bluuebunny/crossref_metadata_2025_split huggingface dataset:
Design and Development of Electrogastrograph Wireless Electrode Module
Authors: Gokul M , Geetha Anandhi C , Monisha R , Nithyaa AN , Harishma S , Murugeshan Pradeep , Mohana M 
Published: September 2022

Abstract
Electrogastrography is a pain free diagnosing method for evaluating stomach myoelectric activity. People are drawn to EGG because it is painless diagnostic procedure. However, given to its non-invasive characteristics, there has been significant debate about the validity and applicability of EGG. The major goal of this project is to decouple the electrode-equipment probe connection. This enhances the patient’s mobility (infant, athlete, handicapped) while lowering probe-to-electrode misconnection. This portable electrode system allows physicians and nurses to examine data of a patient from a distance, decreasing the need for them to be physically present behind the subject for examination. The signal capture, data acquisition unit and communication interface with the Wireless Fidelity module are utilized to quantify stomach activity. Arduino is used to take an analogue signal and convert it to a digital value. The Arduino programming language is used to build the programmers, which are subsequently embedded in the board. As a consequence, the value retrieved from the Electrogastrograph module is transferred over Wireless Fidelity module to the thing speak online platform. Arduino accepts an analogue signal and transforms it to a digital value. The Arduino programming language is used to build the applications, which are subsequently incorporated into the board. As a result, the value retrieved from the EGG module is transferred through Wireless Fidelity module to thing speak online platform.
Signal Processing for Hybrid BCI Signals
Authors: Shelishiyah R , Bharani Dharan M , Kishore Kumar T , Musaraf R , Beeta Thiyam Deepa 
Published: September 2022

Abstract
The brain signals can be converted to a command to control some external device using a brain-computer interface system. The unimodal BCI system has limitations like the compensation of the accuracy with the increase in the number of classes. In addition to this many of the acquisition systems are not robust for real-time application because of poor spatial or temporal resolution. To overcome this, a hybrid BCI technology that combines two acquisition systems has been introduced. In this work, we have discussed a preprocessing pipeline for enhancing brain signals acquired from fNIRS (functional Near Infrared Spectroscopy) and EEG (Electroencephalography). The data consists of brain signals for four tasks – Right/Left hand gripping and Right/Left arm raising. The EEG (brain activity) data were filtered using a bandpass filter to obtain the activity of mu (7-13 Hz) and beta (13-30 Hz) rhythm. The Oxy-haemoglobin and Deoxy-haemoglobin (HbO and HbR) concentration of the fNIRS signal was obtained with Modified Beer Lambert Law (MBLL). Both signals were filtered using a fifth-order Butterworth band pass filter and the performance of the filter is compared theoretically with the estimated signal-to-noise ratio. These results can be used further to improve feature extraction and classification accuracy of the signal.
Silent Speech Interface: An Inversion Problem
Authors: Abirami S , Anirudh L , Vijayalakshmi P 
Published: September 2022

Abstract
When conventional acoustic-verbal communication is neither possible or desirable, silent speech interfaces (SSI) rely on biosignals, non-acoustic signals created by the human body during speech production, to facilitate communication. Despite considerable advances in sensing techniques that can be employed to capture these biosignals, majority of them are used under controlled scenarios in laboratories. One such example is Electromagnetic Articulograph (EMA), which monitors articulatory motion. It is expensive with inconvenient wiring and practically not portable in real world. Since articulator measurement is difficult, articulatory parameters may be estimated from acoustics through inversion. Acoustic-to-articulatory inversion (AAI) is a technique for determining articulatory parameters using acoustic input. Automatic voice recognition, text-to- speech synthesis, and speech accent conversion can all benefit from this. However, for speakers with no articulatory data, inversion is required in many practical applications. Articulatory reconstruction is more useful when the inversion is speaker independent. Initially, we analysed positional data to better understand the relationship between sensor data and uttered speech. Following the analysis, we built a speaker independent articulatory reconstruction system that uses a Bi- LSTM model. Additionally, we evaluated the trained model using standard evaluation measures.
Biomechanical Characterization of Human GAIT Using EMG Parameters
Authors: Das Chitra M , Nagarajan S , Poonguzhali S , Mohanavelu K 
Published: September 2022

