bluuebunny/crossref_metadata_2025_split huggingface dataset:
Pineapple Leaves Juice Characterization for Microbial Growth Inhibition
Authors: Ya’acob A , Zainol N , Mohd Ridza P N Y , Mortan S H 
Published: March 2021

Abstract
Since pineapple leaves are one of the lavishly organic waste materials which contain phenolic compounds with antimicrobial properties, it was desired to gain deeper knowledge on its potential as microbial growth inhibitor (MGI). In this research, the pineapple leaves were extracted using an electrical sugarcane pressing machine to acquire the pineapple leaves juice (PLJ). The PLJ was autoclaved at 121 °C for 15 min. Ultra-high-performance liquid chromatography-quadrupole time of flight mass spectroscopy (UPLC-QTOF-MS) analysis was used to examined the existence of different bioactive components in PLJ. Based on the molecular mass and its fragmentation pattern, the identification of the phenolic compounds was confirmed. From the characterization of PLJ using UPLC-QTOF-MS, seven types of the phenolic compound has been identified namely Octahydrocurcumin, Meliadanoside A, Kukoamine A, Stilbostemin D, Agrimol C, Feralolide and Methyl-5-O-caffeoylquinate. The effect of microbial inhibition time on the colony forming unit (CFU) between the mixtures of PLJ and microbe was studied. CFU/mL decreased with increasing microbial inhibition time from 0 to 1.50 h. Meanwhile, CFU/mL was increased again after increasing the microbial inhibition period from 1.50 to 3 h. This suggests that 1.50 h was necessary for the process of microbial growth inhibition. This research shows that pineapple leaves could be manipulated and used as an MGI agent as useful sources of natural products. Furthermore, it can become one of the cheaper and greener MGI alternatives compared to available synthetic antimicrobial agent.
Dual-modification of sago starch by gamma irradiation and annealing
Authors: Lee J-S , Jahurul MHA , Fong S-L 
Published: March 2021

Abstract
Similar to other native starch, native sago starch found limited industrial applications due to its poor functional properties. Various modification methods may be employed to improve the properties, however, owing to the health concerns over chemical modification, this study was designed to modify sago starch using combination of two physical techniques – gamma irradiation and annealing (ANN). Native sago starch was first subjected to four doses of gamma radiation (5, 10, 25, 50 kGy) followed by annealing treatment. Measurement of pH, FTIR (Fourier Transform Infrared) analysis, characterization of pasting properties and thermal properties of the samples were performed. Irradiated samples contained shorter starch chains with higher mobility to effectively participate in the molecular realignment during ANN, resulted higher crystalline perfection and hence higher gelatinization temperature. The interactions and re-association of starch chains during ANN exerted significant change to the pH and functional properties of irradiated sago starch. Combining gamma irradiation at 5 and 10 kGy with annealing could potentially improve the heat and shear stability of sago starch. Dual-modification was also found able to improve the heat stability of sago starch without affecting the gelatinization enthalpy. These improved features can be beneficial to the sago-based food products that require high temperature processing.
Volatile composition of Perilla frutescens (L.) essential oil in Thai Binh Province, Vietnam extracted by microwave-assisted distillation method
Authors: Dat U T , Tran T K N , Tran T H , Pham Q L , Dinh T T T , Tran Q T 
Published: March 2021

AbstractThe chemical composition of essential oils (EOs) obtained fromPerilla frutescens (L.)(Lamiaceae) was determined. EOs of perilla was obtained by microwave-assisted extraction, giving the optimum content of 0.0941%. The composition of volatile constituents in the extracted oil was analyzed by mass chromatography (GC - MS), showing that the content of volatiles accounted for 97.58% of the total EOs content. Some major components in the obtained EOs were Perilla aldehyde 62.13%, Limonene (4.99%), Linalool (1.41%), Caryophyllene <E-> (6.72%), Humulene <a-> (1.11%), Caryophyllene oxide (2.45%), and Perilla alcohol (0.8%). Obtained perilla EOs may find the use as an alternative natural supplements in food, pharmaceutical and cosmetic products.
RETRACTED: Parameterized Comparison of Regularized Regression Models to Develop Models for Real Estate
Authors: Chaturvedi Ankur , Gupta Akshi , Rajpoot Vikram 
Published: March 2021

