bluuebunny/crossref_metadata_2025_split huggingface dataset:
Disease detection in rice leaves using transfer learning techniques
Authors: Kathiresan Gugan , Anirudh M , Nagharjun M , Karthik R 
Published: June 2021

Abstract
Recent development in deep learning techniques have had a massive impact in the field of agricultural disease detection. The negative impact of pest and bacterial diseases to rice plants are well known, and for regions where rice is staple, this is issue carries a lot of weight. This work proposes a high accuracy, transfer learned model that can provide a mobile solution for farmers and agricultural organizations to detect rice leaf diseases at hand. This study also utilizes a generative adversarial network to balance the number of disease samples. We compare our model to other transfer learning architectures as well. The presented model tested on a GAN augmented dataset, achieves an average cross validation accuracy of 98.79% outperforming paradigm classification architectures. The model is also compared on 3 different datasets, without the GAN augmentation, establishing benchmark performance of 98.38% average accuracy.
RETRACTED: An Adaptive Fuzzy System based Insider Attacks detection with Group Key management for Wireless Ad-Hoc Network
Authors: Chellappan Sivakumar , Nalini C 
Published: June 2021

Abstract
Ad-hoc networking of wireless devices permits the establishment of contact links on the taking part hubs without pre-arranged infrastructure. In wireless ad hoc networks, participatorynodestypicallyengageingroupcommunicationssuchasbroadcast, multi-accessor relay functions. Protecting these communications is a challenging task, as internal attackers may secretly cause damage to the network, which is difficult to predict. Internal attackers can affect the sensors negatively and may disrupt the network by dropping sensitive packets or modifying packet information maliciously. Inside attackers are capable of causing many types of activities as well as passive attacks. Attackers located at a critical point in the network can significantly affect network performance. Insider attackers conceal themselves by marking themselves as legitimate nodes. Therefore, it becomes hard for the system to differentiate dishonest nodes from honest nodes. So as to make the communication among the network more secure, an adaptive fuzzy system that uses CSO algorithm for selecting optimal membership function has been proposed. In this approach, the group key is distributed by the cluster head to the cluster members. This makes sure that only the nodes with the same group key can speak with one another. Implementing appropriate group-key management in the system that focuses on internal attacks identifies the dishonest nodes which can be then eliminated through some other security measures. This ensures the absence of the dishonest nodes inside the network. The results have been simulated and the execution of the proposed approach has been contrasted with the tradition FS approach. The results have shown the presented approach has performed better than the traditional approach.
Acceleration of Web Interface Generation Through Voice Commands
Authors: Rattan Himanshu , Agarwal Shantnu , Poovammal E 
Published: June 2021

Abstract
Advancement in technology is useless unless it is accompanied by a greater access to technology. Be it lack of technical knowledge or a dearth of ability to comprehend or reason, technology must allow low-code or no-code access to all. This work tries to bridge this accessibility barrier by bringing Natural Language Processing (NLP) to the website development process. using NLP and Azure Cognitive Services, we will be able to understand what elements the user wants to add to the website by extracting the required information. Dynamic generation of web pages using voice commands from the user will not only increase low-code accessibility to naive users, but will also speed up work for experienced users by allowing them to easily add elements to the website.
An Efficient Approach to Microarray Data Classification using Elastic Net Feature Selection, SVM and RF
Authors: Koushik C , Shreyas Madhav A V , Singh Rabindra Kumar 
Published: June 2021

AbstractDNA Microarray technology forms an integral part of the bioinformatics world and provides researchers with ability to monitor a large amount of gene expressions simultaneously. The analysis of this data proves extremely beneficial in the detection of several diseases. Classification of the gene expression data obtained from microarrays is an imperative step in providing further information on the types of possible diseases present. Several classification models for microarray data have been proposed, yielding considerable results in terms of accuracy and execution time. This paper demonstrates the implementation of a microarray data classification system using Elastic Net for feature selection on two classification mechanisms. The classification of the data has been carried out using SVM and Random Forest Classifier methods. The implemented system using Elastic Net and SVM exhibits a much better performance in terms of accuracy and execution time than most of the existing systems.
Implementation of robotics and its impact on sustainable banking: A futuristic study
Authors: Choubey A. , Sharma M. 
Published: June 2021

