performance of individual models and neglect the less popular yet complex models. As a result, to explore various impacts of features on prediction methods, this paper will apply both traditional and advanced machine learning approaches to investigate the difference among several advanced models. This paper will also comprehensively validate multiple techniques in model implementation on regression and provide an optimistic result for housing price prediction.

Landslides are phenomena widely present around the world and responsible each year of numerous life loss and extensive property damage. Researchers have developed various methodologies to identify area of high susceptibility of landslides. However, these methodologies cannot predict ‘when’ landslides are going to take place. Indeed, Wireless Sensors Network (WSN), Internet of Things (IoT) and Artificial Intelligence (AI) offer the possibility to monitor in real-time parameters causing the triggering factors of rapid landslides. In this paper, we suggest a real-time monitoring of landslides in order to precociously alert population in dangerous situation by means of a warning system. The novelty of this paper is the coupling of wireless sensors network and a multi-agent system deployed on an edge AI-IoT architecture by means of Kubernetes and Docker.

This paper explores the behavior of different normalization techniques viz. cepstral mean normalization, cepstral variance normalization, cepstral mean subtraction. cepstral mean and variance normalization, wiener filter, and spectral subtraction in noisy conditions. The performance parameters viz. EER (Equal Error Rate) and DCF (Detection Cost Function) has been calculated using NIST 2003 SRE and Aurora 2 with the help of various normalization techniques considered in this paper for different noisy backgrounds at 0, 5 and 10 dB signal-to-noise ratio. The experimental results obtained from these techniques reveal that cepstral mean normalization (CVN) normalization method is found to be better when compared to other normalization techniques used in this paper.

The paper concerns the use of fuzzy cognitive maps and k-means clustering to solve the problem of modeling multidimensional medical data. A fuzzy cognitive map is a recurrent neural network that describes the analyzed phenomenon in the form of key concepts and causal relationships between them. It is an effective tool for modeling decision support systems and is widely used in medicine. The aim of this paper is to analyze the use of fuzzy cognitive maps with k-means clustering to model decision support systems based on multidimensional data related to Parkinson’s disease. K-means method was applied to group the data, and then a separate fuzzy cognitive map was built for each cluster to increase forecasting accuracy. The learning process was realized with the use of the previously developed Individually Directional Evolutionary Algorithm. The obtained results confirm that the analyzed approach provides much better forecasting accuracy than the standard approach based on one model.

IOT and machine learning based household waste management system for Green smart society are aimed to make management of waste from the every apartment of the society more efficient using the most upcoming technology IOT. This paper discusses the collection and decomposition of waste in the smart way so that benefit from the waste is maximized and the actual waste is minimized efficiently. This paper focus on the segregation of the waste at two levels: the first level of segregation is on the individual house of the society and the second level of segregation is at the society. Author, discuss the recycling of the biodegradable waste for making compost. The machine learning technique such as KNN is used to generate an alert message for various combinations of three sensor values like level of bio and non biodegradable waste,concentration of poisonous gas. The overall impact of this research is in the upliftment of the green technologies by reducing pollutants, conserving, resourcing and reusing the energy through the use of technology.

For the last few years, one can see the emergence of a large number of intelligent products and services, their commercial availability and the socioeconomic impact, this raises the question if the present emergence of AI is just hype or does it really have the capability of transforming the world. The paper investigates the wide range of implications of artificial intelligence (AI), and delves deeper into both positive and negative impacts on governments, communities, companies, and individuals. This paper investigates the overall impact of AI - from research and innovation to deployment. The paper addresses the influential academic achievements and innovations in the field of AI; their impact on the entrepreneurial activities and thus on the global market. The paper also contributes in investigating factors responsible for the advancement of AI. For the exploration of entrepreneurial activities towards AI, two lists of top 100 AI start-ups are considered. The inferences obtained from the research will provide an improved understanding of the innovations and the impact of AI on businesses and society in general. It will also provide a better understanding of how AI can transform the business operations and thus the global economy.

Traffic analysis has many purposes such as evaluating the performance and security of network operations and management. Therefore, network traffic analysis is considered vital for improving networks operation and security. This paper discusses different machine learning approaches for traffic analysis. Increased network traffic and the development of artificial intelligence require new ways to detect intrusions, analyze malware behavior, and categorize Internet traffic and other security aspects. Machine learning (ML) shows effective capabilities in solving network problems. A review of the techniques used in the traffic analysis is presented in this paper.

