bluuebunny/crossref_metadata_2025_split huggingface dataset:
Performance Analysis of Deep Learning Architectures for Super Resolution
Authors: Nair Devi Parvathy , John Ann Maria , Varshini E R Amrita , Arunima D , Gopakumar G 
Published: June 2021

Abstract
Super resolution (SR) being one of the computer vision tasks with increasing applications in modern scenarios, several challenging factors are still prominent despite the numerous breakthroughs achieved in this field in recent years. Introduction of deep convolutional neural networks has brought a booming development to the existing SR techniques tackling many unsolved challenges. As an attempt to perform a relative analysis between currently used methods, this paper explores and establish the capability of Enhanced and Wide super resolution networks. These models are encompassed with improved residual networks with an aim to achieve a higher accuracy with reduced memory usage. The models trained with DIV2K dataset are evaluated using the T91 dataset and found to be showcasing a reliable performance in comparison with other cutting-edge methods devised for super resolution.
Assessment & Alternatives for SSR in a Series Capacitor Wind Farm Employing TCSC
Authors: Patil Vaishali S. , Thakre Mohan P. 
Published: June 2021

Abstract
Wind energy systems are going to build their footprint in the electricity grid around the world as one of the most viable renewable and environmentally friendly sources of energy. Keeping this scenario in mind and predicting further growth in installations, power systems should be prepared for the assimilation of such large wind farms. Almost every system has been outfitted with electricity compensation processes to enhance a sustainable transfer of electricity generation.
This would have the possibility to trigger SSR difficulties in a variety of power grid circumstances. An analysis and SSR solutions will be discussed and recommendations. SSR inhibition is accomplished via the TCSC that also aids in increasing the transmitting station’s power flow. So if coupled to closed-loop control loops, TCSC dampens SSO. Efficacy is evaluated using SIMULINK studies in the MATLAB environment to results demonstrate the effectiveness of TCSC throughout the SSR damping process.
Classification of COVID 19 in Chest CT Images using Convolutional Neural Network
Authors: Manikandan T , Alfy T Jabin , Sherin S , Aadhithya V , Senthil Kumar T.K 
Published: June 2021

Abstract
The single-celled organisms called a virus are the root cause of many harmful diseases that affects both animals and humans. Coronavirus is one type of virus that is usually found in animals but if transmitted to humans can cause a wide range of respiratory diseases. One such disease caused is the COVID-19 diseases which caused a worldwide epidemic since its origin in 2019. The disease which is said to have originated in China had caused more than 252000 deaths worldwide in a few months. The test for the COVID-19 involves analysing the throat swab sample which may take days if not a week and by the time the results come the infection would have spread. Hence there is a need to improve the testing procedure for COVID-19. In this paper, we have come up with an automated Image Analysis technique to diagnose COVID-19 using the chest Computed Tomography images of the chest that uses a Convolutional Neural Network. This developed method has shown very good accuracy and efficiency in recognizing the COVID-19 infected CT images.
Non – Clinical Pain Assessment System – A Pilot Study
Authors: Krishnan Chetana , Sandhyavarshini R , Jeyakumar Vijay 
Published: June 2021

Abstract
The term ‘Pain’ refers to the sensory response of the body when the reflex tolerance level of the person exceeds the threshold value. Though most of the pain senses are tolerable, it still disturbs the normal anatomical actions of the body, classifying it as tolerable and intolerable pain. Various techniques have evolved in the past few years to quantify pain; however, they are successful only in subjective determination thus leading to inaccuracy in prediction. The proposed paper discusses a few objective methods to quantify non – clinical pain. FLIR spectroscopic images can recognize inflammation and high-temperature points in a given region. Upon using various mechanical algorithms, accurate pain values can be determined. Similarly, by solving the pain-causing equation, we can derive the pain peak values. These methods can replace the conventional scales and help in subsidiary pain assessment.
A Comprehensive Analysis of Multimodal Speech Emotion Recognition
Authors: Bagadi Kesava Rao 
Published: June 2021