Abstract
Nowadays we are standing on the edge of technological rebellion which would vitally modify the way we live and work. This paper focuses on understanding the changing economic and financial situations with advent of modern technology. Banking sector was analyzed with the help of secondary data collected through several reports, journals, websites and past studies. The study helped me attain insight into probable impacts of implementation of robotics in banking sector and the way it would help to ease the bank’s transactions, maintain sustainability throughout and enhance profitability. While in the field of manufacturing, repetitive procedure have been subjected to automation involving robotics and other technologies, since industrial revolution, robotics has now opened doors to other sectors of the world like banking and finance. This study has been undertaken to understand the impact of RPA (Robotics process automation) technology on banks and its future in a developing nation like India. The research has emphasized on alterations made by changing technology specially robotics in the banking industry.
Improving Throughput in Sub – Nyquist Spectrum Sensing using a Cluster Model for Cognitive Radio Networks
Authors: Bhuvaneswari Vinithkumar GK P.T.V , Gloria A 
Published: June 2021

Abstract
The underutilization of radio spectrum has led to the downfall of Fixed Spectrum Allocation (FSA) since mobile frequency bands are getting overloaded due to rise of users all across the world. These drawbacks could be overcome by a concept called Dynamic Spectrum Allocation (DSA) implemented by an intelligent form of communication termed as Cognitive Radio Networks (CRN). CRN finds its applications adopted in military for battlefield surveillance, medical areas for 24 × 7 patient monitoring and also in TV white spaces. CRN involves SS in an efficient manner to deploy mobile users in TV frequency bands in a wider range through Wideband SS (WSS). WSS provides omnipresent spectrum monitoring for detecting spectral holes in the unutilized TV spectrum. The concept of Sub-Nyquist Sensing in WSS has several advantages in data sampling improving a plethora of application throughputs. The cluster approach in CRN has improved the base station crisis in load handling, throughput performance. In this paper, we focus on improving the traffic Quality of service (QoS) of the users in WSS by employing a cluster CRN network and applying Nyquist Sensing (NS) and Sub-Nyquist sensing (SNS) to find out the significance of SNS in the cluster network.
ARP Poisoning Detection and Prevention using Scapy
Authors: Majumdar Aayush , Raj Shruti , Subbulakshmi T. 
Published: June 2021

Abstract
Address Resolution Protocol is a protocol associated with mapping a given IP address with the associated MAC address. ARP Poisoning or Spoofing attack is an attack which carried out over a Local Area Network. In an ARP Poisoning/Spoofing attack malicious ARP Packets are sent to a default gateway on LAN with intent to change the IP address-MAC address pairings in the ARP cache table. ARP Spoof attack tool has been developed with a script written in Python Programming language using scapy library. A detection algorithm has been proposed for detecting the above generated ARP Poisoning attack (or any ARP Poisoning attack in general) and implemented the same using a python script with scapy library. The detection algorithm is based on analyzing the real MAC Address and response MAC Address of the ARP Packet sniffed for any discrepancies. Lastly, a prevention mechanism is proposed and implemented for ARP Poisoning attacks by implementing static entries in ARP table.
Feature Fusion Based Orange and Banana Fruit Quality Analysis with Textural Image Processing
Authors: Apte S K , Patavardhan P P 
Published: June 2021

Abstract
A nondestructive approach for fruit quality assessment is outlined. Fruit Quality is determined by external and internal features. Only external features won’t help to determine the overall quality of a fruit. Color images and x-ray image of Orange and Banana fruit are captured. With Image processing tool features like Color histogram, color moments and Gabor filter are extracted for external as well as internal quality assessment. Total 18 color features and 27 textural features are extracted for analysis. Sample color images and x-ray image of Orange and Banana fruit are trained and tested. Fusion algorithm is developed to detect the fruit class. Fruits are divided into three classes’ viz. 50% defected, 90% defected and Normal class. SVM and ANN classifier are elected to determine the fruit quality. This information is used to determine degradation level of a fruit. The experimental result showed that % accuracy with SVM classifier is more than ANN classifier.
SME 4.0: Machine learning framework for real-time machine health monitoring system
Authors: Velmurugan K , Venkumar P , Sudhakara Pandian R 
Published: June 2021

