bluuebunny/crossref_metadata_2025 huggingface dataset:
Brain Tumor Reporting and Data System: A Pictorial Review
Authors: Rao B. , Ikuta I. , Mahajan A. , Karam A.A. , Zohrabian V.M.
Published: October 2021

Brain tumors are a diverse group of neoplasms that are a source of substantial morbidity and mortality worldwide. Primary gliomas constitute almost all malignant brain tumors, with the most aggressive as well as most common form in adults, grade IV glioma or glioblastoma multiforme,
carrying an especially poor prognosis. Neuroimaging is critical not only in the identification of CNS tumor but also in treatment-planning and assessing the response to therapy. Structured reporting continues to gain traction in radiology by reducing report ambiguity and improving consistency,
while keeping referring clinicians and patients informed. The Brain Tumor Reporting and Data System (BT-RADS) is a relatively new paradigm that attempts to simplify and maximize consistency in radiologic reporting. BT-RADS incorporates MR imaging features, clinical assessment, and timing of
therapy to assign each study a score or category, which is, in turn, linked to a management suggestion. The purpose of this pictorial review article is to familiarize radiologists and nonradiology neurologic specialists alike with BT-RADS, highlighting both advantages and limitations, in the
hope that adoption of this system might ultimately facilitate more effective communication and improve consistency among reports.Learning Objective: To describe the features and underscore the advantages and disadvantages of the Brain Tumor Reporting and Data System (BT-RADS), a relatively
new classification system that attempts to simplify and maximize consistency in radiologic reporting
Imaging of Complications of Head and Neck Cancer Treatment
Authors: Villarinho L.L. , Macedo V.S. , Taylor C.S.
Published: October 2021

Head and neck tumors are a heterogeneous group of neoplasms with variable cell origin, prognosis, and treatment options. Management is related to histologic type and staging, but surgery and chemoradiation are often the main options of treatment. These treatments, though widely used,
can be associated with local complications. Early complications are usually related to surgery, and late complications are related to radiation therapy. Imaging plays an important role in the diagnosis and management of these complications, and radiologists must be familiar with them. The
authors describe multiple important complications of head and neck cancer treatment with corresponding imaging findings.Learning Objective: To demonstrate important complications of head and neck cancer treatment
Performance Analysis of K-Nearest Neighbor Classification Algorithms for Bank Loan Sectors
Authors: Hemachandran K  , George Preetha Mary , Rodriguez Raul V. , Kulkarni Raviraj M. , Roy Sourav
Published: October 2021

An attempt has been made to develop an algorithm for banks to check the credibility of borrowers to avoid nonperformance assets. People move towards different banks for loan purpose to fulfil their financial needs. Approaching bank for loan is increasing day by day mainly for child marriage, education, agriculture, business, home loan etc. Some people take the loan and they won’t pay back in time or some will move out of the country without any intimation, so that bank will go in loss. Even now in covid-19 pandemic many industries were closed but they need to give salary to the employees, need to pay rent and electricity bills too for that they will approach bank for loan. For all these cases bank first need to analyse their Credit Information Bureau India Limited score and check whether they had done loan repayments in appropriate time or not. In the present work the effectiveness of K nearest neighbor algorithm were analysed. This research were carried out using python. The accuracy of this classifier is analysed using following metrics such as Jaccard index, F1-score and LogLoss. This helps to find the potential of the customer which is much higher than the data mining classification algorithm and thus it helps in sanctioning loans.
An Effective Comparative Analysis of Data Preprocessing Techniques in Network Intrusion Detection System Using Deep Neural Networks
Authors: Aravind Prakash M  , Indra Gandhi K  , Sriram R  , Amaysingh
Published: October 2021

