Human Behavior Analysis Using Intelligent Big Data Analytics.

Intelligent big data analysis is an evolving pattern in the age of big data science and artificial intelligence (AI). Analysis of organized data has been very successful, but analyzing human behavior using social media data becomes challenging. The social media data comprises a vast and unstructured format of data sources that can include likes, comments, tweets, shares, and views. Data analytics of social media data became a challenging task for companies, such as Dailymotion, that have billions of daily users and vast numbers of comments, likes, and views. Social media data is created in a significant amount and at a tremendous pace. There is a very high volume to store, sort, process, and carefully study the data for making possible decisions. This article proposes an architecture using a big data analytics mechanism to efficiently and logically process the huge social media datasets. The proposed architecture is composed of three layers. The main objective of the project is to demonstrate Apache Spark parallel processing and distributed framework technologies with other storage and processing mechanisms. The social media data generated from Dailymotion is used in this article to demonstrate the benefits of this architecture. The project utilized the application programming interface (API) of Dailymotion, allowing it to incorporate functions suitable to fetch and view information. The API key is generated to fetch information of public channel data in the form of text files. Hive storage machinist is utilized with Apache Spark for efficient data processing. The effectiveness of the proposed architecture is also highlighted

Economic appraisal of offshore fisheries: A study on trawl fishing operations in Pakistan

73-79This study attempts to show the effects and relative contributions of the different fisheries factors affecting the revenues for a sample of commercial offshore trawling vessels operating in the Exclusive Economic Zone (EEZ) of Pakistan. In this study, the level of relative contribution of each determinant is estimated by using the Standard Multiple Linear regression (SMLR) with standardized regression coefficients and correlation methods. The data were collected through the survey questionnaire and direct interviews with the boat owners and fishermen. The estimated standard beta regression coefficient values for the catch (ß1=0.253), horsepower (ß2=0.26), fishing days at sea (ß3=0.316) and skipper or captain’s fishing experience (ß4=0.32), respectively. Similarly, the catch contributes (R2=17.7 %), horsepower (R2=18.2 %), fishing days (R2=22.5 %) and skipper (R2=23 %) on the revenue. Moreover, the correlation values for the catch (r(Revenue, Catch) = 0.70), horsepower (r(Revenue, Horsepower) = 0.698), fishing days (r(Revenue, Fishing Days) = 0.713) and skipper (r(Revenue, Skipper)= 0.718) indicates the strong positive relationship of each variable on the revenue. In conclusion, the skipper fishing experience and individual skills have a very strong influence on the maximizing of total earnings of trawling vessels

Privacy-Aware Data Forensics of VRUs Using Machine Learning and Big Data Analytics

The present spreading out of big data found the realization of AI and machine learning. With the rise of big data and machine learning, the idea of improving accuracy and enhancing the efficacy of AI applications is also gaining prominence. Machine learning solutions provide improved guard safety in hazardous traffic circumstances in the context of traffic applications. The existing architectures have various challenges, where data privacy is the foremost challenge for vulnerable road users (VRUs). The key reason for failure in traffic control for pedestrians is flawed in the privacy handling of the users. The user data are at risk and are prone to several privacy and security gaps. If an invader succeeds to infiltrate the setup, exposed data can be malevolently influenced, contrived, and misrepresented for illegitimate drives. In this study, an architecture is proposed based on machine learning to analyze and process big data efficiently in a secure environment. The proposed model considers the privacy of users during big data processing. The proposed architecture is a layered framework with a parallel and distributed module using machine learning on big data to achieve secure big data analytics. The proposed architecture designs a distinct unit for privacy management using a machine learning classifier. A stream processing unit is also integrated with the architecture to process the information. The proposed system is apprehended using real-time datasets from various sources and experimentally tested with reliable datasets that disclose the effectiveness of the proposed architecture. The data ingestion results are also highlighted along with training and validation results

A novel category detection of social media reviews in the restaurant industry

Social media platforms have enabled users to share their thoughts, ideas, and opinions on different subject matters and meanwhile generate lots of information which can be adopted to understand people’s emotion towards certain products. This information can be effectively applied for Aspect Category Detection (ACD). Similarly, people’s emotions and recommendation-based Artificial Intelligence (AI)-powered systems are in trend to assist vendors and other customers to improve their standards. These systems have applications in all sorts of business available on multiple platforms. However, the current conventional approaches fail in providing promising results. Thus, in this paper, we propose novel convolutional attention-based bidirectional modified LSTM by combining the techniques of the next word, next sequence, and pattern prediction with ACD. The proposed approach extracts significant features from public reviews to detect entity and attribute pair, which are treated as a sequence or pattern from a given opinion. Next, we trained our word vectors with the proposed model to strengthen the ACD process. Empirically, we compare the approach with the state-of-the-art ACD models that use SemEval-2015, SemEval-2016, and SentiHood datasets. Results show that the proposed approach effectively achieves 78.96% F1-Score on SemEval-2015, 79.10% F1-Score on SemEval-2016, and 79.03% F1-Score on SentiHood which is higher than the existing approaches