Abstract
Over the past ten years, manufacturers and consumers have become increasingly interested in the applications of smart, sustainable, and autonomous systems in the industry and in everyday life. Due to the recent industrial revolution (Industry 4.0), most of the existing Small and Medium-sized Enterprises (SMEs) also want to adapt their work environment into the smart system by the applications of these technologies such as the Industrial Internet of Things (IIoT), Artificial Intelligence (AI) techniques, Machine Learning Algorithm (MLA), Internet Communication Technology (ICT), and Cyber-Physical System (CPS). Because they are very much interested in maximizing productivity, machine availability, reliability, and customer satisfaction in this competitive industrial world. This research study particularly focuses on the Predictive Maintenance (PdM) activity of critical machines and their components in the SME based on the maintenance history dataset through the application of the supervised machine learning algorithm such as Logistics Regression (LR) and K-Means (K-Nearest Neighbor) approaches. In accordance, the real-time case study is presented in SMEs in the southern region of Tamil Nadu, India with two-phase activities. Initially, the optimal failure rate of the machines is predicted by the utilization of LR trained models. Then trigger the man-machine communication and suitable decision-making process of service and maintenance activity through the application of the K-Mean approaches. The main objective of this research study is to organize the smart PdM activity of the smart factory systems in SMEs with the application of MLA based on the real-time maintenance history dataset.
Android Malware Detection and Classification using LOFO Feature Selection and Tree-based Models
Authors: Roseline S Abijah , Geetha S 
Published: June 2021

Abstract
Cybersecurity threats on mobile devices are also growing substantially with the subsequent rise in the usage of smartphones and mobile applications. Cybercriminals inevitably have expanded their malicious operations to Google’s Android mobile operating system. Due to the limitations of traditional signature-based approaches and the constant evolution of new malware, current malware detection systems are turned to be empowered by intelligent machine learning models. In this aspect, malware defense techniques strive to integrate data science and cybersecurity. This paper presents an Android malware detection system that incorporates the Leave One Feature Out (LOFO) approach and uses tree-based learning models to classify malware applications based on the top selected features. The experimental evaluation is conducted on the DREBIN dataset to demonstrate the efficacy of the proposed method. The prediction accuracy of the XGBoost classifier is observed to outperform other tree-based models, but with higher computational costs.
Fabrication of Austenitic ODS 316L Stainless Steel with Arc Plasma Sintering Method
Authors: Shabrina N , Haerani D N , Salam R , Sujatno A , Sugeng B , Bandriyana B , Silalahi M 
Published: June 2021

AbstractAustenitic ODS (Oxide Dispersion Strengthened) 316L steel was fabricated using 316L SS (Stainless Steel) pre-alloyed powder and yttria powders by mechanical milling with APS (Arc Plasma Sintering) method. The powders were milled in High Milling Shaker at 1000 rpm in rotational speed for 10 hours at 10 BPR (Ball Powder Ratio). As milled powders were pressed at 20 Ton and sintered with APS for 0 to 12 minutes at 70 A. The samples were analysed using X-Ray Diffraction (XRD), Microscope Optic (MO) with micro-hardness tester and Scanning Electron Microscope – Energy Dispersive X-Ray Spectroscope (SEM-EDS). The results of phase and microstructure showed the austenitic grade of the samples. The sintering time contributed to the microstructure of the samples. At room temperature, the mechanical properties of ODS 316L SS was higher than conventional 316L SS.
Notes on Espenson Method for Determining Catalyzed Reaction Fitting-Constants
Authors: Patiha  , Saraswati T E , Wahyuningsih S , Firdaus M , Masykur A 
Published: June 2021