Recently machine learning algorithms are utilized for identifying network threats. Threats otherwise called as intrusions, will harm the network in a stern manner, thus it must be dealt cautiously. In the proposed research work, a deep learning model has been applied to recognize and categorize unanticipated and unpredictable cyber-attacks. The UNSW NB-15 dataset has a vital number of features which will be learned by the hidden layers present in the suggested model and classified by the output layer. The suitable quantity of layers, neurons in each layer and the optimizer utilized in the proposed work are obtained through a sequence of trial and error experiments. The concluding model acquired can be utilized for estimating future malicious attacks. There are several data preprocessing techniques available at our disposal. We used two types of techniques in our experiment: 1) Log transformation, MinMaxScaling and factorize technique; and 2) Z-score encoding and dummy encoding technique. In general, the selection of data preprocessing techniques has a direct impact on the output performed by any machine learning process and our research, attempts to prove this concept.
Feasibility of Convolutional Neural Networks (CNN) for Content Based Image Retrieval of Images from Hadoop
Authors: Muthiah.M.A  , Logashanmugam.E  , Nandhitha.N.M
Published: October 2021

In search engines, though feature-based query is provided, Content Based Image Retrieval (CBIR) still results in less sensitivity and specificity. It is because the conventional approach is based on feature extraction and inherent parameters in conventional feed forward networks. Performance of the system is strongly dependent on the extracted features. Hence it is necessary to develop a CBIR system that retrieves the similar images without explicit feature extraction. Convolutional Neural Networks are the recent neural network architectures which accept images as input and perform both feature extraction and classification. The proposed work aims at using the conventional architectures of VGGNET, RESNET, and DENSENET for flower classification in CBIR. Performance is measured in terms of accuracy of classification.
A Reliable Cloud Based Framework for Large Scale Urban Sensing Systems
Authors: Jansi K.R. , Sandhia G.K. , KasmirRaja S.V.
Published: October 2021

The rising usage of smartphones and sensing models in today’s life induces the development of large scale urban sensing networks in communication concepts. With the efficient implementation of people centric mobility models, the personal communication devices of people are acting as the sensor nodes that are capable of sensing the human behaviours and participating in framing the smart protocol designs for smart cities. Without the involvement of mobile users or people, the new protocols for mobility management, traffic management, environmental sensing and other applications become futile. The proposed cloud based model improves the reliability and scalability of the system with its multiple cloud servers design. The single point of failure can be resisted since many cloud servers belonging to a provider is used. So the framework remains fault tolerant in the presence of any server attacks. A standard homomorphic based encryption scheme is used for providing data confidentiality and also data is transferred anonymously improving the privacy of the system. The data aggregation process is supported by the model protecting the user’s privacy. The performance analysis for the proposed framework is done in terms of design goal analysis and computation cost analysis.
Deep Learning-Based Text Segmentation in NLP Using Fast Recurrent Neural Network with Bi-LSTM
Authors: Vinotheni.C  , LakshmanaPandian.S
Published: October 2021

Segmentation of Text, the undertaking of partitioning a record obsessed by adjoining sections dependent on its semantic design, is a longstanding test in language understanding. Each segment has its applicable significance. Those segments arranged as phrase, text group, point, express or any data unit relying upon the errand of the content examination. This paper proposes the profound learning-based content segmentation strategies in NLP where the content has been portioned utilizing quick tangled neural organization. We propose a bidirectional LSTM prototype where text group embedding is gotten the hang of utilizing fast RNNs and the phrases are fragmented dependent on context-oriented data. This prototype can consequently deal with variable measured setting data and present an enormous new dataset for text segmentation that is naturally divided. Besides, we build up a segmentation prototype dependent on this dataset and show that it sums up well to inconspicuous regular content. We find that albeit the segmentation precision of FRNN with Bi-LSTM segmentation is advanced than some other segmentation techniques. In the proposed framework, every content is resized obsessed by required size, which is straightforwardly exposed to preparation. That is, each resized text has foreordained and these phrases are taken as fragmented content for preparing the neural organization. The outcomes show that the proposed framework yields great segmentation rates which are practically identical to that of segmentation-based plans for manually written content.
Face Mask Detection Using CNN
Authors: SaiSupriya N  , Priyanka  , RashmiS  , Parthasarathy G
Published: October 2021

The COVID-19 pandemic has become an evolving situation all around the globe and the spread is at an alarming rate. Adapting public health-informed hygiene practices can control the transmission of COVID-19, basic measure being wearing a mask. Face detection is the process of identifying faces in a given input image or video and indicating it by drawing a bounding box around the face. In this paper we introduce a deep learning computer vision model to recognise if a person visible through the camera is wearing a mask or not. The deep learning algorithm administered in our work is a transfer learning based Convolutional Neural Network (CNN) and the face detection task is done using the Viola-Jones algorithm approach.
Efficient Diagnosing Method for Heart Disease Using Deep Learning
Authors: Jayanthi S  , Hajara FathimaS  , IndiraniG  , ReneRobin C.R.
Published: October 2021

