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Sentiment Analysis: Amazon Electronics Reviews Using BERT and TextblobThe market needs a deeper and more comprehensive grasp of its insight, where the analytics world and methodologies such as “Sentiment Analysis” come in. These methods can assist people especially “business owners” in gaining live insights into their businesses and determining wheatear customers are satisfied or not. This paper plans to provide indicators by gathering real world Amazon reviews from Egyptian customers. By applying both Bidirectional Encoder Representations from Transformers “Bert” and “Text Blob” sentiment analysis methods. The processes shall determine the overall satisfaction of Egyptian customers in the electronics department - in order to focus on a specific domain. The two methods will be compared for both the Arabic and English languages. The results show that people in Amazon.eg are mostly satisfied with the percentage of 47%. For the performance, BERT outperformed Textblob indicating that word embedding model BERT is more superior than rule-based model Textblob with a difference of 15% - 25%.
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An Efficient Encryption and Compression of Sensed IoT Medical Images Using Auto-encoderHealthcare systems nowadays depend on IoT sensors for sending data over the internet as a common practice. Encryption of medical images is very important to secure patient information. Encrypting these images consumes a lot of time on edge computing; therefore, the use of an auto-encoder for compression before encoding will solve such a problem. In this paper, we use an auto-encoder to compress a medical image before encryption, and an encryption output (vector) is sent out over the network. On the other hand, a decoder was used to reproduce the original image back after the vector was received and decrypted. Two convolutional neural networks were conducted to evaluate our proposed approach: The first one is the auto-encoder, which is utilized to compress and encrypt the images, and the other assesses the classification accuracy of the image after decryption and decoding. Different hyperparameters of the encoder were tested, followed by the classification of the image to verify that no critical information was lost, to test the encryption and encoding resolution. In this approach, sixteen hyperparameter permutations are utilized, but this research discusses three main cases in details. The first case shows that the combination of Mean Square Logarithmic Error (MSLE), ADAgrad, two layers for the auto-encoder, and ReLU had the best auto-encoder results with a Mean Absolute Error (MAE) = 0.221 after 50 epochs and 75% classification with the best result for classification algorithm. The second case shows the reflection of auto-encoder results on the classification results which is a combination of Mean
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Artificial Intelligence-Based Breast Cancer DiagnosisUsing Ultrasound Images and Grid-Based DeepFeature GeneratorPurpose:Breast cancer is a prominent cancer type with high mortality. Early detection of breast cancer could serve to improve clinical outcomes. Ultrasonography is a digital imaging technique used to differentiate benign and malignant tumors. Several artificial intelligence techniques have been suggested in the literature for breast cancer detection using breast ultrasonography (BUS). Nowadays, particularly deep learning methods have been applied to biomedical images to achieve high classification performances.Patients and Methods:This work presents a new deep feature generation technique for breast cancer detection using BUS images.The widely known 16 pre-trained CNN models have been used in this framework as feature generators. In the feature generationphase, the used input image is divided into rows and columns, and these deep feature generators (pre-trained models) have applied to each row and column. Therefore, this method is called a grid-based deep feature generator. The proposed grid-based deep feature generator can calculate the error value of each deep feature generator, and then it selects the best three feature vectors as a final feature vector. In the feature selection phase, iterative neighborhood component analysis (INCA) chooses 980 features as an optimal number of features. Finally, these features are classified by using a deep neural network (DNN). Results:The developed grid-based deep feature generation-based image classification model reached 97.18% classification accuracy on the ultrasonic images for three classes, namely malignant, benign, and normal.Conclusion:The findings obviously denoted that the proposed grid deep feature generator and INCA-based feature selection model successfully classified breast ultrasonic images.
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Improving the Performance of Semantic Text Similarity Tasks on Short Text PairsTraining semantic similarity model to detect duplicate text pairs is a challenging task as almost all of datasets are imbalanced, by data nature positive samples are fewer than negative samples, this issue can easily lead to model bias. Using traditional pairwise loss functions like pairwise binary cross entropy or Contrastive loss on imbalanced data may lead to model bias, however triplet loss showed improved performance compared to other loss functions. In triplet loss-based models data is fed to the model as follow: anchor sentence, positive sentence and negative sentence. The original data is permutated to follow the input structure. The default structure of training samples data is 363,861 training samples (90% of the data) distributed as 134,336 positive samples and 229,524 negative samples. The triplet structured data helped to generate much larger amount of balanced training samples 456,219. The test results showed higher accuracy and f1 scores in testing. We fine-tunned RoBERTa pre trained model using Triplet loss approach, testing showed better results. The best model scored 89.51 F1 score, and 91.45 Accuracy compared to 86.74 F1 score and 87.45 Accuracy in the second-best Contrastive loss-based BERT model.