Abstract
Predicting and analysing individual muscle forces during walking can give a good perspective of anatomical, physiological, and neurological characteristics of human movement. It can help analyse neuromuscular impairments of skeletal system and provide an understanding on how lower limb assistive devices affect wearer’s body, as these assistive devices are vital to assist people with disabilities to carry their daily activities with ease. Estimating force from EMG allows us to assess the contribution of an individual muscle to the over-all force applied by a group of muscles. This finding helps in understanding muscle dynamics during walking, which can serve as input for assistive devices. Thus, electromyography signals (EMG) can be an excellent choice for force estimation in kinesiological studies. This study aims to predict individual muscle force from EMG during walking. The right gastrocnemius lateralis muscle of a 23-year-old-male subject with no neurological/muscular disorder was analysed at normal walking. Two approaches were used to predict forces from EMG using MATLAB. The forces obtained were compared with force predicted using OpenSim. The main parameters used for prediction were muscle length, muscle velocity, pennation angle, and isometric force, along with EMG.
Automatic Seizure Detection Using Modified CNN Architecture and Activation Layer
Authors: Khan Izhar Dad , Farooq Omar , Khan Yusuf Uzzaman 
Published: September 2022

AbstractAn epileptology expert must visually inspect the EEG to identify abnormal neural activity, which is time-consuming and subject to human errors. The capability of convolution neural networks (CNN) to extract visuospatial features and learn from these discriminative features makes them useful for this task. This paper presents seizure classification based on long-term EEGs using CNN. After filtering, the scalogram is plotted using a 1-second window each. A recently published dataset (TUSZ v1.5.2) was used for the performance evaluation of various CNN-based deep neural networks. The best accuracy obtained for GoogLeNet and AlexNet is 95.88%, and 95.79% respectively with 50 epochs and 32 mini-batch sizes by using the SWISH activation function. The proposed hybrid architecture (AG86) for epoch 50 with mini-batch size 32 has shown the best testing results in terms of accuracy (94.98%) as compared to the SqueezeNet (93.19%), GoogLeNet (92.65%), and AlexNet (94.44%). Similar performance was observed using metrics specificity, sensitivity, Mathew correlation coefficient (MCC), and F1 score. A general inference based on evaluation can be drawn as the proposed hybrid architecture (AG86) showed better test results compared to pre-trained CNN models. Moreover, by replacing ReLU with the SWISH activation function, the performance of AlexNet and GoogLeNet improved.
Dose Response Curve Using Chemically Induced PCC Assay in Peripheral Cells Irradiated In-vitro to 5 MeV Alpha Particles Emitted From Radon Source
Authors: Meenakshi C , Chandrasekaran S , Rajesh N P , Venkatraman B 
Published: September 2022

Abstract
Bio-dosimetry is defined as estimation of dose received by individuals based on biological end points induced by ionizing radiation. By using cytogenetic assay, it is possible to quantify the absorbed dose. High linear energy transfer (LET) radiation produces dense ionizations along their path and causes more clustered DNA damage compared to low LET. Radon is a natural radioactive element present in the atmosphere mainly produced during decay of uranium. The main objective of the present study is to construct dose response curve by using Premature Chromosome Condensation (PCC) techniques in blood cells exposed in-vitro to radon exposure. Blood samples were drawn from healthy non-smokers (n=4), aged between 28 to 42 years and exposed to twenty doses of radon ranged between 0 to 5.5 mGy. A total of about 24000 metaphase spreads were counted to find the DNA damages include ring chromosomes (RC), dicentric chromosomes (DC) and acentric fragments (AF). As the dose increases gradual increase was observed in RC, DC and AF with the slope of 0.001, 0.005 and 0.064 respectively. So far, no calibration reference curve was generated for low dose radon using chemically induced PCC assay and hence this study is considered as first of its kind.
Design of a mobile application for the vaccination registry of children under 5 years
Authors: Chaupi Alvarez Miriam Senaida , Loja Ponce Erick Arturo , Auccahuasi Wilver 
Published: September 2022

