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This document is a call for a new era of artificial intelligence (AI) in the connected world, specifically focusing on the field of Web Intelligence (WI). WI has evolved over the past 20 years, with three significant stages: WI 1.0 (Wisdom Web), WI 2.0 (Wisdom Web of Thing), and WI 3.0 (Wisdom Web of Everything). The document highlights several papers published in the Web Intelligence journal that explore different aspects of WI, including its impact on a better connected world, cross-cutting areas with brain informatics, biases in recommender systems, the influence of ChatGPT on information retrieval, the role of big data in the retail industry, order pairing mechanisms in real-time ride-sharing, data replication in cloud systems, and security risks in IoT-fog architecture. The document calls for more collaboration and communication among researchers in the WI field and emphasizes the importance of global interactions for co-intelligence and co-creation. [Extracted from the article]

The prevalence of violence against women and children is concerning, and the initial step is to raise awareness of this issue. Certain forms of detection based techniques are not frequently regarded both socially and culturally permissible. Designing and implementing effective approaches in secondary and supplementary avoidance simultaneously depends on the characterization and assessment. Given the greater incidence of instances and mortalities resulting developing an early detection system is essential. Consequently, violence against women and children is a problem of human health of pandemic proportions. As a result, the focus of this survey is to analyze the existing methods used to identify violence in photos or films. Here, 50 research papers are reviewed and their techniques employed, dataset, evaluation metrics, and publication year are analyzed. The study reviews the potential future research areas by examining the difficulties in identifying violence against women and children in literary works for researchers to overcome in order to produce better results. [ABSTRACT FROM AUTHOR]

This paper is a brief personal journal of our relentless pursuit of Web Intelligence (WI). How it all started? What have achieved? Where are we heading? Our search for the ultimate meaning of the Web enables us to see and appreciate the power of the Web for building a better human society through collaboration, co-learning, and co-creation. The Web is a powerful idea, a scientific and technological innovation, and a social creation. Web Intelligence explores the connectivity, diversity, and plasticity of the Web, as well as the global brain supported by the Web. The goal of research on Web Intelligence is to build a better connected world of everything, by people, and for a new intelligent human society. [ABSTRACT FROM AUTHOR]

In a distributed environment, replication is the most investigated phenomenon. Replication is a way of storing numerous copies of the same data at different locations. Whenever data is needed, it will be fetched from the nearest accessible copy, avoiding delays and improving system performance. To manage the replica placement strategy in the Cloud, three key challenges must be addressed. The challenges in determining the best time to make replicas were generated, the kind of files to replicate, as well as the best location to store the replicas. This survey conducts a review of 65 articles published on data replication in the cloud. The literature review examines a series of research publications and offers a detailed analysis. The analysis begins by presenting several replication strategies in the reviewing articles. Analysis of each contributor's performance measures is conducted. Moreover, this survey offers a comprehensive examination of data auditing systems. This work also determines the analytical evaluation of replication handling in the cloud. Furthermore, the evaluation tools used in the papers are examined. Furthermore, the survey describes a lot of research issues & limitations that might help researchers support better future work on pattern mining for data replication in the cloud. [ABSTRACT FROM AUTHOR]

Cancers are genetically diversified, so anticancer treatments have different levels of efficacy on people due to genetic differences. The main objective of this work is to predict the anticancer drug efficiency for colorectal cancer patients to reduce the mortality rates and provides immune energy for the patients. This paper proposes a novel anti-cancer drug efficacy system in colorectal cancer patients. The input data gene is normalized with the Min–Max normalization technique that normalizes the data in distinct scales. Subsequently, proposes an improved entropy-based feature to evaluate the uncertainty distribution of data, in which it induces weight to overcome the issue of computational complexity. Along with this feature, a correlation-based feature and statistical features are also retrieved. Subsequently, proposes a Recursive Feature Elimination with Hybrid Machine Learning (RFEHML) mechanism for selecting the appropriate feature set by eliminating the recursive features with the aid of hybrid Machine Learning strategies that combine decision tree and logistic regression. Also, the Gini impurity is employed for ranking the feature and selecting the maximum importance score by eliminating the least acquired importance score. Further, proposes a hybrid model for predicting the drug efficiency with the trained feature set. The hybrid model comprises of Long Short-Term Memory (LSTM) and Updated Rectified Linear Unit-Deep Convolutional Neural Network (UReLU-DCNN) model, in which DCNN is modified by updating the activation function at the fully connected layer. Consequently, the learned feature predicts the drug efficacy of anti-cancer in colorectal cancer patients by determining whether the patient is a responder or non-responder of the drug. Finally, the performance of the proposed RFEHML model is compared with other traditional approaches. It is found that the developed method has higher accuracy for each learning percentage, with values of 60LP = 92.48%, 70LP = 94.28%, 80LP = 95.24%, and 90LP = 96.86%, respectively. [ABSTRACT FROM AUTHOR]

Stock market forecasting remains a difficult problem in the economics industry due to its incredible stochastic nature. The creation of such an expert system aids investors in making investment decisions about a certain company. Due to the complexity of the stock market, using a single data source is insufficient to accurately reflect all of the variables that influence stock fluctuations. However, predicting stock market movement is a challenging undertaking that requires extensive data analysis, particularly from a big data perspective. In order to address these problems and produce a feasible solution, appropriate statistical models and artificially intelligent algorithms are needed. This paper aims to propose a novel stock market prediction by the following four stages; they are, preprocessing, feature extraction, improved feature level fusion and prediction. The input data is first put through a preparation step in which stock, news, and Twitter data (related to the COVID-19 epidemic) are processed. Under the big data perspective, the input data is taken into account. These pre-processed data are then put through the feature extraction, The improved aspect-based lexicon generation, PMI, and n-gram-based features in this case are derived from the news and Twitter data, while technical indicator-based features are derived from the stock data. The improved feature-level fusion phase is then applied to the extracted features. The ensemble classifiers, which include DBN, CNN, and DRN, were proposed during the prediction phase. Additionally, a SI-MRFO model is suggested to enhance the efficiency of the prediction model by adjusting the best classifier weights. Finally, SI-MRFO model's effectiveness compared to the existing models with regard to MAE, MAPE, MSE and MSLE. The SI-MRFO accomplished the minimal MAE rate for the 90th learning percentage is approximately 0.015 while other models acquire maximum ratings. [ABSTRACT FROM AUTHOR]