The skin is an important organ of the human body, and the major skin diseases represented by melanoma are difficult to find and difficult to treat, which directly threaten the patient’s life safety. Therefore, rapid and accurate dermatoscopy diagnosis[1] is particularly important. The lack of experienced dermatologists limits the large-scale early diagnosis or screening of melanoma. Based on the above problems, this paper uses the method of multi-scale convolutional neural network to segment the Dermoscopic image by means of image segmentation in artificial intelligence to help medical personnel to achieve reliable diagnostic reference. In the overall segmentation network, this paper first enhances the contrast of the original image through preprocessing, and uses data enhancement and segmentation to increase the dataset. In the model training phase, this paper adopts the basic segmentation framework of U-Net[2] network. Different from U-Net networks, this paper introduces multi-scale features and multi-scale fusion mechanisms to deeply mine and fuse dermoscopic image features. Secondly, the model uses Focal Loss as the fundus segmentation loss function, and finds the optimal parameters of the loss function through a random grid search algorithm. In this paper, for the problem of positive and negative sample imbalance in dermoscopic images, this paper proposes an adaptive Focal Loss loss function and Channel-Wise Attention mechanism to adaptively weight the feature maps.

Information retrieval is a scorching today when the flare-up of data on the Internet increasingly day by day. Due to the infinite growth and availability of the Internet; numbers of educational sites are amplified. Huge Expansion in knowledge puts a special effect on the education system. This helps in upgrading higher education. Interest in Web usage mining has grown swiftly in its short history, both in exploring interest and research. Web usage mining is a type of data mining that extracts the server log information after users browse web pages. For extracting and retrieval information machine learning approaches are designed to recognize useful information from text documents automatically. The main aim of this paper is to cram weblogs using the clustering technique with WEBASTRO tool for predicting next user movement and user behaviour analysis. This paper presents an ideal approach for web mining and predicting their behaviour for the next visit and automatic web site modification.

This paper examines the predictive value of peer’s observations of an individual, applied to computational models of certain states of that individual. In a study of 28 days, 13 participants provided self-assessments about their level of stress, fatigue, and anxiety, while their smartphone passively recorded the sensor’s data. Simultaneously, their designated peers provided assessments about the level of stress, fatigue, and anxiety they perceived from the participant using the PeerMA method. We extracted sensor-derived features (sDFs) from the participant’s smartphone, and peer-derived features (pDFs) from the peer’s assessments. We evaluated the pDFs on a binary classification task using three machine learning algorithms (Decision Tree-DT, Random Forest-RF, and Extreme Gradient Boosting-XGB). As a result, the classification accuracy consistently increased when the algorithms were trained with the sDFs plus the pDFs, compared the tradition of using only the sDFs. More importantly, the classification accuracy was the highest when we trained the algorithms only with the pDFs (73.3% DT, 73.7% RF, and 71.1% XGB), which represents a unique contribution of this paper. The findings are encouraging about the incorporation of peer’s observations in machine learning with potential benefits in the fields of personal sensing and pervasive computing, especially for mental health and well-being.

Table tennis is a complex sport with a distinctive style of play. Due to the rising interest in this sport the past years, attempts have been targeted towards enhancing the training experience and quality through various techniques. Technology has been used to support training sessions for table tennis players before, with a focus on players’ performance measures rather than technique. In this paper, we propose a methodology based on IR depth camera for detecting and classifying the efficiency of strokes performed by players in order to enhance the training experience. Our system is to based on analyzing depth data collected from IR depth camera and recognized using fastDTW algorithm. The results show an average accuracy of 88% - 100%. This is the first paper to address the usage of IR depth camera on the table tennis player to detect and classify the strokes played.

Music is one of the effective media to convey emotion. Emotion recognition in music is the process of identifying the emotion from the music clips. In this paper, a novel approach is proposed to recognize the emotion by classes of musical instruments using deep learning techniques. Music dataset is collected for four instrument types, string, percussion, woodwind, and brass. These instruments types are grouped into four emotions, happy, sad, neutral and fear. The Mel frequency cepstral coefficients (MFCC), Chroma energy normalized statistics (CENS), chroma short time Fourier transform (STFT), spectral features spectral centroid, bandwidth, rolloff, and temporal feature ZCR are extracted from the instrumental music data set. Based on the extracted features recurrent neural networks (RNN) are trained to recognize the emotion. The performance of RNN is compared with the baseline machine learning classification algorithms. The results indicate that MFCC features with deep RNN give better performance for instrument emotion recognition. It also shows that instrument class plays an important role in the emotion induced by the music. Music is one of the effective media to convey emotion. Emotion recognition in music is the process of identifying the emotion from the music clips. In this paper, a novel approach is proposed to recognize the emotion by classes of musical instruments using deep learning techniques. Music dataset is collected for four instrument types, string, percussion, woodwind, and brass. These instruments types are grouped into four emotions, happy, sad, neutral and fear. The Mel frequency cepstral coefficients (MFCC), Chroma energy normalized statistics (CENS), Chroma short time Fourier transform (STFT), spectral features spectral centroid, bandwidth, rolloff, and temporal feature ZCR are extracted from the instrumental music data set. Based on the extracted features recurrent neural networks (RNN) are trained to recognize the emotion. The performance of RNN is compared with the baseline machine learning classification algorithms. The results indicate that MFCC features with deep RNN give better performance for instrument emotion recognition. It also shows that instrument class plays an important role in the emotion induced by the music.