Abstract
Information and Communication Technologies are allowing to improve the development of certain processes optimizing the management of information for users, as in the case of the vaccination control process to digitize the use of the vaccination card, being very important for the registration of the administration of vaccines to the child during his five years. This information should be easily accessible and available to parents and health personnel during each stage of vaccination. In the present work we developed a mobile application called Children’s Vaccine, with the purpose of registering and storing vaccine data (type, date and place of vaccination), as well as relevant information for the management of vaccines, the prototype developed was implemented in the health Center: Juan Pablo II Maternal and Child Center located in the District of Villa el Salvador, in Lima, Peru, in order to improve the quality of the service of attention. We present the results as performance tests to measure the influence of the application, this evaluation was carried out through an observation sheet with the comparison of records before and after the use of the application called “pre-test” and “post-test”. This application is applicable and scalable for various health centers.
Hardware Implementation Of ECG Signal Compression Using SPIHT
Authors: Shanmuga sundari M , Loganathan S , Sujatha C.M 
Published: September 2022

Abstract
Cardiovascular disorder is a primary cause of mortality throughout the world in both developed and underdeveloped countries. Continuous cardiac monitoring enables clinicians to identify arrhythmias and other heart conditions. Tele-cardiology introduces remote monitoring devices for tracking the cardiac activity of the patients. The large volume of Electrocardiogram (ECG) data needs to be stored, processed and transmitted by these portable health care devices. The implementation of ECG compression in hardware platform is crucial for continuous health monitoring applications. The aim of this work is to implement field programmable gate array based set partitioning in hierarchical trees-based electrocardiogram compression. Discrete wavelet transform method is employed to break up the signal into sub bands. The transformed coefficients after discrete wavelet transform are passed through dead zone quantization which rejects low magnitude values of transformed coefficients lying around zero. These quantized coefficients are then encoded by lossless set partitioning used in hierarchical trees compression approach. The introduction of dead zone quantization in the proposed technique is found to be effective and yields an increased compression ratio of 10.33 with decreased distortion value of 1.04 percent for ECG record 117 of MIT-BIH arrhythmia database.
Effect of grounding on electromyogram and hand grip strength in healthy young adults
Authors: Anusha  , Dwivedi Anjana 
Published: September 2022

Abstract
Handgrip strength has often used as a tool to measure upper body conditions including neuromuscular performance, frailty, and cardiovascular health. Grounding has been a way of living in past and presently have been demonstrated by researchers to speed wound healing, reduce inflammation, and balance excessive free radical charge in the body. Grounding is hypothesized to work in two ways-one by discharging the static field developed in the body by exposure to electrical devices and by neutralizing the excess free radicals generated in the body due to improper lifestyle. This study aims to analyze the change in handgrip strength and EMG signals in young adults pre-and post- grounding. The participants are students aged 22-28 having maximum exposure to mobile phones and laptop radiation especially due to the current pandemic situation for the necessity of taking online classes. We are trying to relate grounding and muscular performance (in terms of handgrip strength, fatigue time, electromyogram) to understand how grounding for few hours a day can make change in a person’s life. Grounding can be used as an effective lifestyle intervention for improving strength and hence quality of life. Even after only one hour of earthing, handgrip strength, EMG, and fatigue time were improved.
Inducement of Artificial Sleep using Low Strength Magnetic Waves
Authors: Sudhakar T. , Hari Krishnan G. , Prem Kumar J. , Divakaran Sindu , Janney Bethanney , Mohandass G. , Devanesan Pavithra S , Shalini S. 
Published: September 2022