One of the leading causes of death for people worldwide is liver cancer. Manually identifying the cancer tissue in the current situation is a challenging and time-consuming task. Assessing the tumor load, planning therapies, making predictions, and tracking the clinical response can all be done using the segmentation of liver lesions in Computed Tomography (CT) scans. In this paper we propose a new technique for liver cancer classification with CT image. This method consists of four stages like pre-processing, segmentation, feature extraction and classification. In the initial stage the input image will be pre processed for the quality enhancement. This preprocessed output will be subjected to the segmentation phase; here improved deep fuzzy clustering technique will be applied for image segmentation. Subsequently, the segmented image will be the input of the feature extraction phase, where the extracted features are named as Improved Gabor Transitional Pattern, Grey-Level Co-occurrence Matrix (GLCM), Statistical features and Convolutional Neural Network (CNN) based feature. Finally the extracted features are subjected to the classification stage, here the two types of classifiers used for classification that is Bi-GRU and Deep Maxout. In this phase we will apply the Crossover mutated COOT optimization (CMCO) for tuning the weights, So that we will improve the quality of the image. This proposed technique, present the best accuracy of disease identification. The CMCO gained the accuracy of 95.58%, which is preferable than AO = 92.16%, COA = 89.38%, TSA = 88.05%, AOA = 92.05% and COOT = 91.95%, respectively. [ABSTRACT FROM AUTHOR]

In this paper, the development of action and event detectors over the past three decades is summarized. The detectors are divided into 2D detectors, 3D detectors and deep learning detectors according to whether they contain spatial information and whether they use deep learning. This paper briefly introduces the typical detectors of the different types mentioned above, and explains the advantages, disadvantages and characteristics respectively, and compares them. Comparing traditional feature detection methods with ones based on deep learning, we found that the method of first detecting microscopic details such as point, line, surface angle, etc., and then performing action and event recognition is no longer the mainstream of current research. Due to the strong generalization ability, end-to-end action and event recognition methods based on deep learning perform better than traditional methods. Finally, this paper proposes three research directions for action recognition and event recognition based on feature detectors. [ABSTRACT FROM AUTHOR]

Crop and weed identification remains a challenge for unmanned weed control. Due to the small range between the chopping tine and the important crop location, weed identification against the annual crops must be extremely exact. This study endeavor included a literature evaluation, which included the most important 50 research publications in IEEE, Science Direct, and Springer journals. From 2012 until 2022, all of these papers are gathered. In fact, the diagnosis steps include: preprocessing, feature extraction, and crop/weed classification. This research analyzes the 50 research articles in several aspects, such as the dataset used for evaluations, different strategies used for pre-processing, feature extraction, and classification to get a clear picture of them. Furthermore, each work's high performance in accuracy, sensitivity, and precision is demonstrated. Furthermore, the present hurdles in crop and weed identification are described, which serve as a benchmark for upcoming researchers. [ABSTRACT FROM AUTHOR]

Learning based credit prediction has attracted great interest from academia and industry. Different institutions hold a certain amount of credit data with limited users to build model. An institution has the requirement to obtain data from other institutions for improving model performance. However, due to the privacy protection and subject to legal restrictions, they encounter difficulties in data exchange. This affects the performance of credit prediction. In order to solve the above problem, this paper proposes a federated learning based semi-supervised credit prediction approach enhanced by multi-layer label mean, which can aggregate parameters of each institution via joint training while protecting the data privacy of each institution. Moreover, in actual production and life, there are usually more unlabeled credit data than labeled ones, and the distribution of their feature space presents multiple data-dense divisions. To deal with these, local meanNet model is proposed with a multi-layer label mean based semi-supervised deep learning network. In addition, this paper introduces a cost-sensitive loss function in the supervised part of the local mean model. Conducted on two public credit datasets, experimental results show that our proposed federated learning based approach has achieved promising credit prediction performance in terms of Accuracy and F1 measures. At the same time, the framework design mode that splits data aggregation and keys uniformly can improve the security of data privacy and enhance the flexibility of model training. [ABSTRACT FROM AUTHOR]

Big data refers to the data set that cannot be captured, managed and processed by conventional software tools within a certain period of time. In the seventh paper [[1]], in order to overcome the problem of poor recovery and stability of traditional big data fault-tolerant technology, a fault-tolerant technology of big data cluster in distributed flow processing system is proposed. By using spark application framework, a fault-tolerant model of big data cluster is built to realize the fault-tolerant of big data cluster in distributed flow processing system. [Extracted from the article]

Ageing and its related health conditions bring many challenges not only to individuals but also to society. Various MRI techniques are defined for the early detection of age-related diseases. Researchers continue the prediction with the involvement of different strategies. In that manner, this research intends to propose a new brain age prediction model under the processing of certain steps like preprocessing, feature extraction, feature selection, and prediction. The initial step is preprocessing, where improved median filtering is proposed to reduce the noise in the image. After this, feature extraction takes place, where shape-based features, statistical features, and texture features are extracted. Particularly, Improved LGTrP features are extracted. However, the curse of dimensionality becomes a serious issue in this aspect that shrinks the efficiency of the prediction level. According to the "curse of dimensionality," the number of samples required to estimate any function accurately increases exponentially as the number of input variables increases. Hence, a feature selection model with improvement has been introduced in this paper termed an improved Chi-square. Finally, for prediction purposes, a Hybrid classifier is introduced by combining the models like Bi-GRU and DBN, respectively. In order to enhance the effectiveness of the hybrid method, Upgraded Blue Monkey Optimization with Improvised Evaluation (UBMOIE) is introduced as the training system by tuning the optimal weights in both classifiers. Finally, the performance of the suggested UBMIOE-based brain age prediction method was assessed over the other schemes to various metrics. [ABSTRACT FROM AUTHOR]