Heart Disease(HD) is one of the most serious health issue that attacks people age from 65 and older and has symptoms are palpitations, loss of conscious, abnormal heart beats and it also can attack younger people who has going through lots of stress, over weight and chest pain and so on. Diagnosing heart disease manually is less efficient and mostly not accurate. Machine Learning (ML) helps efficiently in early prediction of Heart Attack. In this paper we have used LSTM (Long Short Term Memory) a Deep Learning Technique to diagonise heart attack. Diagnosing is complicated as it is important task, it needs to be executed accurately and efficiently. This system helps in prediction of HD which used the supervised learning that has low computation.
A Comprehensive Survey on Privacy-Security and Scalability Solutions for Block Chain Technology
Authors: Puneeth R P  , Parthasarathy G
Published: October 2021

Blockchain has various merits such as decentralization, greater transparency and improved traceability. Nowadays blockchain is used at various numbers of applications such as financial related services, business & industry, integrity verification, governance, health and education sectors. Even though the blockchain technology has promising approaches for the building the future of internet systems and also extensive research is going on about the technical challenges. In this paper we presented the comprehensive survey on blockchain and the techniques that can be used to address the scalability issues with respect to storage and also different privacy preserving techniques that can be incorporated during the implementation of blockchain application.
An Efficacious Text Summarization Process Using Triple Encoding-Decoding Mechanism
Authors: Ambeth Kumar V.D. , SubhaShini R. , Priyanka K.
Published: October 2021

As the big data and internet growing huge day by day, there is in need to overwhelm people by the information in large. These kind of issues makes researchers to create a technique that can generate automatic text. This research is very great interesting topic and the summarization of text is the process which undergoes finding important information within a document. Automatic text summarization is the process where a subset is created which represents the relevant content using the original text trained. So, text summarization is an intimidating challenge in Natural Language Processing because getting a good summary is a very big challenge as it is based on various aspects like metrics, non-redundancy, readability etc., At the outset, the text is trained into the system, next the trained text is fed into encoder decoder process where additional techniques are employed between the result in best coherent summary. The aim of automatic text summarization is to the produce finest and top quality text summary and our model also produces supreme quality summary.
Cyber Bullying Scrutiny for Women Security in Social Media
Authors: Madhubala.D  , Rajendiran. M  , Elangovan.D  , Priya.R
Published: October 2021

In recent year, the number of users in social media has been increased multiple times when compared to the past. So most of them are aware about the current affairs in day today life. It is the medium for the user to express the opinion without facing any difficulty. At the same there will be a lot of bullying occurs in social media. The bullying like abuse word, aggressive text or posting some unwanted message. So the women feel unsecured in the society. Although a lot of techniques and methodology has been raised, but still the problem remain same. The major problem is the abuse word can be eliminated by the mean of report to the particular social media. In this methodology the unwanted message can be truncated in between the sender and receiver itself. So there other person cannot be affected in the cyberbullying. Also the unstructured data can be increased in the social media. So it leads to complex for analyzing the text. With the help of sentiment analysis, we can easily filter the unwanted text. Based on the criteria, the paper has been proposed with the problem statement as a main goal and developed a system in machine learning.
Storage Optimization in Cloud Computing Using Discrete Firefly Algorithm to Minimize the Cost
Authors: Magesh Kumar S  , MudassarAli Ahmed , Balasundaram A  , SathishKumar P J , Balaji S
Published: October 2021