Abstract
Emotion recognition is critical in dealing with everyday interpersonal human interactions. Understanding a person’s emotions through his speech can do wonders for shaping social interactions. Because of the rapid development of social media, single-modal emotion recognition is finding it difficult to meet the demands of the current emotional recognition system. A multimodal emotion recognition model from speech and text was proposed in this paper to optimize the performance of the emotion recognition system. This paper, explore the comprehensive analysis of speech emotion recognition using text and audio. The results show that enhancement of accuracy compared to either audio or text. Here, results were obtained using the deep learning model I.e. LSTM. The experiment analysis is done for RAVDESS and SAVEE datasets. This implementation is done by python programming.
Face Spoof Detection Using VGG-Face Architecture
Authors: Balamurali K , Chandru S , Razvi Muhammed Sohail , Sathiesh Kumar V. 
Published: June 2021

Abstract
Face recognition systems have been obtaining substantial importance in modern world. Security systems are major application of face recognition system. However, the potential of the face recognition system to withstand the attack of an unauthorized person is an important concern. Face recognition systems are vulnerable to photographs and video spoof attacks. In these scenarios, anti-spoofing systems comes in handy to evade these attacks. Robust solutions are required for face recognition system to be immune against spoofing attacks. In this paper, the detected face is denoised and then converted to YCbCr and CIELUV colour model and then passed through VGG-Face architecture for extraction of face embeddings of each colour space. Then the extracted face embeddings are concatenated and then passed through SVC (Support Vector Classifier) which then classifies real and spoof faces. The proposed method has obtained a test accuracy of 99.6% with specificity of 99.5% for spoof detection.
Mitigation of black hole attack in MANETs: A sequence no-based approach
Authors: Maheshavel L. , Senthilkumar C. 
Published: June 2021

Abstract
MANET (Mobile Ad-hoc network) is type of network which is comprised of moveableness vertices which are connected to form a wireless network. The connected vertices can move very freely as there is no fixed infrastructure for the network. Black hole attack is a DOS attack where one of the vertices in the network is mischievous which tries to capture all the datagrams passing through it by sending a false reply to the origin vertex. In this paper an improvement is made to the existing AODV protocol to mitigate the black hole attack in Manets. The proposed method finds the mischievous vertex and avoids the delivery route through the mischievous vertex. The simulations were experimented in ns2 and the observations prove that the implemented approach works well for various performance metrices like Packet delivery ratio, End to End delay and throughput.
Razor flip-flop based Detector/Corrector System for Correcting Timing violations in Digital Circuits
Authors: Hema Chitra S , Vaidhehi R , Mohandass S 
Published: June 2021

Abstract
In a flip flop, the difference in time between transitions of data input and the active edge of the clock is called its setup time. If the data input changes during this time window, the storage will not be correct. This is called setup time violation. Hold time is the minimum time during which data must be stable after the active edge of the clock. Hold time violation will cause incorrect data storage. The objective of the paper is to design an efficient Detector/Corrector system that monitors and corrects any setup/hold time violations. A Razor flipflop based Detector circuit is proposed in this paper. The Detector block compares data and clock to produce an error signal which in turn makes the Corrector block to correct the clock that assures the desired setup/hold time. The system avoids the setup/hold time violations by simply inverting the clock signal timing. The system is tested using the following digital circuits: ISCAS89 S27 benchmark circuit and a 2×1 multiplexer. Simulation is done using Cadence Virtuoso with 180nm technology. The results are analyzed with existing and proposed detector/corrector systems. The results show that the proposed detector/corrector system is capable of correcting both the setup/hold time violations and also has lesser power consumption when compared with the existing system.
Secure E-Health using Images Steganography
Authors: Manikandan T , Muruganandham A , Babuji R , Nandalal V , Mazher Iqbal JL 
Published: June 2021