This paper majorly concerns the classification of handwritten numerals of Devanagari Script. The major contributions in this paper are 1) Development of a dataset for handwritten numerals similar to MNIST dataset, 2) Analysis of Pattern Recognitions tools based on NN and Convolution Neural Network, 3) Detailed discussion on the results by calculating the Precision, recall and F-measure values and compared with the other dataset available online. The present dataset includes 4,282 handwritten numerals of Devanagari which are collected from people of different ages. In the methodology developed in this paper, all the numerals are extracted from the image. After pre-processing, the images are resized to 30X30 which are later converted to vectors. These vectors with labels are the inputs for the classifiers. ANN classifier is designed by using PRTool and Deep Learning network is designed to make comparison with ANN. Data for training and testing splits into different ratios – 80:20, 70:30, 60:40 and 50:50. This research has achieved accuracy of more than 95%. The results of the dataset generated are compared with the dataset available online.

This paper presents an Intelligent Information System for education. The system was created for teaching students to use genetic algorithm in application to optimization tasks. The system allows to quickly encode a solution of the problem and pick up most suitable configuration of genetic algorithm. The paper also demonstrates a specific example of usage of educational system to solve an optimization task. The paper contains a description of the educational system and its features, a description of its capabilities of working with genetic algorithm and its graphical interface.

Automatic emotion recognition based on facial expression is an interesting research field, which has presented and applied in several areas such as safety, health and in human machine interfaces. Researchers in this field are interested in developing techniques to interpret, code facial expressions and extract these features in order to have a better prediction by computer. With the remarkable success of deep learning, the different types of architectures of this technique are exploited to achieve a better performance. The purpose of this paper is to make a study on recent works on automatic facial emotion recognition FER via deep learning. We underline on these contributions treated, the architecture and the databases used and we present the progress made by comparing the proposed methods and the results obtained. The interest of this paper is to serve and guide researchers by review recent works and providing insights to make improvements to this field.

Purpose: In this paper, an automatic detection of premalignant lesions based on human voice production theory is designed. The current framework is interested in particularly to the case of vocal fold precancerous lesion detection. Since the earlier detection of cancer is an important fact and directly related to medical treatment, the current paper presents a non-invasive and low time consuming technique for earlier cancer detection. Method: By a simple microphone, a speech signal can be picked up and analysed. We aim to extract the voice source signal from the acoustic speech signal. The voice source generated by vocal fold is altered when a premalignant lesion occurs. Features extracted from source voices are deeply analysed. However, due to the lack of speech samples, extracted features are augmented based on features analysis. Results: The adopted method based on boxplot, histogram and probability density leads to data augmentation of the extracted features. Augmented features are used in learning and testing, processing using SVM. The performances are assessed using four criteria, sensitivity, specificity, precision and accuracy. When augmented features are combined according to PCA analysis, an accuracy of premalignant lesion identification about 95% is accomplished. Conclusion: It is shown in this study that is possible to detect the premalignant lesions with acceptable and fairly sensitivity, specificity, precision and accuracy. The performances are improved when data augmentation process is used.

In this paper, we study the identification problem of human limbs synergistic interaction based on the application of natural human-computer interaction. We proposed an identification model of human limbs cooperative interaction based on the Random Increased Hybrid Learning Machine for the unique requirements of natural human-computer interaction applications. The model combines the motion region image, the Hu invariant moment model and the RIHLM. In this paper, the algorithm theory, the model rationality and the implementation of the human motion recognition model based on the RIHLM is described. Finally, by comparing the experimental results, we verified that the model presented in this paper has better robustness, timeliness and accuracy in the application of natural human-computer interaction.

The Multi-Robot Task Allocation (MRTA) problem has recently become a key research topic. Task allocation is the problem of mapping tasks to robots, such that the most appropriate robot is selected to perform the most fitting task, leading to all tasks being optimally accomplished. Expanding the number of tasks and robots may cause the collaboration among the robots to become tougher. Since this process requires high computational time, this paper describes a technique that reduces the size of the explored state space, by partitioning the tasks into clusters. In real-world problems, the absence of information regarding the number of clusters is ordinarily occurring. Hence, a dynamic clustering is auspicious for partitioning the tasks to an appropriate number of clusters. In this paper, we address the problem of MRTA by putting forward a new simple, automatic and efficient clustering algorithm of the robots’ tasks based on a dynamic distributed particle swarm optimization, namely, ACD2PSO. Our approach is made out of two stages: stage I groups the tasks into clusters using the dynamic distributed particle swarm optimization (D2PSO) algorithm and stage II allocates the robots to the clusters. The assignment of robots to the clusters is represented as multiple traveling salesman problems (MTSP). Computational experiments were carried out to prove the effectiveness of our approach in term of clustering time, cost, and the MRTA time, compared to the distributed particle swarm optimization (dPSO) and genetic algorithm (GA). Thanks to the D2PSO algorithm, stagnation and local optima issues are avoided by adding assorted variety to the population, without losing the fast convergence of PSO.