bluuebunny/crossref_metadata_2025_split huggingface dataset:
Applications of Autoencoders along with Deep Learning Techniques to generate valid molecules
Authors: Sesha Sai Aparna T , Anuradha T 
Published: November 2021

Abstract
From the moment of identifying the fundamental cause of an illness to its availability in the marketplace, it takes an average of 10 years and almost $2.6 billion dollars to develop a medication. We’re actually hunting for a needle in a haystack, which takes a lot of time, effort, and money. In a solution space of between 1030 and 10100 synthetically viable compounds, we’re seeking for the one molecule that can turn off a disease at the molecular level. The chemical solution space is just too large to adequately screen for the desired molecule. Only a small percentage of the synthetically viable compounds for wet lab research are stored in pharmaceutical chemical repositories. Computational de novo drug design can be used to explore this vast chemical space and develop previously undesigned compounds. Computational drug design can cut the amount of time spent in the discovery phase in half, resulting in a shorter time to market and lower drug prices. Deep learning and artificial intelligence (AI) have opened up new perspectives in cheminformatics, especially in molecules generative models. Recurrent neural networks (RNNs) trained with molecules in the SMILES text format, in particular, are very good at exploring the chemical space. Two baseline models were created for generating molecules, one of the model includes an encoder that takes SMILES as input and then develops a deep generative LSTM model which acts as a hidden layer and the output from layers acts as an input to the decoder. The other baseline model acts the same as the above-mentioned model but it includes latent space, it is simply a representation of compressed data that bring related data points closer together physically. To learn data properties and find simpler data representations for analysis, and weights which are obtained from the previous model to generate more efficient molecules. Then created a custom function to play with the temperature of the softmax activation function which creates a threshold value for the valid molecules to generate. This model enables us to produce new molecules through successful exploration.
Modelling the minislump spread of superplasticized PPC paste using Random forest, Decision tree and Multiple linear regression
Authors: Mrithula M , Bhavana Modepu , Janani S , Narayanan Vaishnavi , Rajesh C.B , Sathyan Dhanya 
Published: November 2021

Abstract
Workability is one of the key property of concrete which is governed by water cement ratio. In order to improve the workability of concrete without any variations in water cement ratio Superplasticizers(SPs) are added. Cement paste helps us to analyze the property of fresh concrete where the dispersion of cement particle is taken into account. SP’s Cement dispersive properties are governed by dosage and the family. Various dosages and families of SP are considered for estimating workability feature of cement paste which is picked for investigating on rheological properties through Mini slump spread diameter. The prime motive of this analysis includes measuring the workability of different superplasticizers by conducting a minislump test and hence modelling the flow rate of the superplasticized Portland Pozzolona Cement (PPC)paste using the application of random forest(RF), decision tree(DT) and multiple regression algorithms. Testing and training data for a model were 287 unique mixture compositions at a water by cement ratio was 0.37. This mixture was tested experimentally in a laboratory using four types of locally available PPC’s and of SP which can be broadly categorised in to four families. Amount of seven types of SP brands, water content, cement weight were the input parameters for the model and flow rate was the output parameter. The model’s predicted and experimentally measured values of flow speed were compared and the amount of deviation was recorded.
Optimal setting of interline power flow controller in deregulated power systems congestion management by using artificial intelligent controllers
Authors: Chaithanya K Krishna , Kumar G V Nagesh , Rafi Vempalle , Kumar B Sravan 
Published: November 2021

