Semantic Term-Term Coupling based Feature Enhancement of User Profiles in Recommendation Systems

Content-based recommender system is a subclass of information systems that recommends an item to the user based on its description. It suggests items such as news, documents, articles, webpages, journals, and more to users as per their inclination by comparing the key features of the items with key terms or features of user interest profiles. This paper proposes the new methodology using Non-IIDness based semantic term-term coupling from the content referred by users to enhance recommendation results. In the proposed methodology, the semantic relationship is analyzed by estimating the explicit and implicit relationship between terms. It associates terms that are semantically related in real world or are used inter-changeably such as synonyms. The underestimated features of user profiles have been enhanced after term-term relation analysis which results in improved similarity estimation of relevant items with the user profiles.The experimentation result proves that the proposed methodology improves the overall search and retrieval results as compared to the state-of-art algorithms.

A holistic overview on corn cob biochar: A mini-review

Corn cob is one of the agricultural waste materials subjected to improper burning, which creates pollution. It can be used for the production of green technologies for further applications. Carbonisation or slow pyrolysis could be promising alternative to burning. It has many applications, such as soil ameliorant, waste water treatment, carbon sequestration, composting, supercapacitor, fuel cell and biocomposites material. It motivated to investigate the suitability of corn cob as a potential material for biochar production and its application. The advanced form of analysis, such as thermogravimetric, scanning electron microscopy, surface area, Fourier transform infrared spectroscopy, nuclear magnetic resonance spectroscopy and Raman spectroscopy, is elaborated for in-depth knowledge of characteristics. The hypothesis is that if the available corn cob is used for biochar production, it will reduce the carbon dioxide (CO2) emission. On a global level, conversion of available corn cob into biochar is expected to reduce CO2 emission by 0.13 Gt per year. The reduction in CO2 emission also favours economy. If 1 tonne of biomass per year is converted into biochar, 0.82 tonnes of CO2 can be reduced per year and by considering the emission cost of Rs 1800 per tonne, the cost saving would be Rs 1476 per year. The presented mini-review article provides an outline of the state-of-art information on corn cob biochar and its novel application. It will be helpful to scientific domain to find new opportunities in biochar research and also the humanity will be benefitted due to reduction in greenhouse gases.

A Modified XG Boost Classifier Model for Detection of Seizures and Non-Seizures

Diagnosis of Epilepsy is immensely important but challenging process, especially while using traditional manual seizure detection methods with the help of neurologists or brain experts’ guidance which are time consuming. Thus, an automated classification method is require to quickly detect seizures and non-seizures. Therefore, a machine learning algorithm based on a modified XGboost classifier model is employed to detect seizures quickly and improve classification accuracy. A focal loss function is employed with traditional XGboost classifier model to minimize mismatch of training and testing samples and enhance efficiency of the classification model. Here, CHB-MIT SCALP Electroencephalography (EEG) dataset is utilized to test the proposed classification model. Here, data gathered for all 24 patients from CHB-MIT Database is used to analyze the performance of proposed classification model. Here, 2-class-seizure experimental results of proposed classification model are compared against several state-of-art-seizure classification models. Here, cross validation experiments determine nature of 2-class-seizure as the prediction is seizure or non-seizure. The metrics results for average sensitivity and average specificity are nearly 100%. The proposed model achieves improvement in terms of average sensitivity against the best traditional method as 0.05% and for average specificity as 1%. The proposed modified XGBoost classifier model outperforms all the state-of-art-seizure detection techniques in terms of average sensitivity, average specificity.

Data and Communication Infrastructure in Smart Grids

The communication infrastructure constitutes the key element in smart grids. There have been great advances to enhance the way data is communicated among the different smart grid applications. The aim of this chapter is to present the data communication part of the smart grid with some pioneering developments in this topic. A succinct review of the state of art projects to improve the communication link is presented. An illustrative simulation using LABVIEW is included with a proposed idea of introducing some newly technologies involved in the current and future generations of wireless communication systems.

Deep Learning based Tomato’s Ripe and Unripe Classification System

Effective productivity estimates of fresh produced crops are very essential for efficient farming, commercial planning, and logistical support. In the past ten years, machine learning (ML) algorithms have been widely used for grading and classification of agricultural products in agriculture sector. However, the precise and accurate assessment of the maturity level of tomatoes using ML algorithms is still a quite challenging to achieve due to these algorithms being reliant on hand crafted features. Hence, in this paper we propose a deep learning based tomato maturity grading system that helps to increase the accuracy and adaptability of maturity grading tasks with less amount of training data. The performance of proposed system is assessed on the real tomato datasets collected from the open fields using Nikon D3500 CCD camera. The proposed approach achieved an average maturity classification accuracy of 99.8 % which seems to be quite promising in comparison to the other state of art methods.

Logistic Map and Exponential Scaling Factor based Differential Evolution

Differential evolution (DE), an important evolutionary technique, enhances its parameters such as, initialization of population, mutation, crossover etc. to resolve realistic optimization issues. This work represents a modified differential evolution algorithm by using the idea of exponential scale factor and logistic map in order to address the slow convergence rate, and to keep a very good equilibrium linking exploration and exploitation. Modification is done in two ways: (i) Initialization of population and (ii) Scaling factor.The proposed algorithm is validated with the aid of a 13 different benchmark functions taking from the literature, also the outcomes are compared along with 7 different popular state of art algorithms. Further, performance of the modified algorithm is simulated on 3 realistic engineering problems. Also compared with 8 recent optimizer techniques. Again from number of function evaluations it is clear that the proposed algorithm converses more quickly than the other existing algorithms.