Abstract
This research paper has been made on the data of life expectancy. Data carries two sort of regression tasks in it; one is continuous feature (Life expectancy) while another one is discrete feature (Status). Life expectancy depends on many a thing such as alcohol consumption, polio, infant deaths, etc. Generally, in data there exists two models separately, but research has been made to implement both at once. Research goes in a manner that it also involves the comparison or models accuracy among linear, ridge, and lasso. Visualization, normalization, data cleaning, feature reduction, etc, is also performed so as to increase the accuracy. One always looks for less time to complete task with less workout. Ultimately, research successfully implemented both linear regression and logistic regression both at once with optimized model. It is also stating the importance of the ridge and lasso algorithms for optimization.
Exploratory and Predictive Analytics of User Preferences from Kaggle LEGO-Toys Datasets Using Spark ML
Authors: Bahad Pritika , Saxena Preeti , Kamal Raj 
Published: March 2021

AbstractApache Spark is an open-source distributed data processing framework. The paper presents a processing architecture for exploring and predicting user preferences using Apache Spark. The architecture is evaluated on LEGO-toys datasets of period 1949-2019 using the Spark Machine Learning (ML) algorithms. The large datasets analyzed consist of LEGO-toys parts, categories, themes and colour features. Spark ML algorithms are applied as (i) k-means analyses of clusters to identify commonalities in LEGO-toys themes and colours, (ii) classifications using the Support Vector Machines (SVMs), Naïve Bayes (NB) and Random Forest (RF) algorithms for theme-preference identification, and (iii) linear regression, decision tree regression, RF, and Gradient Boost for regression analyses to identify the colour-shift in user preferences. The paper elucidates the steps for analytics based on Spark. The results for exploratory and predictive analytics are presented. The evaluation metrics shows that the ensemble regression prediction is better when compared to other algorithms. The analytics give many interesting results. For example, LEGO company’s products have become more colourful (children preferences exhibiting colours spectral-shift and width), diversified and multifaceted over-the-time. The architecture helps in discovering future directions for the new designs in future LEGO products. The proposed architecture can be successfully employed in the related domain to predict product and user’s preferences.
Novel Algorithm for Image Classification Using Cross Deep Learning Technique
Authors: Kumar Jugnesh , Bedi Pradeep , Goyal S B , Shrivastava Ashish , Kumar Sunil 
Published: March 2021

Abstract
The basic concepts of training and the model structure of deep belief networks (DBNs) in deep analysis are studied to apply image recognition in the area of deep learning. Random propound is provided with the parameter in the fine-tuning stage and the randomly hidden layer eliminated to maintain unchanged weights. The results show that the layered DBN training system reduces training problems and training times significantly. In the small sample, the deep faith network has improved significantly after introducing the down sample and random dropdown and effectively alleviates the over-fitting phenomenon. Design a new Deep Learning Image Recognition and Classification Algorithm. Novel Algorithm for Image Classification Using Cross Deep Learning Technique.
A Design Approach for Identifying, Diagnosing and Controlling Soybean Diseases using CNN Based Computer Vision of the Leaves for Optimizing the Production
Authors: Kamal Raj , Tiwari Sadhna , Kolhe Savita , Deshpande Manojkumar Vilasrao 
Published: March 2021

Abstract
Leaves express the initial symptoms of many diseases in soybean crop. CNN based Computer-Vision (CV) of the leaf-images identifies, enables the diagnosing and controlling the soybean-crop diseases. All three actions need proactive maintenance and Operational Intelligence (OI) for the model used for the control measures. The OI optimizes production. The paper describes a new design approach. Proposed design deploys Computer-Vision (CV), Convolution Neural Network (CNN), Edge AI Computing and the IoT. The approach consists of four-stage framework. Four stages are (1) Cameras in a chain of two sets of four cameras, each at two plants. (2) Edge-AI computing unit for each pair of plants at the field. (3) Edge-Server, which serves multiple networked edge units, one each of the two, sets of cameras. The server also functions as gateway for Internet connectivity with Cloud services. (4) Cloud services for the OI, proactively control the updating CNN model feature metrics, CV functions and camera operations.
Fake News Detection Using Machine Learning Approaches
Authors: Khanam Z , Alwasel B N , Sirafi H , Rashid M 
Published: March 2021