Abstract
Espenson states that many catalyzed reactions can be represented in a unified equation. Based on the basic assumption that the catalyzed reaction occurs in 2 reaction mechanisms (zeroth-order and first-order), he also introduced a time-lag-based equation for determining the fitting-constants, q and κ, using a combination of outset and near-end data. A new method, compiled based on the outset and near-end data separately, has been introduced. This study aims to demonstrate the inaccuracy of Espenson Method. The study was carried out theoretically, and the inaccuracies were tested by applying both methods to the literature data. Assessment is based on the kobs
and κ values. The results showed that Espenson Method does not always use basic assumptions. The average percentage difference in the mean value of kobs
, relative to the new method was 14.24% and for κ was 52.23%. The q value is not constant; q is not the maximum velocity. Doubling [Cat.] will double q, but kobs
and κ are the same. Therefore, q is simply a maximum relative velocity. The real constant is kobs
as the observed catalyst constant.
The chemical kinetics studies of methylene blue degradation treated in dielectric barrier discharge (DBD) plasma with and without TiO2 photocatalyst
Authors: Zuhaela I A , Cahyani M R , Saraswati T E , Kusumandari K. , Anwar M. , Suselo Y H , Ismayenti L , Rahardjo S B 
Published: June 2021

Abstract
The presence of textile industry color-wastewater in a toxic environment needs to be treated to reduce the toxicity. The wastewater treatment can be performed by the combination technique of photocatalysis and plasma ozonation. The kinetics studies of these techniques are interesting to be studied to investigate which process is more dominant when playing a role in pollutant degradation. In this study, the kinetic study of a combination technique of plasma ozonation and photocatalysis using TiO2 was analyzed using a methylene blue (MB) dye pollutant model. The study was compared to the kinetic study of the plasma ozonation alone. The plasma ozonation was generated by a dielectric barrier discharge (DBD) plasma reactor, which used two electrodes separated by a dielectric barrier of quartz glass, which high voltage 5 kV (AC power supply) and an airflow rate of 1 L/min. The results of the experiment show that MB concentration after DBD treatment decreased. The chemical kinetics test is able to determine the reaction order, in which the treatment process using DBD plasma with photocatalyst TiO2 follows the second order. In contrast, those using DBD treatment without TiO2 follows the first order.
Effect of Reaction Time on the rGO-CoS Composite Structural Properties
Authors: Fathoni H I , Kawuni K R , Hanif Q A , Wahyuningsih S , Supriyanto A , Harjana  , Ramelan A H 
Published: June 2021

Abstract
In this research, the rGO-CoS composites were successfully synthesized from GO and the Cobalt and Sulfur precursors via solvothermal reaction. The GO material was obtained by Hummer method reaction synthesis. In term of rGO synthesis, reaction time is one of important factors that determine the reduction process and the resulting material properties. Therefore, we studied the influence of reaction time to the chemical properties of the obtained composites by varying i.e 12, 24, 36 and 48 hours of reaction time. The characterizations were carried out using Scanning Electron Microscope (SEM), X-Ray Diffraction (XRD), and Fourier Transform Infra-Red (FTIR). Morphological observation of rGo-CoS shows that rGO-CoS are has many thin layers, rougher, and wider surface. From the XRD result, GO shows shift of peak diffractogram to 2θ 9.56 ° with interplanar space 9.24 Á and rGO-CoS 36 shows several peaks at 2θ 30.84, 35.47, 47.54, 54.94°. The GO spectrum pattern shows specific functional group, OH groups (hydroxyl), carboxyl stretching vibration C=O (-COOH), C=C of unoxidized graphite consisting aromatic rings, and epoxy group vibration C-O(-C), while rGO and rGO-CoS shows decrease in intensity of these bands. It concluded that the rGO-CoS 36 (hours) is considered as the most optimum reduction reaction time.
Nanocellulose Obtained from Biomass as Advance Adsorbent for Methylene Blue and Crystal Violet
Authors: Mahardiani L , Ilfama R , Saputro S , Pranolo S H , Septianing P W 
Published: June 2021