Coupling relationship of leaf economic and hydraulic traits of alhagi sparsifolia shap. In a hyper-arid desert ecosystem

In this study, Alhagi sparsifolia Shap. was used to test the hypothesis that leaf economic and hydraulic traits are coupled in plants in a hyper-arid region. Five economic traits and six hydraulic traits were examined to explore the relationship. Results showed that the stomatal density (SD) on both surfaces was coupled with maximum stomatal conductance to water vapor (gwmax) and leaf tissue density (TD). SD on adaxial surface (SDaba) was significantly positively related to vein density (VD) but negatively related to leaf thickness (LT) and stomatal length on adaxial surface (SLada). Nitrogen concentration based on mass (Nmass) was significantly negatively correlated with leaf mass per area (LMA), LT, and VD, whereas nitrogen concentration based on area (Narea) was significantly positively related to LMA and TD. Mean annual precipitation (MAP) contributed the most to the changes in LT and stomatal length (SL). Soil salt contributed the most to TD, SD, and gwmax. Soli nutrients influenced the most of LMA and VD. Mean annual temperature contributed the most to Nmass and Narea. In conclusion, the economics of leaves coupled with their hydraulic traits provides an economical and efficient strategy to adapt to the harsh environment in hyper-arid regions.Fil: Yin, Hui. University Of Chinese Academy Of Sciences; China. Xinjiang University; China. Xinjiang Institute Of Ecology And Geography Chinese Academy Of Sciences; China. Cele National Station Of Observation And Research For Desert-grassland Ecosystems; ChinaFil: Tariq, Akash. University Of Chinese Academy Of Sciences; China. Cele National Station Of Observation And Research For Desert-grassland Ecosystems; China. Xinjiang Institute Of Ecology And Geography Chinese Academy Of Sciences; ChinaFil: Zhang, Bo. University Of Chinese Academy Of Sciences; China. Cele National Station Of Observation And Research For Desert-grassland Ecosystems; China. Xinjiang Institute Of Ecology And Geography Chinese Academy Of Sciences; ChinaFil: Lv, Guanghui. Xinjiang University; ChinaFil: Zeng, Fanjiang. Cele National Station Of Observation And Research For Desert-grassland Ecosystems; China. Xinjiang Institute Of Ecology And Geography Chinese Academy Of Sciences; China. University Of Chinese Academy Of Sciences; ChinaFil: Graciano, Corina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Fisiología Vegetal. Universidad Nacional de La Plata. Facultad de Ciencias Naturales y Museo. Instituto de Fisiología Vegetal; ArgentinaFil: Santos, Mauro. Universidade Federal de Pernambuco; BrasilFil: Zhang, Zhihao. University Of Chinese Academy Of Sciences; China. Xinjiang Institute Of Ecology And Geography Chinese Academy Of Sciences; China. Cele National Station Of Observation And Research For Desert-grassland Ecosystems; ChinaFil: Wang, Peng. Cele National Station Of Observation And Research For Desert-grassland Ecosystems; China. Xinjiang Institute Of Ecology And Geography Chinese Academy Of Sciences; ChinaFil: Mu, Shuyong. Xinjiang Institute Of Ecology And Geography Chinese Academy Of Sciences; Chin

Integrated cooperative spectrum sensing and access control for cognitive Industrial Internet of Things

Industrial Internet of Things (IIoT) usually utilizes 2.4-GHz unlicensed frequency band, which is also heavily used by many other communication systems, such as ZigBee, WiFi, Bluetooth, etc. Therefore, the lack of spectrum resources has become a key technical bottleneck to restrict the development of IIoT. Integrating cognitive radio (CR) into IIoT, Cognitive IIoT (CIIoT) can cope with the spectrum resource shortage by accessing the frequency bands licensed to primary user (PU). However, spectrum sensing and access control must be performed to avoid bringing severe interference to the PU. In this article, an integrated cooperative spectrum sensing (CSS) and access control model is proposed to improve the transmission performance of the CIIoT while guaranteeing the CSS’s detection probability and controlling the interference to the PU. This model is optimized to maximize the total throughput of IIoT in each frame by jointly optimizing sensing time, the number of sensing nodes and the transmit power for each node under the constraints of the minimum detection probability, the total power control, the interference control, and the minimum rate for each node. The optimization problem is solved by the joint optimization of spectrum sensing and access control. A simultaneous CSS and access control model is also proposed to increase the communication time by using one time slot to perform CSS and access control simultaneously. The simulation results show that there exist optimal sensing and control parameters to maximize the total throughput of CIIoT

Security requirement management for cloud-assisted and internet of things⇔enabled smart city