The conventional approach to event extraction requires predefined event types and their corresponding annotations to train event extractors. However, these prerequisites are often difficult to satisfy in real-world applications. To automatically induct event types, most work has been devoted to clustering event triggers, where a cluster of event triggers is represented as an event type. Some works use trigger semantics, while others use co-occurrence relationships to cluster triggers. However, the clustering results of event triggers obtained by the above work are not sufficiently detailed in describing event types, making it difficult to accurately determine the corresponding event types manually. This paper proposes an open-domain event type induction framework that automatically discovers a set of event types from a given corpus. Unlike previous work on event trigger clustering, this paper takes into consideration the hierarchical relationship of event types to partition the event trigger clusters into event mains and subtypes. The framework employs a latent variable-based neural generation module and a semantic-based clustering module, the former of which obtains event trigger clusters representing the main types of events by jointly projecting the co-occurrence and semantic information of event triggers into a latent space for event type latent variable mining, and the latter of which further divides these event trigger clusters into event subtypes based on semantic information. Finally, experiment results show that, compared with the benchmark model, the ETGen-Clus can improve event type quality scores of 6.23% and 3.11% on the two datasets, respectively. [ABSTRACT FROM AUTHOR]

In order to improve the early warning effect of equipment abnormal state and shorten the early warning time, this paper designs an early warning method of laboratory equipment abnormal state based on the Internet of things and running big data. Collect the running status data of laboratory equipment in the environment of Internet of things, and implement dimension reduction processing on the collected running status data. After the dimensionality reduction, extract the abnormal characteristics of big data of laboratory equipment running. On the basis of iterative update, the real-time feature analysis results are compared with the abnormal feature set, and the early warning response program is started according to the abnormal. According to the experimental results, the maximum false alarm rate of this method is only 1.34%, and the abnormal state response is always kept below 4.0 s when applied, which fully proves that this method effectively realizes the design expectation. [ABSTRACT FROM AUTHOR]

In order to improve the accuracy of information exchange transmission and reduce the transmission response delay, this paper proposes an industrial Internet of things communication information exchange transmission method based on audio information hiding. Firstly, information embedding algorithm is used to extract the communication audio carrier information. Secondly, audio information hiding algorithm is used to realize the hidden embedding of specific information; Then, the information transmission data integration is realized according to the similarity matrix. Finally, the Pareto distribution is used to realize the information transmission data matching, and the audio information hiding is used to realize the data exchange transmission control. The experimental results show that the transmission accuracy of this method is 98.3%, the transmission accuracy is as high as 96.0%, and the response delay is only 16 ms, indicating that this method can improve the transmission effect of industrial Internet of things communication information exchange. [ABSTRACT FROM AUTHOR]

Ride-sharing contributes significantly to lowering trip expenses, easing traffic congestion and decreasing air pollution. However, current order pairing approaches in ride-sharing usually focus on minimizing total trip distances or maximizing platform profits, overlooking the drivers' desire for increased earnings. As a result, drivers might provide dishonest information to gain higher profits, leading to inefficient order pairing for the ride-sharing platform and potential losses for both the platform and drivers. In this paper, we address this challenging issue by developing efficient order pairing mechanisms that maximize the social welfare of the platform and drivers. Specifically, we introduce two truthful auction-based order pairing mechanisms, SWMOM-VCG and SWMOM-GM, where drivers bid on platform-published orders to complete them and earn profits. We provide theoretical proof that both mechanisms fulfill the criteria of individual rationality, profitability, truthfulness and so on. Using real taxi order data from New York City, we assess the performance of both mechanisms and show that they achieve greater social welfare compared to existing methods. Additionally, we find that SWMOM-GM requires less computation time than SWMOM-VCG for order pairing, with only a minor reduction in social welfare. [ABSTRACT FROM AUTHOR]

The rapid advancement of artificial intelligence (AI) has spotlighted ChatGPT as a key technology in the realm of information retrieval (IR). Unlike its predecessors, it offers notable advantages that have captured the interest of both industry and academia. While some consider ChatGPT to be a revolutionary innovation, others believe its success stems from smart product and market strategy integration. The advent of ChatGPT and GPT-4 has ushered in a new era of Generative AI, producing content that diverges from training examples, and surpassing the capabilities of OpenAI's previous GPT-3 model. In contrast to the established supervised learning approach in IR tasks, ChatGPT challenges traditional paradigms, introducing fresh challenges and opportunities in text quality assurance, model bias, and efficiency. This paper aims to explore the influence of ChatGPT on IR tasks, providing insights into its potential future trajectory. [ABSTRACT FROM AUTHOR]

User feedback data (e.g., clicks, dwell time in the product detail page) have been incorporated in the training process of many ranking models for better performance. Such approaches are widely used in many ranking applications, including search and recommendation. Recently, the inherent biases in user feedback data have been studied, which indicates how the users' behaviors can be affected by factors other than relevancy. By identifying and removing these biases, the ranking models can be further improved. Researchers have developed a variety of debiasing methods on different bias factors. Most of them only focus on one type of bias and pay little attention to different types of bias from a unified perspective. In this paper, we conduct a comprehensive study of bias focusing on the application of ranking problems in recommender systems which is highly important for the research of web intelligence. Then, we share our experiences derived from designing and optimizing unbiased models to improve feeds recommendation. To uncover the effects of biases and achieve better ranking performance, we propose several unbiased models and compare with state-of-the-art models. We conduct extensive offline experiments on real datasets and validate the effectiveness of our method by performing online A/B testing in a real-world recommender system. [ABSTRACT FROM AUTHOR]