Abstract
Every human need eight to ten hours sleep every day for healthy and good functioning of brain and body. Proposed system induces sleep using low frequency, low strength magnetic waves produced by using coil circuitry. Majority of age people, people with anxiety, stress, depressed feel difficulty to get sleep due to few unknown factors. Our proposed system will induce sleep without drugs, meditation and other chemical-based methods having huge side effects.
Methodology for the multichannel recording of upper limb muscle activity
Authors: Ovalle Christian , Cordova-Buiza Franklin , Herrera Lucas , Auccahuasi Aly , Echevarría Martha Romero , Rojas Karin , Urbano Kitty , Loza Ana Barrera , Revilla Jorge Figueroa , Herrera Salazar José Luis 
Published: September 2022

Abstract
In the study of the functioning of the muscle, the study standard is to be able to analyze its behavior or functioning, this behavior is represented by a continuous signal where the muscle signal can be recorded and visualized, to perform this registration a designed circuit is required For the capture of electromyography signals, there are many options of these circuits in the market, depending on the application, the part of the body that is required to be analyzed, the best option can be chosen, in this work, we present a methodology for recording in Simultaneous 8 signals, for the characteristic use of the arm muscles, due to its special shape, which allows it to be placed in the form of a bracelet, the signals that are acquired simultaneously and individually, can be used in many applications depending on the problem and challenge, recommending for use on the arm and providing a clean signal allowing Being used in the design of detection mechanisms for some pathologies related to the arm muscles and in the design of control mechanisms for the design of prostheses.
Effective minutiae extraction and template creation in fingerprints
Authors: Karuppathal E , Kalpana R , Thilagavathy B , Baghavathi Priya S , Rajamanogaran M 
Published: September 2022

Abstract
Despite years of research in using the fingerprint as a biometric entity, there remains a niche to improve the underlying accuracy, specifically on enhancement, feature extraction and classification consideration. Attempted in this paper is a ridge compensation filtering technique for enhancement, followed by minutiae extraction and false minutiae removal and eventually a procedure for template creation. The extracted features are evaluated using metrics like accuracy, error rate and goodness index. These values were found to be better when compared with the results of some state-of-art algorithms, which endorse the effectiveness of the first two stages. Mahalanobis distance (MD) is used to recognise the fingerprints through the generated features that resulted in accuracy above 92%. Finally, convolution neural network (CNN) is used to cross-examine the recognition capacity. Though CNN also able to give a similar accuracy, the proposed algorithm is simple and robust and hence easily embedded in hand-held devices.
Deep ensemble of texture maps for false positive reduction in mammograms
Authors: Srinivashini N , Raveenthini M , Lavanya R 
Published: September 2022

Abstract
Worldwide, breast cancer is a life-threatening disease attributing to increased mortality rates among women. Mammograms are commonly used for screening breast cancer in asymptomatic stages. However, the subtle nature of abnormalities in early stages makes mammogram analysis a cumbersome task. A computer aided diagnosis (CAD) system can complement subjective diagnosis of physicians with its objective assessment. Mass detection is the most important task in breast cancer diagnosis, as masses are the prominent indicators of the disease. Nevertheless, it is the most challenging task due to the ambiguity between masses and the surrounding normal tissues, especially in dense breasts. Though CAD systems are effective in detecting masses with high sensitivity, the price paid is usually high false positive rates (FPR). Texture analysis is normally employed to reduce the FPR in mass detection, where texture features extracted from suspicious regions are used to build a classifier model to discriminate between actual masses and false positives. Deep learning (DL) is a data-driven model that is gaining increased importance in diverse fields, including medical diagnosis, that involve voluminous amounts of data. In particular, convolutional neural network (CNN) plays an important role in image analysis in various applications, including mammogram analysis. Converting raw images to texture maps can enhance the performance of CNN for false positive reduction. In this work, textural image maps based on Hilbert curve, forest fire model, Radon transform, discrete wavelet transform (DWT) and curvelet transform are analysed using CNN. More specifically, an ensemble of CNNs based on these individual textural image representations is constructed. The proposed work is validated on CBIS-DDSM, a publicly available benchmark dataset, demonstrating 100% accuracy for mass detection with 0% FPR.
Computer aided diagnosis system for breast density classification in mammograms
Authors: Shrinithi S , Vijayan Devi , Lavanya R 
Published: September 2022