Abstract
In electricity market with a deregulation withmany times will become difficult for dispatchingpower, whichis obtained with thepower congestion in transmission lines due to flow of power in buses. To reduce the flow of power in buses and system loss in lines which are heavily loaded, toimproveloadability and system stability for which an Interline Power Flow Controller used. In this article, Disparity Utilization Factor for Line is utilized with the optimal tuning and placement with Gravitational Search Algorithm IPFC hinged transmission lines for power congestion. The rankingof transmission lines by line congestion in terms of relative DLUF. According to the IPFC placementwith the essentialminimum congested, weakline connected to the same bus in the congested distribution network. IPFC optimal Sizing is carried with Gravitational Search Algorithm. The multiobjective function is chosen for parameters tuning of IPFC. A suggested IEEE-30 bus test network is implemented by a graphical representation whichis considered in this study toreduce LUF in transmission lines with IPFC optimal placement. Bothactive power losses and reactive power losses in power systemare necessary to be minimized after every iterationof IPFC optimal tuning. The proposed tuning in this articlegives effectivenesswith the reduction in the values of power system with the objective functions.
Smart Villages: IoT Technology Based Transformation
Authors: Mudholkar Pankaj , Mudholkar Megha , Puneeth Kumar B S , Dankan Gowda V , Raju S. Srinivasulu 
Published: November 2021

Abstract
In recent years, large-scale urbanisation has been on the rise with cities being the hubs for growth, but rural business continues to play an important role in any country’s overall development. A recent report reveals that almost 69 percent of India’s population resides in their villages, accounting for almost 50 percent of the nation’s GDP. This agricultural region varies from small towns with less than 500 residents to small towns. There are also similar situations in western nations. Despite their economic contribution, smaller villages seem to earn fewer in terms of infrastructural expenditure. The major problems addressed in these small towns include shortage of adequate public transit, emergency care and limited knowledge on federal subsidies for rural areas. Our vision is to expand the Smart city to Smart village by allowing use of recent technical advances and giving more attention to the problems in rural areas. The approach in this chapter is to bring IoT technology to Villages by literally showing a network of linked sensors and knowledge dissemination devices, controlling energy use and ensuring infrastructure protection. It gives a wide-ranging vision of enhancing the standard of living in villages and encourages them to meet the essential needs of domestic villagers.
Design of MoS2/graphene heterostructure thin film sensors for high performance NO2 gas sensor applications
Authors: Sakthivel R , Geetha A , Anandh B A , Jagadeesan V , Ganesh A Shankar , Dineshkumar J 
Published: November 2021

Abstract
In this paper, we fabricate a large-area chemiresitive type MoS2/graphene films sensor is grown by spray pyrolysis technique. The prepared sensor films were characterization by XRD, SEM, TEM Raman and BET analysis. The synergistic effect between MoS2 and graphene through the CVD method produces such a hierarchical layer-by-layer assembly of the thin film structure. MoS2/graphene hybrid films not only show enhanced NO2 sensitivity compared to NO2 sensitivity alone. Graphene or MoS2 films, but they also exhibit characteristics of rapid response and strong reproducibility. Selectiveness and stability findings demonstrate the outstanding sensing properties of the MoS2 thin film sensor. The MoS2/G showed higher sensitivity (81%) towards NO2 gas at the concentration of 1000 ppm followed by graphene (22 %) and MoS2 (45 %) based sensors in sequence. The MoS2/G sensor also exhibits fast response (12 s) and recovery time (17 s) than other sensor samples. The concept of operation and sensing mechanism behind their impressive results has also been studied in depth. The effect of humidity on the performance of gas sensing was also discussed in the point of practical device applications.
Artificial Neural Network Controller Strategy for Improving DC Link Voltage of Grid Connected Hybrid PV-Wind Generation Systems
Authors: Maheswara Rao B. , Nagesh Kumar G.V. , Rafi Vempalle 
Published: November 2021

Abstract
This paper presents a power system, consisting of photovoltaic (PV) station and wind farm integrated by ac bus, connected to the grid. The load gets power from both the sources and maximum power is tracked by maximum power point techniques (MPPT) during any changes in the environment. The paper explores how MPPT techniques help power system in tracking power from PV and wind in the conditions of different solar irradiances and different wind speeds. This paper’s objective is to show the improvement in step response of dc link voltage by artificial neural network (ANN) controller. The control method significantly maintains constant grid voltage ensuring unity power factor even during climatic conditions variation. The whole system is simulated using matlab/simulink software and the results compare the proposed system with existing controller i.e., Proportional Integral (PI). The results show the efficient performance of ANN controller than PI controller.
A Reliable Low Power Multiplier Using Fixed Width Scalable Approximation
Authors: Stella K , Vinith T , Sriram K , Vignesh P 
Published: November 2021