Traditional financial data storage methods are prone to data leakage and narrow data coverage. Therefore, this paper proposes a dynamic and secure storage method of financial data based on cloud platform.In order to improve the ability of enterprise data management, the paper constructs a financial cloud computing platform, mining financial data by rough set theory, and analyzing the results of frequent pattern mining of financial data by fuzzy attribute characteristics.According to the granularity theory, the financial data is classified and processed, and the CSA cloud risk model is established to realize the dynamic and secure storage of financial data.The experimental results show that. The maximum data storage delay of this method is no more than 4.1 s, the maximum data leakage risk coefficient is no more than 0.5, the number of data types can reach 30, and the data storage coverage is improved. [ABSTRACT FROM AUTHOR]

Legal Charge Prediction (LCP) is a task of predicting charge labels for which a criminal should be charged based on the factual case descriptions. In recent years, the advancement of deep learning networks has led to the emergence of effective approaches for LCP through the Fine-tuning of pre-trained language models. While existing methods primarily treat it as multiple binary classifications, excelling in the single charge label given factual case descriptions, they still exhibit substantial room for improvement in multi-label LCP. This is because single-label classification methods do not effecitively deal with the relationship between multiple labels, mainly determining the likelihood of a factual description’s association to each individual charge label. Recognizing the need to enhance the relationship among multi-label charges in Fine-tuning-based discriminative charge prediction, this paper introduces a multi-label LCP method based on summarization generation. This method reconstructs the multi-label classification problem as a summarization generation task, capturing the relationship among multiple labels within the summarization content. Empirical findings from the Chinese legal domain’s CAIL2018 dataset demonstrate the method’s capacity to discern the relationship between multiple legal charge labels with Micro-F1 and Macro-F1 enhancements, compared to the fine-tuning-based approaches. [ABSTRACT FROM AUTHOR]

In order to overcome the problems of high unloading time cost, long unloading task delay and poor load balance of traditional offloading methods, this paper studies the mobile edge computing task offloading method of wireless network based on improved genetic algorithm. Based on the wireless network mobile edge computing architecture, a wireless network mobile edge computing task scheduling scheme is constructed to lay the foundation for subsequent task offloading. Then, the improved genetic algorithm is used for initial operation allocation and offloading priority ranking, and the mobile edge computing task offloading is realized by dynamically adjusting the trade-off coefficient. The experimental results show that the offloading time cost of this method is between 0.16 min–0.31 min, the offloading task delay is between 1.05 s–1.47 s, and the load balance can reach 97.9%, indicating that it effectively realizes the design expectation. [ABSTRACT FROM AUTHOR]

Technology-enabled learning has progressively grown for research areas with wide application of information and communication technologies for numerous standard-compliant Learning and Open Educational Resources. This provides formidable support to users for the selection of courses when they want to develop the course with available learning materials. But selecting a course via searching learning objects is an inherently complex operation having various repositories. In an E-learning Platform, many complexities arise due to various software tools and specification formats that hinder the success of the course. In this paper, many obstacles in the E-learning platform are eradicated by utilizing Fuzzy Local Information C-Means (FLICM) clustering with matrix factorization for the selection of courses. The dataset utilized in this work is E-Khool review data, from which an agglomerative matrix is generated. Here, the agglomerative matrix consists of the learner series matrix and course series matrix along with their binary matrix. After this process, course grouping is carried out by FLICM clustering with matrix factorization. Moreover, group course bilevel matching, followed by relevant learner retrieval and group user is done by Minkowski and Chebyshev distance. From this learner's preferred course is retrieved and then a recommendation using matrix factorization is carried out. Finally, the course is recommended for the user based on maximum rating. Furthermore, the performance of developed FLICM_matrix factorization is achieved by performance metrics, like precision, recall, and f-measure with values 0.915, 0.850, and 0.882, accordingly. [ABSTRACT FROM AUTHOR]

Handoff management is the method in which the mobile node maintains its connection active when it shifts from location to other. The devastating success of mobile devices as well as wireless communications is emphasizing the requirement for the expansion of mobility-aware facilities. Moreover, the mobility of devices requires services adapting their behavior to abrupt context variations and being conscious of handoffs, which make an intermittent discontinuities and unpredictable delays. Thus, the heterogeneity of wireless network devices confuses the situation, since a dissimilar treatment of handoffs and context-awareness is essential for every solution. Hence, this paper introduced the Deep Q network-based Firefly Aquila Optimizer (DQN-FAO) for performing the handoff management. In order to establish the handoff management, the process of selecting network is very important. Here, the network is selected based on the devised FAO algorithm, which is the consolidation of Aquila Optimizer (AO) and Firefly algorithm (FA) that considers the metrics, such as Jitter, Handoff latency, and Received Signal Strength Indicator (RSSI) as fitness function. Moreover, the handover decision is taken by the DQN, where the hyper-parameters are tuned by the devised FAO algorithm. According to the hand over decision taken, the context aware video streaming is happened by adjusting the bit rate of the videos using network bandwidth. Besides, the devised scheme attained the superior performance based on the call drop, energy consumption, handover delay, throughput, handoff latency, and PSNR of 0.5122, 7.086 J, 10.54 ms, 13.17 Mbps, 93.80 ms and 46.89 dB. [ABSTRACT FROM AUTHOR]