Abstract
The main aim is to secure the medical records by ensuring safe communication between the sender and the receiver by adding multiple layers of security. This project is a combined approach of both encryption and steganography to give security the utmost importance. In the sender, only the authorized person can send the secret image to the receiver. In the first stage, an image steganography system is processed using confidential medical X-ray images which are hidden under the cover images. The encryption method used is based on Random Number Generator and the pixel indicator, in other words, by moving the position of the pixel in the image to encrypt the image on the transmitting side. Whereas on the receiver side, this project proposes a method to authenticate the receiver by incorporating Email Authentication and OTP Verification to decrypt the encrypted image.
BDoor App-Blood Donation Application using Android Studio
Authors: Periyanayagi S , Manikandan A , Muthukrishnan M , Ramakrishnan M 
Published: June 2021

Abstract
In fashionable days folks ought to create use of the trendy technologies to seek out growth altogether sectors. During this sense, Blood, donation apps are the new hopes for the folks in The Asian nation for blood property. Nowadays, several blood apps are accessible in Asian nation however their utilization is extremely poor owing to some disadvantages. One such serious disadvantage is privacy and safety. Donor Contacts may be simply accessed by everybody. It causes several issues to the top user. All apps that are accessible within the play store focuses the property between the donors and therefore, the recipient at fewer intervals. The B-Door app was created to protect the donor’s privacy and donor identity, as well as the recipient’s safety, using the J48 decision tree algorithm. The proposed solution attempts to provide support for both emergencies and reserved time things. The core plan of the applying is to link blood banks, donors with recipients through a licensed hospital, NGO’s and alternative establishments to avoid misuse. This Application was developed through Android Studio and Flutter UI Framework together with base at backside and provides our result on Real time basis.
Infant Action Database: A Benchmark for Infant Action Recognition in Uncontrolled condition
Authors: Balasathiya S Sujitha , Roomi S Mohamed Mansoor , Sathyabama B 
Published: June 2021

Abstract
The main focus of our work is to create a database for action recognition of unattended infants in uncontrolled environment with wide variations in surroundings, lighting, interactions with objects, camera motions, etc., Action recognition of infants is emerging as an important and technically challenging computer vision problem as compared with adult action recognition because of their physical appearance. Most of the previous action recognition techniques have focused on the recognition of action captured under a controlled environment in a standard laboratory setting. In this study a novel database is introduced that can be used as a benchmark for surveillance parenting. This database involves nine normal and nine abnormal actions classes which consist of actions and movements of infants occurring in fairly uncontrolled conditions. This database consists of realistic user-uploaded videos which are recorded in the clustered background and different camera motion. After the collection of all videos, they are manually trimmed to form a database. To further evaluate the performance of the database, HOG features were extracted from database set and trained by different Machine Learning classifiers like Multi-class Naive Bayes, Support Vector Machine, Ensemble classifier, Discriminant analysis and Decision tree classifiers. This experimentation shows that the database is complex and robust that can serve as a base for testing action recognition algorithms.
Fabric Woven Textile Antenna for Medical Applications
Authors: Sugunavathy S , Sudha V K , Parthiban D. 
Published: June 2021

Abstract
Wearable Technologies are key driving forces in transforming conventional Health care systems to an unprecedented prototype of Connected Health (CH) Care systems. In these Connected Health Care systems, the role of wearable antennas cannot be overlooked. For designing reliable wearable systems, antenna should be of low profile, compact in size, lighter in weight, conformal and flexible to fasten it on Patient’s attire. As a contribute approach, this paper aims at proposal of microstrip patch antenna that resonates at ISM band of 2.4GHz. The proposed antenna is built on Rubber substrate with Polyester (Ground plane) as a conducting fabric driven by a microstrip line feeding structure. Improvement in antenna performance could be achieved by using Defected Ground Structure (DGS) polyester ground. Performance characteristics such as Directivity, Gain, VSWR and Radiation Pattern are measured. Specific Absorption Rate (SAR) value for Human Phantom model was calculated by on Body Simulation. The antennas and the human body tissue effects is observed in CST tool.
Detecting Subacute Thyroiditis after COVID-19 infection using Deep Learning Techniques – A Case Study
Authors: Abdullah A Sheik , Suganya R , Abirami A M , Bhubesh K R A 
Published: June 2021