Abstract
Recent Approximate computing is a change in perspective in energy-effective frameworks plan and activity, in light of the possibility that we are upsetting PC frameworks effectiveness by requesting a lot of precision from them. Curiously, enormous number of utilization areas, like DSP, insights, and AI. Surmised figuring is appropriate for proficient information handling and mistake strong applications, for example, sign and picture preparing, PC vision, AI, information mining and so forth Inexact registering circuits are considered as a promising answer for lessen the force utilization in inserted information preparing. This paper proposes a FPGA execution for a rough multiplier dependent on specific partial part-based truncation multiplier circuits. The presentation of the proposed multiplier is assessed by contrasting the force utilization, the precision of calculation, and the time delay with those of a rough multiplier dependent on definite calculation introduced. The estimated configuration acquired energy effective mode with satisfactory precision. When contrasted with ordinary direct truncation proposed model fundamentally impacts the presentation. Thusly, this novel energy proficient adjusting based inexact multiplier design outflanked another cutthroat model.
Texture Feature Extraction of Lumbar Spine Trabecular Bone Radiograph Image using Laplacian of Gaussian Filter with KNN Classification to Diagnose Osteoporosis
Authors: Patil Kavita Avinash , Mahendra Prashanth K V , Ramalingaiah A 
Published: November 2021

Abstract
The human bones are categorized based on elemental micro architecture and porosity. The porosity of the inner trabecular bone is high that is 40-95% and the nature of the bone is soft and spongy whereas the cortical bone is harder and is less porous that is 5 to 15%. Osteoporosis is a disease that normally affects women usually after their menopause. It largely causes mild bone fractures and further stages lead to the demise of an individual. The detection of Osteoporosis in Lumbar Spine has been widely recognized as a promising way to frequent fractures. Therefore, premature analysis of osteoporosis will estimate the risk of the bone fracture which prevents life threats. The paper is systematized in two different sections to classify normal (non-osteoporosis) and abnormal(osteoporosis)Lumbar spine trabecular bone. In this method, the first section is based on discriminating the lumbar spine trabecular bone micro-architecture predisposing by means of first and second order directional derivative of Laplacian of Gaussian filter with different standard deviation to acquire the minimum and maximum responses. The dimension reduction of texture features, quantization and adjacent scale coding with weighted multipliers are used to lessen the intensity variations of texture features. The second section is based on the reduction of histogram features as a training data set for classification of normal and osteoporotic images of lumbar spine (L1-L4) using K-Nearest Neighborhood (KNN) classifier. The tested dataset result gives effective classification accuracy of 97.22% with lesser texture feature dimension. The usage of weight multiplier as well as quantization technique plays a major role for the improvement of accuracy to diagnose osteoporosis for an input noisy and noiseless image.
Optimal Design of SRM for EV Application
Authors: Srinivasulu B. , Nagesh Kumar G.V. , Rafi Vempalle , Nageswara Rao P. 
Published: November 2021

Abstract
Electric vehicles (EVs) need a wide speed and torque range for their reliable operation and working. Switched reluctance motors (SRMs) offer several advantages like high life cycle, meager cost, simple construction, robustness, good speed characteristics, and fault-tolerance, making it a suitable motor drive for EV application. The selection of independent variables like dimensions, winding turns, make materials, the number of slots, and the shape of rotor and stator teeth is a cumbersome task as SRM performance is mainly dependent on these parameters. This paper describes the methodology for selecting these independent variables by evaluating the SRM performance for different shapes of rotor and stator teeth, with the different stator and rotor materials, by taking all the design constraints like winding resistance, turns, and the number of slots. The EV drive performance is evaluated for the chosen independent variables and analyzed using JMAG software for determining the efficiency, Torque, and speed responses for comparison and analysis to get the optimal design constraints of the independent variables of SRM.
Performance analysis of pre-trained transfer learning models for the classification of the rolling bearing faults
Authors: Sharma Pavan , Amhia Hemant , Sharma Sunil Datt 
Published: November 2021