The convergence of systems neuroscience and open science arouses great interest in the current brain big data era, highlighting the thinking capability of intelligent agents in handling multi-source knowledge, information and data across various levels of granularity. To realize such thinking-inspired brain computing during a brain investigation process, one of the major challenges is to find a holistic brain map that can model multi-dimensional variables of brain investigations across brain functions, experimental tasks, brain data and analytical methods synthetically. In this paper, we propose a context-enhanced graph learning method to fuse open knowledge from different sources, including: contextual information enrichment, structural knowledge fusion, and holistic graph learning. Such a method can enhance contextual learning of abstract concepts and relational learning between two concepts that have large gap from different dimensions. As a result, an extensible space, namely Thinking Space, is generated to represent holistic variables and their relations in a map, which currently contributes to the field of brain research for systematic brain computing. In the future, the Thinking Space coupled with the rapid development and spread of artificial intelligence generated content will be developed in more scenarios so as to promote global interactions of intelligence in the connected world. [ABSTRACT FROM AUTHOR]

The legal judgments are always based on the description of the case, the legal document. However, retrieving and understanding large numbers of relevant legal documents is a time-consuming task for legal workers. The legal judgment prediction (LJP) focus on applying artificial intelligence technology to provide decision support for legal workers. The prison term prediction(PTP) is an important task in LJP which aims to predict the term of penalty utilizing machine learning methods, thus supporting the judgement. Long-Short Term Memory(LSTM) Networks are a special type of Recurrent Neural Networks(RNN) that are capable of handling long term dependencies without being affected by an unstable gradient. Mainstream RNN models such as LSTM and GRU can capture long-distance correlation but training is time-consuming, while traditional CNN can be trained in parallel but pay more attention to local information. Both have shortcomings in case description prediction. This paper proposes a prison term prediction model for legal documents. The model adds causal expansion convolution in general TextCNN to make the model not only limited to the most important keyword segment, but also focus on the text near the key segments and the corresponding logical relationship of this paragraph, thereby improving the predicting effect and the accuracy on the data set. The causal TextCNN in this paper can understand the causal logical relationship in the text, especially the relationship between the legal text and the prison term. Since the model uses all CNN convolutions, compared with traditional sequence models such as GRU and LSTM, it can be trained in parallel to improve the training speed and can handling long term. So causal convolution can make up for the shortcomings of TextCNN and RNN models. In summary, the PTP model based on causality is a good solution to this problem. In addition, the case description is usually longer than traditional natural language sentences and the key information related to the prison term is not limited to local words. Therefore, it is crucial to capture substantially longer memory for LJP domains where a long history is required. In this paper, we propose a Causality CNN-based Prison Term Prediction model based on fact descriptions, in which the Causal TextCNN method is applied to build long effective history sizes (i.e., the ability for the networks to look very far into the past to make a prediction) using a combination of very deep networks (augmented with residual layers) and dilated convolutions. The experimental results on a public data show that the proposed model outperforms several CNN and RNN based baselines. [ABSTRACT FROM AUTHOR]

The Semantic Web (Web 3.0) is an advancement of the existing web in which knowledge is given well-defined importance, allowing people and machines to operate better. The Semantic Web is the next step in the evolution of the Web. The semantic web improves online technologies in need of generating, distributing, and linking material. In literature, multiple surveys have been done on the semantic web (Web 3.0), but those surveys are limited to some specific topics. According to the best of our understanding, none of the surveys provides a comprehensive study about the applications, challenges, and future of the semantic web along with its relationship with the Internet of things (IoT). The previous surveys focused on the Web 3.0 without touching on applications or challenges or focused on only the application prospect of the web 3.0, focused on the just the challenges, or focused on web 3.0 relationship with either internet of things or knowledge graphs but failed to touch the other important factors i.e., failed to provide comprehensive web 3.0 survey. This survey paper covers the gaps created from the previous survey papers in the same field and provides a comprehensive survey about web 3.0, a comparison between web 1.0, 2.0, and 3.0, the study of application and challenges in web 3.0, the relationship between web 3.0 with IoT and knowledge graph. Moreover, it focuses on the evolution of the web, and semantic web along with an explanation of the various layers, ontology tools, and semantic web tools with their comparison and semantic web service search. Despite all the shortcomings and challenges, the semantic web is moving in the right direction, and it is the future of the web. [ABSTRACT FROM AUTHOR]

Customer Relationship Management (CRM) has been an important ally to companies willing to improve their customer experiences. Lately, Machine Learning (ML) and its techniques have been transforming the way companies interact with their customers through data analysis. This paper reviews the literature on the application of ML techniques to improve CRM processes and provides an overview of used techniques and their application to each CRM dimension and element. In addition, the practical implications are analyzed based on recent developments of CRM tools in the field of ML. The paper delivers insights for future studies as well as for CRM tools willing to improve their analytical and automated services and features. [ABSTRACT FROM AUTHOR]

Recommender systems are pivotal components of modern Internet platforms and constitute a well-established research field. By now, research has resulted in highly sophisticated recommender algorithms whose further optimization often yields only marginal improvements. This paper goes beyond the commonly dominating focus on optimizing algorithms and instead follows the idea of enhancing recommender systems with reputation data. Since the concept of reputation-enhanced recommender systems has attracted considerable attention in recent years, the main aim of the paper is to provide a comprehensive survey of the approaches proposed so far. To this end, existing work is identified by means of a systematic literature review and classified according to seven carefully considered dimensions. In addition, the resulting structured analysis of the state of the art serves as a basis for the deduction and discussion of several future research directions. [ABSTRACT FROM AUTHOR]

In order to overcome the problems existing in traditional financial risk evaluation methods, such as high generalization error and fitting degree with actual value, this paper designs a financial risk evaluation method of blockchain digital currency based on CART algorithm. After screening and ranking the financial risk indicators of blockchain digital currency, the financial risk level of blockchain digital currency is determined by combining CART algorithm. Finally, by calculating the covariance matrix of the financial risk decision matrix, the positive ideal solution and the negative ideal solution of the financial risk are found, and then the final evaluation result is obtained by combining the progress of the financial risk. Experimental results show that the minimum generalization error of this method is only 0.021, the fitting degree of the obtained results and the actual risk can reach 98.0%, and the maximum risk accuracy can reach 0.978. [ABSTRACT FROM AUTHOR]