Abstract
Breast cancer is a deadly disease affecting women around the globe. Mass detection in the breast tissue at an early stage can lessen the mortality rate occurring due to breast cancer. Through mammograms, the presence of masses can be detected at an early stage, however, it’s sensitivity and specificity are limited in the case of dense tissues. Identification of the breast density type prior to the detection of mass can lessen the chance of misclassifying a breast tissue as normal or abnormal, which eventually decreases the false negative and false positive rate. The proposed system classifies breast density on the basis of Breast Imaging Reporting and Data System (BI-RADS). The proposed method has explored the effect of local descriptors on breast density classification and various feature-classifier combinations have also been explored for the classification. The proposed method validated on 624 mammograms from the Image Retrieval in Medical Applications (IRMA) version of the Digital Database for Screening Mammography (DDSM) database has produced an accuracy of 73% for multi-class breast density classification using the speeded-up robust features (SURF) and support vector machine (SVM) classifier.
Transfer learning approaches in deep learning for Indian sign language classification
Authors: Abraham Tuhina Sheryl , Sachin Raj S P , Yaamini A , Divya B 
Published: September 2022

Abstract
Speech is the major way of human communication, but when it is limited, humans move to tactile kinaesthetic communication. People with speech-hearing impairments use sign language as an example of such adaptations. The deaf community uses Indian sign language (ISL) throughout India. In India, 250 licensed sign language interpreters are serving a deaf population of 1.8 to 7 million individuals. ISL interpreters are badly needed at institutes and places where persons with hearing impairments communicate. An Indian sign language picture database for English alphabets is established in this project. To prepare it for training, several pre-processing techniques were used. The effectiveness of deep learning neural networks is frequently influenced by the quantity of data available. As a result, data augmentation, a strategy for adding more and diverse samples to train datasets, was used to boost the effectiveness and outcomes of machine learning models. Our model is trained in CNN models utilizing transfer learning methodologies, with an accuracy of 95% for vgg16 and an accuracy of 92% for the inception model. More study on this research, as well as real-time implementation, has the potential to better connect people with hearing loss to society.
Methodology for muscle recovery, through motor imagery techniques applying virtual reality
Authors: Ovalle Christian , Cordova-Buiza Franklin , Linares Oscar , Auccahuasi Aly , Echevarría Martha Romero , Meza Sandra , Urbano Kitty , Loza Ana Barrera , Revilla Jorge Figueroa , Bernardo Grisi 
Published: September 2022

Abstract
Recovery of movement is a long and hard process for patients. One of the most common problems that occurs is the so-called “phantom limb", which induces the body to feel strange due to the failure to use the injured limb. This process is due to the lack of use of the leg that in most cases is fractured and the long recovery time, so the use of crutches makes the body in conjunction with the brain understand that normal movement of the body is with the leg suspended and always with the help of crutches. In the present work a technique for the retraining of the brain is presented by means of which the brain is taught through the presentation of images if it corresponds to the right and left side of the arms and legs. This retraining makes it possible for the brain to understand the coordinated movement between the arms and legs. In addition to the use of virtual reality, through the OCULUS GEST II lenses, allowing maximum concentration, the results allow to improve the recovery time of patients, the technique can be used both in rehabilitation centers and in the patient’s own home patient.