Semantic Information Extraction from Multi-Corpora Using Deep Learning

Information extraction plays a vital role in natural language processing, to extract named entities and events from unstructured data. Due to the exponential data growth in the agricultural sector, extracting significant information has become a challenging task. Though existing deep learning-based techniques have been applied in smart agriculture for crop cultivation, crop disease detection, weed removal, and yield production, still it is difficult to find the semantics between extracted information due to unswerving effects of weather, soil, pest, and fertilizer data. This paper consists of two parts. An initial phase, which proposes a data preprocessing technique for removal of ambiguity in input corpora, and the second phase proposes a novel deep learning-based long short-term memory with rectification in Adam optimizer and multilayer perceptron to find agricultural-based named entity recognition, events, and relations between them. The proposed algorithm has been trained and tested on four input corpora i.e., agriculture, weather, soil, and pest & fertilizers. The experimental results have been compared with existing techniques and it was observed that the proposed algorithm outperforms Weighted-SOM, LSTM+RAO, PLR-DBN, KNN, and Naïve Bayes on standard parameters like accuracy, sensitivity, and specificity.     

Blockchain Assisted Intrusion Detection System Using Differential Flower Pollination Model

Cyberattacks are developing gradually sophisticated, requiring effective intrusion detection systems (IDSs) for monitoring computer resources and creating reports on anomalous or suspicious actions. With the popularity of Internet of Things (IoT) technology, the security of IoT networks is developing a vital problem. Because of the huge number and varied kinds of IoT devices, it can be challenging task for protecting the IoT framework utilizing a typical IDS. The typical IDSs have their restrictions once executed to IoT networks because of resource constraints and complexity. Therefore, this paper presents a new Blockchain Assisted Intrusion Detection System using Differential Flower Pollination with Deep Learning (BAIDS-DFPDL) model in IoT Environment. The presented BAIDS-DFPDL model mainly focuses on the identification and classification of intrusions in the IoT environment. To accomplish this, the presented BAIDS-DFPDL model follows blockchain (BC) technology for effective and secure data transmission among the agents. Besides, the presented BAIDS-DFPDL model designs Differential Flower Pollination based feature selection (DFPFS) technique to elect features. Finally, sailfish optimization (SFO) with Restricted Boltzmann Machine (RBM) model is applied for effectual recognition of intrusions. The simulation results on benchmark dataset exhibit the enhanced performance of the BAIDS-DFPDL model over other models on the recognition of intrusions.     

Theoretical Investigations of Magnetic Properties and Mechanical Stability of Quaternary Heusler Compounds FeYCrZ (Z = Al,Ga, Ge,and Si): a Spin Gapless Semiconductor

Journal of Superconductivity and Novel Magnetism - In this work, full potential linearized augmented plane wave (FP-LAPW) method is used to determine the electronic, magnetic, and mechanical...     

Magnetocaloric Properties of the Perovskite GdCuO3: Monte Carlo Simulation

Journal of Superconductivity and Novel Magnetism - This work aims to examine the magnetocaloric properties of the Perovskite GdCuO3, such as the total magnetization, the susceptibility, and the...     

Effect of Various Surfactants on the Viscosity,Thermal and Electrical Conductivity of Graphene Nanoplatelets Nanofluid

International Journal of Thermophysics - The paper describes the metrological characterization of the highly stable Pt-40%Rh/Pt-6%Rh thermocouples to determine their reference function in the...     

Effect of the substituent group in p-X-benzylidenemalononitrile on the optical and the electrical parameters of π-conjugated polymers based devices

Journal of Materials Science: Materials in Electronics - The effects of the substituent in p-substituent-benzylidenemalononitrile small molecules have been investigated. We found that the...     

Structural,electrical and magnetic properties of perovskite La0.4Sr0.6MnO3 prepared by mechanochemical synthesis technique

Journal of Materials Science: Materials in Electronics - Lanthanum strontium manganite (La0.4Sr0.6MnO3) of perovskite structure was successfully produced by mechanochemical synthesis method....     

Manufacturing Demonstration of Automotive Seat Backrest Using Sheet Molding Compound and Overmolding with Continuous Reinforcement

Applied Composite Materials - Sheet molding compounds (SMCs) are increasingly used in high-volume automotive applications for lightweighting and as a cost-neutral or low-cost solution to incumbent...     

Photocatalytic activity and radiation-attenuation ability of copper ions surface-doped dysprosium oxide

Journal of Materials Science: Materials in Electronics - Herein, Cu ions surface-doped Dy2O3 photocatalyst (Cu–Dy2O3) is synthesized utilizing hydrothermal technique to degrade methyl violet...     

Er3+/Yb3+ ions in a novel zinc borate glass: spectroscopic and physical properties

Journal of Materials Science: Materials in Electronics - Novel zinc borate glass doped with mixed rare-earth ions such as erbium (Er3+) and ytterbium (Yb3+) ions were prepared by conventional melt...