In order to overcome the problems of retrieval accuracy and time-consuming of traditional document information retrieval methods, this paper designs an intelligent retrieval method of library document information based on hidden topic mining. Firstly, LDA model is used to mine the hidden topics of library document information, and then, based on the mining results, similarity degree of document information is calculated in inference network model. Finally, the Bayesian model is constructed in the sample space to retrieve the library literature information under the maximum retrieval space coverage. Experimental results show that, compared with traditional retrieval methods, the proposed method improves the retrieval accuracy significantly, with the highest retrieval accuracy reaching 99%, and the retrieval time is significantly reduced, indicating that the proposed method effectively improves the retrieval accuracy and timeliness. [ABSTRACT FROM AUTHOR]

Now a days, the Internet of Things (IoT) plays a vital role in every industry including agriculture due to its widespread and easy integrations. The agricultural methods are incorporated with IoT technologies for significant growth in agricultural fields. IoT is utilized to support farmers in using their resources effectively and support decision-making systems with better field monitoring techniques. The data collected from IoT-based agricultural systems are highly vulnerable to attack, hence to address this issue it is necessary to employ an authentication scheme. In this paper, Auth Key_Deep Convolutional Neural Network (Auth Key_DCNN) is designed to promote secure data sharing in IoT-enabled agriculture systems. The different entities, namely sensors, Private Key Generator (PKG), controller, and data user are initially considered and the parameters are randomly initialized. The entities are registered and by using DCNN a secret key is generated in PKG. The encryption of transmitted data is performed in the data protection phase during the protection of data between the controller and the user. Additionally, the performance of the designed model is estimated, where the experimental results revealed that the Auth Key_DCNN model recorded superior performance with a minimal computational cost of 142.56, a memory usage of 49.5 MB, and a computational time of 1.34 sec. [ABSTRACT FROM AUTHOR]

From the planting base to the consumer's table, agricultural products must go through multiple links such as planting, processing, transportation, warehousing, and sales. The quality and safety of agricultural products have received extensive attention from all walks of life. Based on the block chain technology, this paper will build a traceability system for the quality and safety of agricultural products, refine the research objects, and design solutions from the aspects of overall structure, role authority, operating process, and functional modules according to the characteristics of planted agricultural products, so as to realize the whole process of agricultural product supply chain tracking, traceability to ensure the quality and safety of agricultural products. [ABSTRACT FROM AUTHOR]

Blockchain technology is commonly used as a replicated and distributed database in different areas. In this paper, a smart home blockchain network connects smart homes through smart devices for reducing carbon footprint and thereby earning bitcoin value in the network. The network is composed of different smart homes interconnected with smart devices. The user makes a transaction request through the network layer and matches the user's activity with the reward table located at the incentive layer to estimate the bitcoin value. Furthermore, the miner verifies the transaction and sends the bitcoin value to the user, and adds the respective block to the network structure. The optimal parameter used to estimate the bitcoin value is computed using the proposed Improved Invasive Weed Mayfly Optimization (IIWMO) algorithm. The developed method attained higher performance with the metrics, like coins earned, Annual Carbon Reduction (ACR), and fitness as 0.00357BTC, 23.891, and 0.6618 for 200 users. For 200 users the fitness obtained by the proposed method is 14.41%, 16.68%, and 11.68% higher when compared to existing approaches namely, Without optimization, IIWO, and MA, respectively. [ABSTRACT FROM AUTHOR]

This paper presents an automatic facial micro-expression recognition system (FMER) from video sequence. Identification and classification are performed on basic expressions: happy, surprise, fear, disgust, sadness, anger, and neutral states. The system integrates three main steps. The first step consists in face detection and tracking over three consecutive frames. In the second step, the facial contour extraction is performed on each frame to build Euclidean distance maps. The last task corresponds to the classification which is achieved with two methods; the SVM and using convolutional neural networks. Experimental evaluation of the proposed system for facial micro-expression identification is performed on the well-known databases (Chon and Kanade and CASME II), with six and seven facial expressions for each classification method. [ABSTRACT FROM AUTHOR]

Modern systems like the Internet of Things, cloud computing, and sensor networks generate a huge data archive. The knowledge extraction from these huge archived data requires modified approaches in algorithm design techniques. The field of study in which analysis of such huge data is carried out is called big data analytics, which helps to optimize the performance with reduced cost and retrieves the information efficiently. The enhancement of traditional data analytics needs to modify to suit big data analytics because it may not manage huge amounts of data. The real thought is how to design the data mining algorithms suitable to handle big data analysis. This paper discusses data analytics at the initial level, to begin with, the insights about the analysis process for big data. Big data analytics have a current research edge in the knowledge extraction field. This paper highlights the challenges and problems associated with big data analysis and provide inner insights into several techniques and methods used. [ABSTRACT FROM AUTHOR]

Task scheduling problems are involved in various fields, such as personal travel planning, UAV group path planning, intelligent furniture task scheduling and so on. As most of these task scheduling problems are subject to constraints of time, space and resource, conflicts often arise. However, the existing methods are typically limited to specific areas or geared to meet one or two types of constraints. As a result, they are unable to solve all conflicts systematically. This paper proposes a Task Heterogeneous Information Network (THIN) to model scheduling tasks and constraints comprehensively. Then, by dynamically exploring and converting Task Heterogeneous Information Networks, a series of algorithms are designed to detect and resolve all types of conflicts. Finally, conflict-free task plans are produced as outputs. Experiments have been conducted on datasets of different sizes, and the results show that our methods are effective. [ABSTRACT FROM AUTHOR]