Abstract
Nowadays, artificial intelligence techniques are getting popular in modern industry to diagnose the rolling bearing faults (RBFs). The RBFs occur in rotating machinery and these are common in every manufacturing industry. The diagnosis of the RBFs is highly needed to reduce the financial and production losses. Therefore, various artificial intelligence techniques such as machine and deep learning have been developed to diagnose the RBFs in the rotating machines. But, the performance of these techniques has suffered due the size of the dataset. Because, Machine learning and deep learning methods based methods are suitable for the small and large datasets respectively. Deep learning methods have also been limited to large training time. In this paper, performance of the different pre-trained models for the RBFs classification has been analysed. CWRU Dataset has been used for the performance comparison.
Development of Test Automation Framework for Printed Circuit Board Assembly
Authors: Aravind Balaji B , Sasikumar S , Ramesh K 
Published: November 2021

Abstract
A Test Automation Framework is a bunch of rules or coding guidelines for test-process handling that is followed during coding. These are simple strategies that produce helpful results such as improved code re-usability, higher portability, diminished script support cost, reduced interdependency, higher interchangeability, and so on. This paper presents the development of a test automation framework for Printed Circuit Board Assembly (PCBA) to test the functionality and fault identification of the electronic circuit in the PCBA. It provides an integrated array of test and measurement Instruments which is automated using SCPI (Standard Command for programming Instrument) based commands to control test and measurement processes, making it simpler to configure and query. These test instruments imitate the environment in which the PCBA is to be deployed, which helps to observe its characteristics to determine whether it meets the expectation for which it was designed. Any possible variations in its characteristics due to fault are observed during testing and those faults are identified automatically using the data-driven fault analysis method.
Millimeter Wave MIMO out Door Channel Estimation and Precoding
Authors: Murali Krishna P V , Ramana T.V. 
Published: November 2021

Abstract
Millimeter wave (mm Wave) communications is one of the technologies for 5G cellular systems. In the mm Wave communication, there is a lot of path loss can be reduced by Precoding. The channel state information (CSI) should be known at the transmitting station, in the design of precoding matrices and to get good accuracy in estimating sparse channels a Compressive sensing (CS) based recovery algorithms was used. Not only for good accuracy the algorithm is also used for mm Wave channel estimation for exploiting the mm Wave channel’s sparse in multi-path construction. Hence, in this paper, for mm Wave outdoor channel estimation, the CS recovery methods orthogonal matching pursuit (OMP) and compressive sampling matching pursuit (CoSaMP) are used. The singular value decomposition (SVD) precoding is developed using the estimated channel. By (MSE) mean square error and spectral efficiency which were the performance metrics in channel estimation and precoding were done by using MATLAB simulations to get the efficacy of the OMP and CoSaMP algorithm.
Study on piezo-electric flapping wing mechanism for bio-inspired micro aerial vehicles
Authors: Shakya N K , Padhee S S 
Published: November 2021

Abstract
The Micro Aerial Vehicle (MAV) with a flapping wing configuration is much more efficient and capable of generating substantial lift at low flight speeds and has excellent maneuverability. Different motor-driven mechanisms have been developed to mimic this flapping motion, but these mechanisms introduced mechanical complexity and heavy weight to the system. Piezo-electric based mechanisms have been used to solve these problems, but provide very small flapping amplitudes within the size limitation of MAVs. So some kind of amplification mechanism is needed. In this paper, a flexible wing is created by attaching a polymer skin to a pair of carbon fiber reinforced plastic spars. This wing is connected by means of an elastic-element (EE) to a pair of piezoelectric unimorphs (piezofan). The motion from the piezofan to the wing is transferred through this EE. Simulation has been done by applying sinusoidal voltages of varying frequency to this piezofan and observations have been made for the flapping amplitude of the wing for different stiffness of the EE. It is observed that the amplitude of the peak flapping amplitude initially increases, attains a maximum value, then decreases again with an increase in the stiffness of the EE. It is also observed that as the EE stiffness increases, the corresponding peak of the flapping amplitude shifts towards higher frequency.
Detection of Epilepsy based on EEG Signals using PCA with ANN Model
Authors: Shiva Shankar R , Raminaidu CH , Sivarama Raju VV , Rajanikanth J 
Published: November 2021