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A Core Ontology to Support Agricultural Data InteroperabilityThe amount and variety of raw data generated in the agriculture sector from numeroussources, including soil sensors and local weather stations, are proliferating. However, these raw data in themselves are meaningless and isolated and, therefore, may offer little value to the farmer. Data usefulness is determined by its context and meaning and by how it is interoperable with data from other sources. Semantic web technology can provide context and meaning to data and its aggregation by providing standard data interchange formats and description languages. In this paper, we introduce the design and overall description of a core ontology that facilitates the process of data interoperability in the agricultural domain.
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Walk Through Event Stream Processing Architecture, Use Cases and Frameworks SurveyNowadays events stream processing is one of the top demanding field(s) because of the business urgent need for ongoing real time analytics & decisions. Most business domains avail huge amount of data aiming to make use of each data point efficiently. Corporate(s) have a cloud of events vary from internal business transactions, social media feeds, IoT devices logs, ... etc. In this paper we would discuss state of the art event stream processing technologies using cloud of events by summarizing event stream processing definition, data flow architectures, common use cases, frameworks and architecture best practice. A final comparison is given for best practice and technology use based on data type.
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Dynamic Modeling and Identification of the COVID-19 Stochastic DispersionIn this work, the stochastic dispersion of novel coronavirus disease 2019 (COVID-19) at the borders between France and Italy has been considered using a multi-input multi-output stochastic model. The physical effects of wind, temperature and altitude have been investigated as these factors and physical relationships are stochastic in nature. Stochastic terms have also been included to take into account the turbulence effect, and the random nature of the above physical parameters considered. Then, a method is proposed to identify the developed model's order and parameters. The actual data has been used in the identification and prediction process as a reference. These data have been divided into two parts: the first part is used to calculate the stochastic parameters of the model which are used to predict the COVID-19 level, while the second part is used as a check data. The predicted results are in good agreement with the check data.
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Using Knowledge Graph Embeddings in Embedding Based Recommender SystemsThis paper proposes using entity2rec [1] which utilizes knowledge graph-based embeddings (node2vec) instead of traditional embedding layers in embedding based recommender systems. This opens the door to increasing the accuracy of some of the most implemented recommender systems running in production in many companies by just replacing the traditional embedding layer with node2vec graph embedding without the risk of completely migrating to newer SOTA systems and risking unexpected performance issues. Also, Graph embeddings will be able to incorporate user and item features which can help in solving the well-known Cold start problem in recommender systems. Both embedding methods are compared on the movie-Lens 100-K dataset in an item-item collaborative filtering recommender and we show that the suggested replacement improves the representation learning of the embedding layer by adding a semantic layer that can increase the overall performance of the normal embedding based recommenders. First, normal Recommender systems are introduced, and a brief explanation of both traditional and graph-based embeddings is presented. Then, the proposed approach is presented along with related work. Finally, results are presented along with future work.
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Recommender Diagnosis System with Fuzzy Logic in Cloud EnvironmentRecommendation systems are now used in a wide range in many fields. In the medical field, recommendation systems have a great stature to both doctors and patients for its accurate prediction. It can reduce the time and efforts spent by doctors and patients. The present work introduces a simple and effective methodology for medical recommendation system based on fuzzy logic. Fuzzy logic is an important method to be used based on fuzzy input data. The input data for each patient are not the same, on which recommendation can differ. This work aims to develop techniques for handling the patient data to urge accurate lifestyle recommendations to the patient. Fuzzy logic is utilized to form different recommendations for the patient like lifestyle recommendations, medicine recommendations, and sports recommendations based on different patient factors like age, gender and patient diseases. After evaluating the system its efficiency reached 94%. This Experiment is the final module in a four modules recommendation system. The first one is responsible for diagnosing chest diseases using ECG signals. The second one makes diagnosis using X-ray images. The third is utilizing the security of the whole system through encryption when sending user data over the cloud.
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Detection of epileptic seizures on EEG signals using ANFIS classifier, autoencoders and fuzzy entropiespileptic seizures are one of the most crucial neurological disorders, and their early diagnosis will help the clinicians to provide accurate treatment for the patients. The electroencephalogram (EEG) signals are widely used for epileptic seizures detection, which provides specialists with substantial information about the functioning of the brain. In this paper, a novel diagnostic procedure using fuzzy theory and deep learning techniques is introduced. The proposed method is evaluated on the Bonn University dataset with six classification combinations and also on the Freiburg dataset. The tunable-Q wavelet transform (TQWT) is employed to decompose the EEG signals into different sub-bands. In the feature extraction step, 13 different fuzzy entropies are calculated from different sub-bands of TQWT, and their computational complexities are calculated to help researchers choose the best set for various tasks. In the following, an autoencoder (AE) with six layers is employed for dimensionality reduction. Finally, the standard adaptive neuro-fuzzy inference system (ANFIS), and also its variants with grasshopper optimization algorithm (ANFIS-GOA), particle swarm optimization (ANFIS-PSO), and breeding swarm optimization (ANFIS-BS) methods are used for classification. Using our proposed method, ANFIS-BS method has obtained an accuracy of 99.74% in classifying into two classes and an accuracy of 99.46% in ternary classification on the Bonn dataset and 99.28% on the Freiburg dataset, reaching state-of-the-art performances on both of them.