Abstract
The act of Subacute Thyroiditis (ST) is considered to be a challenge in medical informatics which severely affects half of everybody. It plans the thyroid knobs of high-goal thyroid ultrasound. The mechanism formulates the C cells in the Thyroid gland, which makes a hormone that helps control the levels of calcium in the body which gets affected after COVID-19. Hundreds of research teams have been working in recent days to collect data and apply computational techniques to analyze and interpret the experimental results in COVID-19. Still, only a few researched the impact of Thyroid due to COVID-19. In the proposed architecture, we introduced CNN with GapNET-PL and Convolutional Block Attention Module (CBAM) to improve the overall mechanism and also utilize the significant features from the Pooling layer. We collected real-time ultrasound thyroid image dataset consists of 19 images from COVID-19 infected patients for preparing and approval of the proposed model. The significance of this work is to gauge the relationship between’s contamination with COVID 19 and the improvement of ST utilizing AI procedures. The trial results show that the assessment of thyroid datasets with the proposed approach has given 89% of accuracy with improvement in P-value <0.001. The training parameters provided valid results with improvisation in statistical performance. Thereby this research can support doctors in the domain of imaging analytics with the aid of AI-systems in reasoning COVID-19 diseases related to Subacute Thyroiditis. Hence this can be strongly recommended for the validation of medical data and its risk factors that contribute towards the disease.
Face Detection in Blurred Surveillance Videos for Crime Investigation
Authors: Menaka K. , Yogameena B. 
Published: June 2021

Abstract
Face detection itself acts as a significant problem in low-resolution surveillance video, primarily due to out-of-focus blur. Real-time and forensic analysis for Law enforcement desires superior face recognition system for person identification in surveillance applications. However, face detection influences face recognition in critical situations. If faces on the blacklist are not detected, recognition fails, which increases the burden and is a tremendous challenge for police authority. This motivates us to present the deblurring algorithm to improve the face detection rate and reduce the false-positive rate. Hence, this paper focuses on removing blur by applying a blind deconvolution algorithm, suppressing the ringing artifact in surveillance video by adopting Discrete Wavelet Transform (DWT). Finally, after this preliminary work, faces are detected by Yolo v2. The proposed framework works well in the sparsely crowded scenario and improves face detection rate compared to the Lucy-Richardson-Yolo v2 framework, which suffers from the problem mentioned earlier. Experiments and evaluations on the surveillance dataset show that the proposed blind deconvolution with ringing suppression-based solution outperforms the state-of-the-art methods in detecting both frontal and profile faces.
Detection of Inferior Alveolar Nerve Canal by Feature based Machine Learning Approach
Authors: Uma Maheswari P. , Banumathi A. , Priya K. 
Published: June 2021

Abstract
For pre-diagnostic surgical process in dental implantology, detection of Inferior Alveolar Nerve Canal (IAC) in dental images is highly essential to avoid surgical complications and injury. In this paper, a feature based machine learning model is developed for the detection of IAC from the mandible regions of dental OPG images. Initially, the soft tissue regions are enhanced by S-CLAHE (Sharpening based Contrast Limited Adaptive Histogram Equalization). Subsequently, Shape features of the IAC using Histogram of Oriented Gradient (HOG) and texture features using Local Forward Rajan Transform (LFRT) are extracted. These features are considered as an input for Machine Learning classifier. From the trained results, the feature points of IAC region are detected by polynomial curve fitting approach. The performance of the classification technique is evaluated with existing machine learning classifiers. Adaboost M2 Ensemble classifier achieves the best accuracy of 96% compared to other state of art techniques such as Naïve Bayes, KNN, SVM, and Decision Tree. Therefore, the proposed method has high potential in IAC detection and avoids complexities in dental implant surgery.
Identification of Melanoma from Nevus Images
Authors: Bharathi S , Premchand J , Nivedeethaa AS , Kousiya M , Ajay Kumar V 
Published: June 2021