Abstract
In this research, the ability of papaya tree trunks as nanocellulose adsorbent for dyes removal was conducted. The synthesis of nanocellulose is carried out in several steps, including isolation of cellulose using the extraction method, immersion with a variety of alkaline bases, namely KOH, KOH+NH3, and KOH+Na2CO3 and synthesis of nanocellulose by hydrolysis reaction. The adsorption process was carried out in batch method, by contacting the nanocellulose adsorbent with the dye, then filtered and observed the change in concentration using UV-Vis. Meanwhile, to observe the functional groups contained in the nanocellulose obtained from synthesis of papaya tree stems FTIR result was evaluated. The results showed that all cellulose-forming groups (CO, CH2, CH) were present in synthesis nanocellulose in accordance with those of the commercial. The synthesis results with variations in the alkaline base which the closest to commercial yield was the variation with the alkaline KOH base. In addition, determination of adsorption performance of ones is compared with performance of commercial nanocellulose.
Potential edible coating of pectin obtained from banana peel for fruit preservation
Authors: Mahardiani L , Larasati R , Susilowati E , Hastuti B , Azizah N L 
Published: June 2021

Abstract
Edible coating is one of the strategies to preserve food because it provides protection by covering the food with its coating. Pectin is one of the sources of edible coating which can be obtained from biomass. This research aims to (1) investigate the potential banana peel as the source edible coating, (2) characterize the characteristics of pectin obtained, and (3) evaluate the edible coating effect on tomato in different temperatures storage. This research used the experimental method in the laboratory. The making of edible coating solution used banana peel pectin was varied with concentration of 2% and 10% added the following additives, namely: CMC, glycerol, potassium sorbate, and stearate fatty acids. The edible coating solution was applied to tomatoes with variations of temperature storage. The results showed that: (1) banana peel has potential as source edible coating, (2) the characterizations of pectin are in accordance with standard of international pectin quality, and (3) in the room and cold temperature storage, the coating tomato is 10% better than that of 2% and without treatment seen from the shrinkage value of weight.
Techno Economic Analysis of Online and Closed Loop Transformer Oil Purification System for Offshore Oil and Gas Platform
Authors: Bahroinuddin M. , Sukirno  
Published: June 2021

Abstract
The focus of this research is to conduct techno-economic analysis for investment on purification system of transformer oil in offshore oil and gas industry. The challenges of the purification system are the design shall be appropriate for limited space area, no production disruption (works online), and conducted without discharging from the pipe / transformer system in accordance with regulations. The methodology of this research is conducting literature study and review to previous researches and using existing contract data and manufacturer’s offering which applied in oil & gas industry of Indonesia. The result is the most appropriate purification system is the combination of filtration – degassing & dehydration – filtration methods, 70 cm x 70 cm x 125 cm in dimension. The investment is good in economic value which indicated by IRR value 25.24%, NPV IDR 2,425,529,703, and payback period 4.35 years.
Adsorption of Metal Ion Manganese (Mn) in Sodium Silicate Solution using Chitosan
Authors: Febriana E , Pitriani P , Handayani M , Irawan J , Prasetyo A B , Sulistiyono E , Firdiyono F , Hendrawati  
Published: June 2021

Abstract
High purity Silica precipitate is very potential materials which can be utilized for many applications for high grade silicon, filler, photovoltaic, catalytic chemical, etc. Indonesia has abundant natural resources including quartz sand as raw materials for making high purity silica precipitate. Silica precipitate is made by roasting quartz sand forming sodium silicate solution. In order to obtain high purity silica precipitate, purification of sodium silicate solution is needed. In this study, purification of sodium silicate solution is performed by adsorption process of metal ion manganese (Mn) using chitosan as adsorbent. The experiment was done using batch technique. The variation of adsorption process was performed to get optimum conditions such as sample concentration, adsorbent mass, contact time, pH and temperature. The adsorption of metal ions of Mn was analyzed by atomic adsorption spectroscopy. The result of experiment shows that the best mass of chitosan for Mn adsorption process is 0.2 grams which gives percentage adsorption of 56.63%. The optimum contact time is 30 minutes, it results percentage adsorption of 52.5%. The optimum pH 3 gives adsorption percentage of 58.67%. And the best temperature for adsorption is at 60 °C with the percentage adsorption of 65.72%. Adsorption mechanism of metal ion Mn follow the isotherm model of langmuir and freundlich with the linearity value of R2 = 0.99 and 0.98 respectively.