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Exploring factors influencing bicycle-sharing adoption in India: a UTAUT 2 based mixed-method approachPurpose Bicycle sharing offers a novel way to create smart and sustainable mobility solutions for the future. The purpose of this study is to draw on the Unified Theory of Acceptance and Use of Technology 2 (UTAUT 2) framework for identifying the factors necessary to predict bike-sharing intention among users in India. Design/methodology/approach Data were collected through a questionnaire distributed across four major cities in India, and 515 responses were analyzed. A sequential approach was employed to analyze the data using Partial Least Square–Structural Equation Modeling (PLS-SEM) and Fuzzy-set Qualitative Comparative Analysis (fsQCA). Findings The findings from PLS analysis revealed that performance expectancy, effort expectancy, facilitating conditions, hedonic motivation and price value are the salient variables that affect users' intentions to participate in bike sharing. In addition, based on fsQCA, six configurations of causal conditions are presented as intermediate solutions that produce the same results. Although antecedent conditions, such as habit and social influence, had an insignificant effect on individuals' BSI, they create conditions sufficient to encourage users' participation in bike sharing in combination with other variables. Research limitations/implications A few limitations of this research and the implications of the findings in terms of theory and policy implications are also discussed. Originality/value The reported study is one of the earliest to explain bike-sharing adoption in India using the UTAUT 2 model.
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The era of advanced machine learning and deep learning algorithms for malware detectionSoftware has been the essential element to computers in today's digital era. Unfortunately, it has experienced challenges from various types of malware, which are designed for sabotage, criminal money-making, and information theft. To protect the gadgets from malware, numerous malware detection algorithms have been proposed. In the olden days there were shallow learning algorithms, and in recent years there are deep learning algorithms. With the availability of big data for training of model and affordable and high-performance computing services, deep learning has demonstrated its superiority in many smart city applications, in terms of accuracy, error rate, etc. This chapter intends to conduct a systematic review on the latest development of deep learning algorithms for malware detection. Some future research directions are suggested for further exploration.
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Gene Ontology GAN (GOGAN): a novel architecture for protein function predictionOne of the most important aspects for a deep interpretation of molecular biology is the precise annotation of protein functions. An overwhelming majority of proteins, across species, do not have sufficient supplementary information available, which causes them to stay uncharacterized. Contrastingly, all known proteins have one key piece of information available: their amino acid sequence. Therefore, for a wider applicability of algorithms, across different species proteins, researchers are motivated to make computational techniques that characterize proteins using their amino acid sequence. However, in case of computational techniques like deep learning algorithms, huge amount of labeled information is required to produce good results. The labeling process of data is time and resource consuming making labeled data scarce. Utilizing the characteristic to address the formerly mentioned issues of uncharacterized proteins and traditional deep learning algorithms, we propose a model called GOGAN, that operates on the amino acid sequence of a protein to predict its functions. Our proposed GOGAN model does not require any handcrafted features, rather it extracts automatically, all the required information from the input sequence. GOGAN model extracts features from the massively large unlabeled protein datasets. The term “Unlabeled data” is used for piece of information that have not been assigned labels to identify their characteristics or properties. The features extracted by GOGAN model can be utilized in other applications like gene variation analysis, gene expression analysis and gene regulation network detection. The proposed model is benchmarked on the Homo sapiens protein dataset extracted from the UniProt database. Experimental results show clear improvements in different evaluation metrics when compared with other methods. Overall, GOGAN achieves an F1 score of 72.1% with Hamming loss of 9.5%, using only the amino acid sequences of protein.
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Brain stroke detection from computed tomography images using deep learning algorithmsStroke is one of the common causes of death worldwide. Stroke is the inability of a focus to be fed in the brain due to clogged or bleeding of the vessels feeding the brain. Because early stroke treatment and diagnosis are related to a favorable patient outcome, time is a critical aspect of successful stroke treatment. In this chapter, we examine the stroke classification from Brain Stroke CT Dataset, with deep learning architectures. In the experimental study, a total of 2501 brain stroke computed tomography (CT) images were used for testing and training. For this purpose, numerus widely known pretrained convolutional neural networks (CNNs) such as GoogleNet, AlexNet, VGG-16, VGG-19, and Residual CNN were used to classify brain stroke CT images as normal and as stroke. Several performance metrics such as accuracy (ACC), specificity (SPE), sensitivity (SEN), and F-score are used to evaluate the performances of the classifier. The best classification results are achieved by VGG-19 with ACC 97.06%, SEN 97.41%, SPE 96.49%, and F-score 96.95%.