AbstractMelanoma is one of the fatal type skin cancers. The mortality rate is more severe than the present skin related diseases. Humans are prone to various diseases and are easily infected by them due to their adaptive and constrained lifestyle and melanoma is not an exception. Melanoma spreads faster and is less responsive to treatment in its later stages. Thus, harnessing the disease rate becomes more enigmatic and initial diagnosis is the need. Melanoma and nevus are akin and show analogous symptoms and traits. In order to overcome this, we use the technique of image processing to segregate Melanoma and nevus. The input image is processed in such way that the noise in the image (skin lesion) is removed using median filter and is segmented using improved K-means clustering. From the lesion necessary textural and chromatic features are extracted and a unique feature vector is formulated. Melanoma and Nevus are segregated using both Adaptive Neuro-Fuzzy inference System (ANFIS) and Feed-Forward Neural Network (FFNN). The skin images from DERMIS dataset is used in this work and it has 1023 skin images including 104 melanoma and 917 nevus images. Our proposed methodology provides efficient results having 97.3% and 96.8% accuracy for ANFIS and FFNN classifiers.
Smart Irrigation System in Agriculture
Authors: Vimal S P , Sathish Kumar N , Kasiselvanathan M , Gurumoorthy K B 
Published: June 2021

Abstract
The most difficult task in agriculture is watering the fields. The different types of watering system are drip system, nozzles type, tube method and sprinkler system. This paper focus about the drip system. The work specifies the moisture level sensing of the crops and supply the require water when needed. The components included in this work are ATmega 328 Microcontroller, GSM Module, Humidity sensor and soil moisture sensor. A prototype has been developed to protect the plants or crops more self sufficient from watering and sunlight. The model reports the status of current condition and also reminds the need of water through GSM module. The prototype model is a loop closed control system is designed to continuously monitor the humidity, temperature, soil moisture level and controlling the irrigation systems from the output of the pumping unit.
Air Pollution Hotspot Detection and Identification of Their Source Trajectory
Authors: Gurumoorthy K B , Vimal S P , Sathish Kumar N , Kasiselvanathan M 
Published: June 2021

Abstract
Millions of deaths everywhere the planet, thanks to anthropogenesis fine material (or PM2.5) is principally caused thanks to outside pollution. Coimbatore may be a centre of textile and cotton trade, producing, poultry farming, education, info technology and health care and it’s the second largest town once Chennai within the state of state. Thus, this paper predicts the accumulation of PM2.5 from wind (velocity and direction) and precipitation levels. It imbibes a machine learning (ML) algorithm supported six years of earth science and pollution information inferences. At present, pollution may be a world downside. Republic of India is additionally an enormous sufferer of this downside. Thus, it’s necessary to spot the recent spots of pollutants and their transport specifically carbon monoxide gas (CO), sulphur-dioxide (SO2) and oxides of element (NO+NO2) victimization advanced information analysis techniques. Challenges concerned during this current statement is mining the datasets from completely different parameters and providing the ultimate output with moderate abstraction resolution on pollution info. Therefore, the study illustrates that the employment of applied mathematics models supported the ML algorithm is most relevant to predict PM2.5 accumulation from earth science information.
Fabrication and characterization of composite dental material using X-ray radiography
Authors: Sutikno  , Ayu D N L , Susilo  
Published: June 2021

Abstract
Acrylic resin is a material that often used in the manufacture of dentures. Composite dental base materials consist of acrylic and glass powder. The addition of glass powder aims to improve the physical and mechanical properties. The increases in the mass fractions of glass powder in composite dental were optimized between 12.5% - 75% and added 2 ml of liquid for each sample. The composite dental surface is characterized using x-ray radiography, CCD microscopy, and the hardness is characterized using Vickers hardness tester, and the density is also measured. The result shows a mass fraction of 75% was the optimum condition with a radiographic gray level image of 196.814 at a voltage of 50 kV and a current of 16 mA, hardness of 26.7 g/mm2, density of 2.28 g/mm3 and greater mass fraction of glass powder, the surface of the composite dental look tighter and lighter in color.