Abstract
The fake news on social media and various other media is wide spreading and is a matter of serious concern due to its ability to cause a lot of social and national damage with destructive impacts. A lot of research is already focused on detecting it. This paper makes an analysis of the research related to fake news detection and explores the traditional machine learning models to choose the best, in order to create a model of a product with supervised machine learning algorithm, that can classify fake news as true or false, by using tools like python scikit-learn, NLP for textual analysis. This process will result in feature extraction and vectorization; we propose using Python scikit-learn library to perform tokenization and feature extraction of text data, because this library contains useful tools like Count Vectorizer and Tiff Vectorizer. Then, we will perform feature selection methods, to experiment and choose the best fit features to obtain the highest precision, according to confusion matrix results.
Fitness for Solving SMCP Using Evolutionary Algorithm
Authors: Gupta Neetu , Rana Ajay , Gupta Sumit 
Published: March 2021

Abstract
Search based software engineering is a paradigm with prime focus to apply search techniques and meta-heuristics for solving various software engineering open NP hard problems. Evolutionary meta-heuristics techniques are already proven to provide optimized solutions to other software engineering problems like automated software test data generation, project estimation, class responsibility assignment to name few. Software module clustering is such an open problem of software engineering that cannot be solved in definite manner. For a given module dependency graph of software system, there exists large number of possible partitions. Identifying a good partition to cluster all modules of software system is an exhaustive search that cannot be carried out in finite manner. With search based techniques applying evolutionary algorithms, an optimized solution can be identified with evaluating goodness of a given partitions. Identifying a fitness function that can direct the search towards optimal solution is very critical. This paper discusses various types of fitness applicable to solve software module clustering problem.
A Review on Swarm Intelligence Techniques in Automated Cryptanalysis of Classical Substitution Cipher
Authors: Jain Ashish , Vishwakarma Santosh Kumar , Sharma Prakash Chandra , Gupta Nirmal Kumar 
Published: March 2021

AbstractBetween the year 2006 and 2019, a considerable new and different swarm intelligence techniques have been presented in the literature for automated cryptanalysis of classical substitution cipher. This paper compares the performance of these new and different swarm intelligence techniques. Three main comparison measures are considered to assess the performance of presented swarm intelligence techniques: efficiency, effectiveness, and success rate. To the best of author knowledge first time this kind of review has been carried out. It is noteworthy that among the presented swarm intelligence techniques the performance of cuckoo search technique is best with respect to all the measures.
Energy Efficient WSN Clustering Using Cuckoo Search
Authors: Bhatt Devershi Pallavi , Sharma Yogesh Kumar , Sharma Anand 
Published: March 2021

Abstract
A comprehensive range of applications exists for Wireless Sensor Networks (WSNs) have in today’s world. To name a few they are used in monitoring environmental parameters, condition of infrastructures, and related geophysical processes near to real-time, water quality monitoring, air pollution monitoring, etc. Wireless sensors can be placed in places that are difficult to human intervention for impossible to reach with a physical wired system. These WSNs, are low in power and computation so constructing an energy-effective data collecting protocol is a demanding issue. This is because all sensor nodes are usually provided with limited power sources. The development of an energy-efficient algorithm is the need of the hour as replacing the energy sources for the sensor nodes is a tedious task. Various clustering protocols have been proposed earlier but they suffer unbalanced energy problems and are not efficient enough to cater to the needs of these WSNs. To deal with this challenge, an improved algorithm using cuckoo-based search clustering protocol has been proposed in this research paper.
A Critical Review on Nature Inspired Optimization Algorithms
Authors: Soni Vishnu , Sharma Abhay , Singh Vijander 
Published: March 2021