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Classification of brain hemorrhage computed tomography images using OzNet hybrid algorithmClassification of brain hemorrhage computed tomography (CT) images provides a better diagnostic implementation for emergency patients. Attentively, each brain CT image must be examined by doctors. This situation is time-consuming, exhausting, and sometimes leads to making errors. Hence, we aim to find the best algorithm owing to a requirement for automatic classification of CT images to detect brain hemorrhage. In this study, we developed OzNet hybrid algorithm, which is a novel convolution neural networks (CNN) algorithm. Although OzNet achieves high classification performance, we combine it with Neighborhood Component Analysis (NCA) and many classifiers: Artificial neural networks (ANN), Adaboost, Bagging, Decision Tree, K-Nearest Neighbor (K-NN), Linear Discriminant Analysis (LDA), Naïve Bayes and Support Vector Machines (SVM). In addition, Oznet is utilized for feature extraction, where 4096 features are extracted from the fully connected layer. These features are reduced to have significant and informative features with minimum loss by NCA. Eventually, we use these classifiers to classify these significant features. Finally, experimental results display that OzNet-NCA-ANN excellent classifier model and achieves 100% accuracy with created Dataset 2 from Brain Hemorrhage CT images.
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Gene Ontology Capsule GAN: an improved architecture for protein function predictionProteins are the core of all functions pertaining to living things. They consist of an extended amino acid chain folding into a three-dimensional shape that dictates their behavior. Currently, convolutional neural networks (CNNs) have been pivotal in predicting protein functions based on protein sequences. While it is a technology crucial to the niche, the computation cost and translational invariance associated with CNN make it impossible to detect spatial hierarchies between complex and simpler objects. Therefore, this research utilizes capsule networks to capture spatial information as opposed to CNNs. Since capsule networks focus on hierarchical links, they have a lot of potential for solving structural biology challenges. In comparison to the standard CNNs, our results exhibit an improvement in accuracy. Gene Ontology Capsule GAN (GOCAPGAN) achieved an F1 score of 82.6%, a precision score of 90.4% and recall score of 76.1%.
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How rumors diffuse in the infodemic: Evidence from the healthy online social change in ChinaInfodemic is defined as ‘an overabundance of information-some accurate and some not-that makes it hard for people to find trustworthy sources and reliable guidance when they need it’ by the World Health Organization. As unverified information, rumors can widely spread in online society, further diffusing infodemic. Existed studies mainly focused on rumor detection and prediction from the statement itself and give the probability that it will evolve into a rumor in the future. However, the detection and prediction from rumors production perspective is lack. This research explores the production mechanism from the uncertainty perspective using the data from Weibo and public rumor data set. Specifically, we identify the public uncertainty through user-generated content on social media based on systemic functional linguistics theory. Then we empirically verify the promoting effect of uncertainty on rumor production and constructed a model for rumor prediction. The fitting effect of the empirical model with the public uncertainty is significantly better than that with only control variables, indicating that our framework identifies public uncertainty well and uncertainty has a significantly predictive effect on rumors. Our study contributes to the research of rumor prediction and uncertainty identification, providing implications for healthy online social change in the post-epidemic era.
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An efficient and secure identity-based signature system for underwater green transport systemThe smart ocean has aroused the interest of government, business, and academia because of the wealth of marine resources. It has been suggested to use underwater Internet of Things (IoT) frameworks to collect a variety of data from smart seas that can aid in the underwater green transport system, ecological sustainability, military intelligence gathering, and a variety of other operations. Because of the limited resources accessible to IoT devices regarding communication overhead, processing expenses, and battery capacity, security and privacy concerns in underwater green transport systems have lately been a critical source of worry. In this context, We presented a unique identity-based authentication mechanism for underwater green transport systems. Our suggested solution uses lightweight authentication mechanisms that prove secure communication between different elements of the green transport system.
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Artificial intelligence and big data analytics for smart healthcareArtificial Intelligence and Big Data Analytics for Smart Healthcare serves as a key reference for practitioners and experts involved in healthcare as they strive to enhance the value added of healthcare and develop more sustainable healthcare systems. It brings together insights from emerging sophisticated information and communication technologies such as big data analytics, artificial intelligence, machine learning, data science, medical intelligence, and, by dwelling on their current and prospective applications, highlights managerial and policymaking challenges they may generate.