The allocation of resources in the cloud environment is efficient and vital, as it directly impacts versatility and operational expenses. Containers, like virtualization technology, are gaining popularity due to their low overhead when compared to traditional virtual machines and portability. The resource allocation methodologies in the containerized cloud are intended to dynamically or statically allocate the available pool of resources such as CPU, memory, disk, and so on to users. Despite the enormous popularity of containers in cloud computing, no systematic survey of container scheduling techniques exists. In this survey, an outline of the present works on resource allocation in the containerized cloud correlative is discussed. In this work, 64 research papers are reviewed for a better understanding of resource allocation, management, and scheduling. Further, to add extra worth to this research work, the performance of the collected papers is investigated in terms of various performance measures. Along with this, the weakness of the existing resource allocation algorithms is provided, which makes the researchers to investigate with novel algorithms or techniques. [ABSTRACT FROM AUTHOR]

In order to overcome the problems of the traditional power big data mining methods, such as the low integrity of data mining and the long time-consuming of data mining, this paper realizes multi-dimensional power big data mining by improving the grey clustering algorithm. Firstly, a relay multi hop network is established to collect power big data through the collector; Secondly, Lagrange interpolation method is used to fill the missing data of power data mining; Standardized processing of power consumption data; Finally, according to the grey theory and FCM clustering algorithm, the multi-dimensional power big data mining is realized. The experimental results show that the integrity of power big data mining in this method is up to 0.996, the mining time is not more than 3.05 s, and the mining integrity is up to 0.992, which indicates that this method can effectively improve the effect of power big data mining. [ABSTRACT FROM AUTHOR]

Service Oriented Architecture is built on services in which web service discovery is one of the most widely explored domains. Service reputation plays a vital role in the discovery process while selecting the optimal service from a large pool of functionally equivalent services. Reputation mechanism attempts to forecast the future performance of a service based on its past behaviors generally obtained in the form of feedback ratings submitted by users of the service. These feedback ratings about a service may vary from user to user. Variations in feedback ratings could be because of different users' different subjective judgments and/or dishonest users' purposely submitted unfair ratings. This paper proposes a service reputation measurement approach counting such diversified act of rating. To realize this goal, an efficient user credibility assessment methodology has been devised by employing the measure of interrater reliability. Experiments are performed to validate the feasibility and effectiveness of the proposed reputation measurement approach. The experimental results depict that the proposed approach can fairly assess service reputations in the presence of various kinds of raters in the system. [ABSTRACT FROM AUTHOR]

Virtual communities are complex systems interweaving technical and human aspects. We propose here to identify usual functions provided by virtual communities and define a typology based on the common objects or goals shared by their users. The papers included in this special issue show some examples of research works lead in some of these categories of communities. [ABSTRACT FROM AUTHOR]

User navigation behavior modeling has attracted much research interest on the web. User Navigation Behavior modeling deals with creating a model to predict future user click based on previous user clicks. Previous works focus on predicting the next page without much attention to the user-selected content. In contrast to existing works, this paper focuses on the user selected contents and the page content layout for creating the user navigation behavior. In this paper, for the first time in the literature, a novel model for user navigation prediction has been proposed using the time series neural network. The performance of the proposed prediction model was examined by two variations of time series neural networks including nonlinear autoregressive (NAR) and nonlinear autoregressive with exogenous input (NARX). Providing external information for the NARX, page content layout is used. The experimental results show that the NAR and NARX neural networks are more efficient than other methods for click prediction, and using the page content layout in the NARX improves the accuracy of predictions. [ABSTRACT FROM AUTHOR]

Early detection of depression is important to improve human well-being. This paper proposes a new method to detect depression through time-frequency analysis of Internet behaviors. We recruited 728 postgraduate students and obtained their scores on a depression questionnaire (Zung Self-rating Depression Scale, SDS) and digital records of Internet behaviors. By time-frequency analysis, classification models are built to differentiate higher SDS group from lower group, and prediction models are built to identify mental status of depressed group more precisely. Experimental results show classification and prediction models work well, and time-frequency features are effective in capturing the changes of mental health status. Results of this paper are useful to improve the performance of public mental health services. [ABSTRACT FROM AUTHOR]

A crowd sensing network takes the use of the dense distribution, frequent encountering, and social property of mobile terminals in real society. In a crowd sensing network, the datasensing transmission is based on the collaborative opportunistic transmission, the final message data will be forwarded to the user who is willing to report data (e.g. high battery capacity and with low-cost Internet link equipment), and then report the perceptual data to the backdrop data center. In the mobile crowdsensing network, there is a big difference in the social ability of each node carrier, so that the data forwarding capability of each node is also different. In this paper, we first analyze the shortcomings of node based on node quantization. On these grounds, this paper proposes a data forwarding mechanism based on node function and node centricity, and gets its performance improved effectively, and reduces the energy consumption of nodes. [ABSTRACT FROM AUTHOR]

In order to overcome the problems of poor data classification accuracy and effectiveness of traditional data monitoring methods, this paper designs a data security monitoring method based on narrow-band Internet of things. Firstly, the model of network data acquisition and sensor node's optimal configuration is established to collect intranet data. Based on the analysis of data characteristics, dynamic intranet data analysis indexes are designed from three aspects: establishing security incident quantity index, establishing address entropy index and data diversion. According to the above-mentioned narrow-band data aggregation rate, the security index of the Internet of things is calculated to realize the security of monitoring data. The experimental results show that: whether the network attack exists or not, the accuracy rate of the method is always higher than 90%, the classification time is less than 4 s, and the energy consumption of monitoring process is always less than 150 J, which fully proves that the method achieves the design expectation. [ABSTRACT FROM AUTHOR]

Web data extraction has seen significant development in the last decade since its inception in the early nineties. It has evolved from a simple manual way of extracting data from web page and documents to automated extraction to an intelligent extraction using machine learning algorithms, tools and techniques. Data extraction is one of the key components of end-to-end life cycle in web data extraction process that includes navigation, extraction, data enrichment and visualization. This paper presents the journey of web data extraction over the last many years highlighting evolution of tools, techniques, frameworks and algorithms for building intelligent web data extraction systems. The paper also throws light into challenges, opportunities for future research and emerging trends over the years in web data extraction with specific focus on machine learning techniques. Both traditional and machine learning approaches to manual and automated web data extraction are experimented and results published with few use cases demonstrating the challenges in web data extraction in the event of changes in the website layout. This paper introduces novel ideas such as self-healing capability in web data extraction and proactive error detection in the event of changes in website layout as an area of future research. This unique perspective will help readers to get deeper insights in to the present and future of web data extraction. [ABSTRACT FROM AUTHOR]