Abstract
Nature-inspired optimization algorithms turned out to be progressively more accepted in modern era, and the majority of these meta-heuristic algorithms, such as ‘Bat algorithm’, ‘Lion Algorithm’, ‘Particle swarm optimization’, ‘Water wave optimization algorithm’, ‘Elephant herding optimization algorithm’, ‘Optics inspired optimization algorithm’, ‘Cuckoo search’, ‘Flower algorithms’, ‘Genetic algorithms’, ‘Differential evolution’, ‘Harmony search’, ‘Simulated annealing’, and many more. This paper attempts to establish the most recent improvements concerning all main ‘Nature-Inspired Algorithms’ and it is strived to coat at least one hottest optimization problem related to these above-mentioned algorithms. Based on their outcomes it is established that there are a few considerable gaps between hypothesis and observe. On one side, nature-inspired algorithms for optimization are extremely successful and can acquire most favourable clarifications in a convincingly realistic time. On the other side, mathematical investigation of key characteristics of these algorithms is missing. It is extremely advantageous that researchers add on some impending into the neutrality of diverse nature-inspired algorithms and hence can obtain the dare to crack key difficulties that must be solved. The paper is extending extensively and intensifying the need to evaluate and review these various optimization algorithms. This paper is organized in a manner to provide complete description of each NIA starting from introduction then review of literature, analysis of various NIAs and finally conclusion.
Digital Transformation Trends and Innovation
Authors: Guarda Teresa , Balseca Joel , García Kevin , González Jairon , Yagual Fabian , Castillo-Beltran Hernán 
Published: March 2021

Abstract
Today, advances in technology bring changes to people’s lifestyles, and challenging organizations to accompany these advances. Organizational managers and those responsible for the Information Technology (IT) department must evaluate current trends in innovation, and determine how they can boost business, creating new business models, and promoting competitiveness. The objective of these paper is presented a group of technological advances aimed at being the latest digital trends and changing previous trends by producing improvements or updates, the same ones that streamline processes in the field of business intelligence (BI). In this survey paper, current trends in the Internet of Things (IoT) are described, as well as topics related to 5G mobile communication, WiFi 6 technologies, the transformation of the user’s technological experience, and Machine Learning (ML). In addition, the support in decision-making process will be analysed as these trends, both personally and at the business level.
Linear constrained combinatorial optimization on well-described sets
Authors: Pichugina Oksana , Koliechkina Liudmyla 
Published: March 2021

Abstract
A Horizontal method (LCCO-HM) for linear constrained combinatorial optimization (LCCO) on well-described combinatorial sets (CWDs) is offered. LCCO-HM utilizes an essential feature of the sets to solve linear unconstraint programs in polynomial time. The method belongs to the Branch and Bound group. It reduces a search domain to solutions of auxiliary unconstrained combinatorial problems and performs pruning branches based on properties of a linear function on CWDs. Applicability of LCCO-HM is justified for sets of multipermutations and partial multipermutations embedded in Euclidean space. LCCO-HM is illustrated by an example of solving a permutation-based linear optimization problem.
An Empirical Study on Lean Performance Parameters of Manufacturing Sector
Authors: Vijayvargy Lokesh , Gupta Srikant 
Published: March 2021

Abstract
Growth and GDP of any nation is depend on its in-house manufacturing capacity. Due to manufacturing many countries like: China, Japan, USA, and Korea are leading world in growth and employability. But due to low cost and govt. policy, India is one of the most lucrative option for manufacturing industry to prosper. This paper will help us in identification of lean performance parameters of manufacturing companies in India. Data were collected in form of questionnaire by mangers of 100 manufacturing firms to identify various lean performance parameters. Sample of manufacturing industry are like agriculture tools and equipment’s, Material handling equipment’s, Parts of various machines parts and construction machines and parts, bolts and nuts, etc are included in our research. The respondent has different position in organization like HR manager, operations manager, SCM manager, owner, director, etc. To identify parameters of lean performance, this study used statistical tools. The outcomes of the research work offer the important implications for the Indian manufacturing organizations to understand the factors affecting their performance.
A Preservation Technology Model for Deteriorating Items with Advertisement Dependent Demand and Partial Trade Credit
Authors: Rathore Himanshu , Sharma Rohit Shiv Ashish 
Published: March 2021