Abstract
Epilepsy is a chronic neurological illness that affects millions of people throughout the world. Epilepsy affects around 50 million people globally. It is estimated that if epilepsy is correctly diagnosed and treated, up to 70% of people with the condition will be seizure-free. There is a need to detect epilepsy at the initial stages to reduce symptoms by medications and other strategies. We use Epileptic Seizure Recognition dataset to train the model which is provided by UCI Machine Learning Repository. There are 179 attributes and 11,500 unique values in this dataset. MLP, PCA with RF, QDA, LDA, and PCA with ANN were applied among them; PCA with ANN provided the better metrics. For the metrics, we received the following findings. It is 97.55% Accuracy, 94.24% Precision, 91.48% recall, 83.38% hinge loss, and 2.32% mean squared error.
Techno-economic analysis by homer-pro approach of solar on-grid system for Fatehpur-Village, India
Authors: Kapoor Sarang , Sharma Arvind Kumar 
Published: November 2021

Abstract
There are numerous locations available in rural regions of India, where essential electrical energy for load demand is not feasible. Individuals of those particular areas are facing problems, related to their social and economic development. To eliminate the energy crisis, uses of renewable sources are increasing day by day. In this study, we are analysis an On-grid solar energy harvesting system, which will provide necessary power requirement for the whole community of Fatehpur village located at 27°59′15″N and 77°44′07″E, to fulfil energy demand of the community load without much affecting environment. Here, the economic analysis using Homer-pro programme version 3.14.4, are examined, with Homer pro cycle charging as dispatch strategy. Load calculation and components pricing details are concluded through a local survey. Homer pro provides optimized results for selected site, through total present cost (NPC), the least cost of energy (LCOE) and operating cost etc. Based on simulation results by Homer pro, we obtained 1.77 RS/kWh as levelized cost of energy, which is much less than grid power price i.e. 5.6 Rs/kWh and 9,291,770.00 Rs as NPC. Main criteria of this software-package are NPC and LCOE to find out the best possible outcomes for the suited site.
Corrosion inhibition and statistical data of low carbon steel in HCl media by admixed organic compounds
Authors: Loto Roland Tolulope , Loto Cleophas Akintoye , Leramo Richard , Oyebade Babatunde 
Published: November 2021

Abstract
Corrosion inhibition of carbon steels with organic compounds exhibiting effective corrosion inhibition is an on-going research. The corrosion inhibition of low carbon steel in 1M HCl solution by the combined admixture of vanillin and benzonitrile (VBN), and salvia officinalis with lavendulan officinalis (SLV) was studied by weight loss method. Results obtained show that VBN and SLV performed effectively with maximum inhibition efficiency 91.03% at 1.5% VBN concentration and 97.89% at 5% SLV concentration. The inhibition efficiency of VBN increased with concentration but decreased with exposure time compared to the values obtained for SLV which showed non-dependence on concentration and exposure time after 1% SLV concentration. Correlation plots of inhibition efficiency versus inhibitor concentration show VBN to be more concentration dependent in performance. Calculated data for standard deviation shows the degree of variation from mean values for both compounds is significant at low inhibitor concentration due to time dependence action. Statistical analysis through ANOVA shows inhibitor concentration overwhelmingly influences the inhibition performance of the despite even though exposure time is statistically relevant to minimal degree.