Agriculture is a significant source of income, and categorizing the crop has turned into vital factor that aids more in the crop production sector. Traditionally, crop development stage determination is done manually by eye inspection. However, producing high-quality crop type maps using modern approaches remains difficult. In this paper, the hyperspectral crop image classification model is proposed that includes four stages, they are (a) preprocessing, (b) segmentation, (c) feature extraction and (d) classification. In the preprocessing step, the hyperspectral image is provided as input, where the filtering process will carried out using median filtering. The filtered image is then used as the segmentation’s input. The image is segmented in the segmentation step using the enhanced entropy-based fuzzy c-means technique. Subsequently, spectral spatial features and vegetation index-based features are derived from segmented images. The final step is the classification, where the ensemble of classification model will be used that includes models like Convolutional Neural Networks (CNN), Deep Maxout (DMO), Recurrent Neural Networks (RNN), and Bidirectional Gated Recurrent Unit (Bi-GRU), respectively. The proposed Self Improved Tasmanian devil Optimization (SI-TDO) approach has optimally adjusted the Bi-GRU model’s training weights to enhance ensemble classification performance. Finally, the effectiveness of the proposed SI-TDO method compared to the traditional algorithm is examined for several metrics. The SI-TDO obtained the greatest accuracy of 94.68% in training rate 80, while other existing models have the lowest ratings. [ABSTRACT FROM AUTHOR]

Due to the enormous content of text available online through emails, social media, and news articles, it has become complicated to summarize the textual information from multiple documents. Text summarization automatically creates a comprehensive description of the document that retains its informative contents through the keywords, where Multi-Document Summarization (MDS) is a productive tool for data accumulation that creates a concise and informative summary from the documents. In order to extract the relevant information from the documents, Graph neural networks (GNNs) is the neural structure that detains the interrelation of the graph by progressing the messages between the graphical nodes. In the current years, the advanced version of GNNs, such as graph attention network (GAN), graph recurrent network, and graph convolutional network (GCN) provides a remarkable performance in text summarization with the advantage of deep learning techniques. Hence, in this survey, graph approaches for text summarization has been analyzed and discussed, where the recent text summarization model based on Deep learning techniques are highlighted. Further, the article provides the taxonomy to abstract the design pattern of Neural Networks and conducts a comprehensive of the existing text summarization model. Finally, the review article enlists the future direction of the researcher, which would motivate the enthusiastic and novel contributions in text summarizations. [ABSTRACT FROM AUTHOR]

Mobile Ad-Hoc Network (MANETs) is referred to as the mobile wireless nodes that make up ad hoc networks. The network topology may fluctuate on a regular basis due to node mobility. Each node serves as a router, passing traffic throughout the network, and they construct the network’s infrastructure on their own. MANET routing protocols need to be able to store routing information and adjust to changes in the network topology in order to forward packets to their destinations. While mobile networks are the main application for MANET routing techniques, networks with stationary nodes and no network infrastructure can also benefit from using them. In this paper, we proposed a Self Adaptive Tasmanian Devil Optimization (SATDO) based Routing and Data Aggregation in MANET. The first step in the process is clustering, where the best cluster heads are chosen according to a number of limitations, such as energy, distance, delay, and enhanced risk factor assessment on security conditions. In this study, the SATDO algorithm is proposed for this optimal selection. Subsequent to the clustering process, routing will optimally take place via the same SATDO algorithm introduced in this work. Finally, an improved kernel least mean square-based data aggregation method is carried out to avoid data redundancy. The efficiency of the suggested routing model is contrasted with the conventional algorithms via different performance measures. [ABSTRACT FROM AUTHOR]

The fast advancement of information technology has resulted in more efficient information storage and retrieval. As a result, most organizations, businesses, and governments are releasing and exchanging a large amount of micro data among themselves for commercial or research purposes. However, incorrect data exchange will result in privacy breaches. Many methods and strategies have been developed to address privacy breaches, and Anonymization is one of them that many companies use. In order to perform anonymization, identification of the Quasi Identifier (QI) is significant. Hence this paper proposes a method called Quasi Identification Based on Tree (QIBT) for automatic QI identification. The proposed method derives the QI, based on the relationship between the numbers of distinct values assumed by the set of attributes. So, it uses the tree data structure to derive the unique and infrequent attribute values from the entire dataset with less computational cost. The proposed method consists of four phases: (i) Unique attribute value computation (ii) Tree construction and (iii) Computation of quasi-identifier from the tree (iv) Applying Anonymization Technique to the identified QI. Attributes with high risk of disclosure are identified using our proposed algorithm. Synthetic data are created exclusively for the detected QI using a partial synthetic data generating technique to improve usefulness. The suggested method's efficiency is tested with a subset of the UCI machine learning dataset and produces superior results when compared to other current approaches. [ABSTRACT FROM AUTHOR]

The on-demand transport (ODT) systems have developed worldwide as they have significant social, environmental, and economic benefits. Even with those benefits, it's still important to gain popular acceptance. The acceptance key is the reactivity of the system in providing fast and reliable solutions whilst respecting vehicles' and clients' constraints. This paper presents a decentralized multi-agent approach to model and solve the ODT problem in a static road network. The agents interact with each other using the A* algorithm to find an optimal solution for each transport demand. The optimal solution is expressed by the fastest trajectory taken by the cheapest vehicles. We utilize factual data from a Lebanese city to do experiments evaluating the proposed approach. [ABSTRACT FROM AUTHOR]