The world is rapidly changing with the advance of information technology. The expansion of the Internet of Things (IoT) is a huge step in the development of the smart city. The IoT consists of connected devices that transfer information. The IoT architecture permits on-demand services to a public pool of resources. Cloud computing plays a vital role in developing IoT-enabled smart applications. The integration of cloud computing enhances the offering of distributed resources in the smart city. Improper management of security requirements of cloud-assisted IoT systems can bring about risks to availability, security, performance, confidentiality, and privacy. The key reason for cloud- and IoT-enabled smart city application failure is improper security practices at the early stages of development. This article proposes a framework to collect security requirements during the initial development phase of cloud-assisted IoT-enabled smart city applications. Its three-layered architecture includes privacy preserved stakeholder analysis (PPSA), security requirement modeling and validation (SRMV), and secure cloud-assistance (SCA). A case study highlights the applicability and effectiveness of the proposed framework. A hybrid survey enables the identification and evaluation of significant challenges

IoT-Enabled Big Data Analytics Architecture for Multimedia Data Communications

The present spreading out of the Internet of Things (IoT) originated the realization of millions of IoT devices connected to the Internet. With the increase of allied devices, the gigantic multimedia big data (MMBD) vision is also gaining eminence and has been broadly acknowledged. MMBD management offers computation, exploration, storage, and control to resolve the QoS issues for multimedia data communications. However, it becomes challenging for multimedia systems to tackle the diverse multimedia-enabled IoT settings including healthcare, traffic videos, automation, society parking images, and surveillance that produce a massive amount of big multimedia data to be processed and analyzed efficiently. There are several challenges in the existing structural design of the IoT-enabled data management systems to handle MMBD including high-volume storage and processing of data, data heterogeneity due to various multimedia sources, and intelligent decision-making. In this article, an architecture is proposed to process and store MMBD efficiently in an IoT-enabled environment. The proposed architecture is a layered architecture integrated with a parallel and distributed module to accomplish big data analytics for multimedia data. A preprocessing module is also integrated with the proposed architecture to prepare the MMBD and speed up the processing mechanism. The proposed system is realized and experimentally tested using real-time multimedia big data sets from athentic sources that discloses the effectiveness of the proposed architecture

Economic appraisal of offshore fisheries: A study on trawl fishing operations in Pakistan

This study attempts to show the effects and relative contributions of the different fisheries factors affecting the revenues for a sample of commercial offshore trawling vessels operating in the Exclusive Economic Zone (EEZ) of Pakistan. In this study, the level of relative contribution of each determinant is estimated by using the Standard Multiple Linear regression (SMLR) with standardized regression coefficients and correlation methods. The data were collected through the survey questionnaire and direct interviews with the boat owners and fishermen. The estimated standard beta regression coefficient values for the catch (ß1=0.253), horsepower (ß2=0.26), fishing days at sea (ß3=0.316) and skipper or captain’s fishing experience (ß4=0.32), respectively. Similarly, the catch contributes (R2=17.7 %), horsepower (R2=18.2 %), fishing days (R2=22.5 %) and skipper (R2=23 %) on the revenue. Moreover, the correlation values for the catch (r(Revenue, Catch) = 0.70), horsepower (r(Revenue, Horsepower) = 0.698), fishing days (r(Revenue, Fishing Days) = 0.713) and skipper (r(Revenue, Skipper)= 0.718) indicates the strong positive relationship of each variable on the revenue. In conclusion, the skipper fishing experience and individual skills have a very strong influence on the maximizing of total earnings of trawling vessels

Coupling Relationship of Leaf Economic and Hydraulic Traits of <i>Alhagi sparsifolia</i> Shap. in a Hyper-Arid Desert Ecosystem

In this study, Alhagisparsifolia Shap. was used to test the hypothesis that leaf economic and hydraulic traits are coupled in plants in a hyper-arid region. Five economic traits and six hydraulic traits were examined to explore the relationship. Results showed that the stomatal density (SD) on both surfaces was coupled with maximum stomatal conductance to water vapor (gwmax) and leaf tissue density (TD). SD on adaxial surface (SDaba) was significantly positively related to vein density (VD) but negatively related to leaf thickness (LT) and stomatal length on adaxial surface (SLada). Nitrogen concentration based on mass (Nmass) was significantly negatively correlated with leaf mass per area (LMA), LT, and VD, whereas nitrogen concentration based on area (Narea) was significantly positively related to LMA and TD. Mean annual precipitation (MAP) contributed the most to the changes in LT and stomatal length (SL). Soil salt contributed the most to TD, SD, and gwmax. Soli nutrients influenced the most of LMA and VD. Mean annual temperature contributed the most to Nmass and Narea. In conclusion, the economics of leaves coupled with their hydraulic traits provides an economical and efficient strategy to adapt to the harsh environment in hyper-arid regions.Instituto de Fisiología Vegeta