AbstractThis study presents a preservation technology model for deteriorating items with constant rate of deterioration. The requirements for the items are received as a function of advertisement frequency and selling price. The two types of trade credit is the main focus of this study. First one is provided by the supplier to the retailer and another one is the partial trade credit offered by the retailer to the customer. The optimal policy is presented to earn maximum profit with optimal values of selling price, advertisement frequency, preservation technology and total cycle length. The study is numerically verified with sensitivity analysis to check the sensitivity of the model.
Comparison of Pairwise Similarity Distance Methods for Effective Hashing
Authors: Öztürk Ş 
Published: March 2021

Abstract
Content-based image retrieval (CBIR) methods search for points with the most similar content to query features from within a large dataset. The most notable approach for this purpose is an approximate nearest neighbor (ANN) searching. The main properties expected from a retrieval system can be listed as follows; low storage requirement, high retrieve speed, and high average precision. Hashing, which can generate discriminative and low-dimensional binary codes, is one of today’s most effective ANN searching methods. Although there are various hashing approaches in the literature, almost all hashing approaches consist of low-dimension feature representation and binarization sections. This study focuses on the low-dimension feature representation. Hand-crafted or deep learning based approaches are used for feature extraction in hashing methods. These features are the main components that affect the performance in creating binary codes. Contrastive loss is often used in the literature to update the learnable parameters of these feature extraction algorithms. The distance parameter of these data points critical to calculating contrastive loss. In this study, contrastive loss performance is tested using five different distance methods (Euclidean, Manhattan, Cosine, Minkowski, Chebyshev) for more effective feature representation. Retrieval performance is tested using low-dimensional feature vectors produced by these methods in MNIST and CIFAR-10 datasets. It is thought that the information obtained from this study is very useful for new researchers.
Talk about Pop Muzik: Discussion of Enrico Terrone, ‘Listening to Other Minds: A Phenomenology of Pop Songs’,BJA60 (2020), 435–453
Authors: Wiltsher Nick 
Published: March 2021

AbstractIn ‘Listening to Other Minds’, Enrico Terrone provides an account of the mental activity in which we ought to engage to appreciate pop music. He argues that we should ‘play a game of make-believe’ (p. 452) in which we imagine that we can ‘hear … the mind’ of a fictional character (pp. 452, 440). We should use this ability to grasp the thoughts and feelings that the mind contains, and thus undertake ‘exploration’ of the character’s ‘inner life’ (p. 445). This article argues, first, that only a simplified version of the account is plausible; second, that its plausibility as a general account of pop music depends on a dubious conception of the ‘paradigm cases’ (p. 449); third, that its desirability as an account of a narrower range of cases is questionable; and, fourth, that it is motivated by unsuitable assumptions about representation in pop.
Structure-based drug repurposing against COVID-19 and emerging infectious diseases: methods, resources and discoveries
Authors: Masoudi-Sobhanzadeh Yosef , Salemi Aysan , Pourseif Mohammad M , Jafari Behzad , Omidi Yadollah , Masoudi-Nejad Ali 
Published: March 2021

AbstractTo attain promising pharmacotherapies, researchers have applied drug repurposing (DR) techniques to discover the candidate medicines to combat the coronavirus disease 2019 (COVID-19) outbreak. Although many DR approaches have been introduced for treating different diseases, only structure-based DR (SBDR) methods can be employed as the first therapeutic option against the COVID-19 pandemic because they rely on the rudimentary information about the diseases such as the sequence of the severe acute respiratory syndrome coronavirus 2 genome. Hence, to try out new treatments for the disease, the first attempts have been made based on the SBDR methods which seem to be among the proper choices for discovering the potential medications against the emerging and re-emerging infectious diseases. Given the importance of SBDR approaches, in the present review, well-known SBDR methods are summarized, and their merits are investigated. Then, the databases and software applications, utilized for repurposing the drugs against COVID-19, are introduced. Besides, the identified drugs are categorized based on their targets. Finally, a comparison is made between the SBDR approaches and other DR methods, and some possible future directions are proposed.