Organizations incline to ruminate of data storage as an adjuvant service and do not elevate storage after data is stirred to the cloud. Many also fail to clean up unexploited storage and let these services run for days, weeks, and even months at imperative cost Proposed work offers a broad and foldable arrangement of information stockpiling decisions that move between various layers of capacity and change stockpiling types whenever. Our work likewise examines how to choose capacity benefits that meet information stockpiling wants at the most minimal expense and how to raise these administrations utilizing proposed discrete firefly algorithm to accomplish balance between concert, obtainability, and sturdiness. While basic storage arrangements could check the bytes and even de-duplicate information, they couldn’t figure the business estimation of substance or the danger of losing data. Our Proposed work shows, our storage optimization analytics elucidation is facilitating creativities to better cognize their content and reduce storage expenditures by stirring the precise data to the cloud.
Future Prediction of Cardiovascular Disease Using Deep Learning Technique
Authors: Indhumathi.M  , Ambeth Kumar V.D.
Published: October 2021

Cardiovascular disease is the one of the most leading causes of death. Based on symptoms and risk factors the diagnosis of heart can be done. Predicting the cardiovascular disease in the early stage can save the human being. There is no complete cure which reduces the risk of CVD. Deep learning technique has been used to predict the CVD in a prior stage. Based on the symptoms and risk factors, the CVD has been classified into four types such as No heart disease and no symptoms, Structural Heart Disease without symptoms, Structural Heart Disease with Symptoms and the risk factor for Heart failure are “High blood pressure, high cholesterol, genetic, diabetes, obesity is the major risk factors” to identify the cardiovascular disease and current technique is used to control the risks. To manage all the risk factors Electrocardiography (ECG) method is used to manipulate based on particular situation.
Delineation of Lung Tumor with Neural Networks
Authors: Kalpana R. A  , Ganga A  , Deepika R  , Kiruthiga Devi M  , Ramya N
Published: October 2021

Due to the picture similar region of interest, local area, and the effect of respiration, separating a lung tumor from neighboring tissue from a collection of magnetic resonance images (MRI) faces a number of challenges. However, precise tumor segmentation is needed when preparing radiation therapy in order to prevent excessive radiation exposure to healthy tissues. Delineation of the whole MRI pattern by hand is boring, time-consuming, and costly. Using neural networks, this research studies the automated tracking of tumor borders during radiation therapy. To improve the accuracy of lung tests, we proposed using neural network architecture with fuzzy clustering.
Automatic Bridge Monitoring System Using IoT
Authors: SharmikhaSree.R  , Meera.S  , Deepika.R  , DivyaS  , Priyadarshini V S
Published: October 2021

Monitoring the bridge is very important as a poor bridge may lead to many accidents. Therefore it needs a system to monitor the strength or stability of the bridge in order to avoid any failures. Cracks may occur due to accidents or overload in the bridges which will be identified by the mems sensor. The bridge monitoring system is significant for monitoring bridges or flyovers. The aim is to develop an IOT based bridge monitoring system that will automatically detect the cracks and checks if there is an increase in vibration. Different sensors are used for monitoring. It also monitors the light in the bridge which is very important especially during nighttime. It also checks if the heat of cables in bridges increases which may cause fire and also checks the fire.
Deep Learning for Multi Grade Brain Tumor Classification in Smart Healthcare Systems: A Prospective Survey
Authors: Sravya V J  , Malathi S
Published: October 2021

Cancer is one the dangerous disease for human mankind. There are different types of cancers and the dangerous among them is Brain tumor cancer. Though radilogists are able to diagnose the brain tumor, they find difficulties in identifying grade of brain tumor. In the digitally advanced field, the grade identification of brain tumor still seems a challenging task. There are multiple survey that study the identification of brain tumor. But identifying grade of brain tumor is one area which needs more focus. State of the art convolution network is an key algorithm to identify brain tumor grades. We explore convolution network with transfer learning techniques and data augmentation to achieve more accuracy in detecting appropriate grade of brain tumor. We also detail describe various techniques used prior to our study and how our study enhances from current mechanism.
Bone Cancer Detection Using Feature Extraction with Classification Using K-Nearest Neighbor and Decision Tree Algorithm
Authors: Satheesh Kumar, B  , Sathiyaprasad. B
Published: October 2021

The malignant cells that cannot be controlled from spreading throughout the body is Cancer. Among which the cancer occurs in bone is their type. It is malignant disease occur in bone of human body where their growth cant be controlled from growing. This bone cancer is very critical of all the cancer types since the malignant cells are not identified at their earlier stage and it is the major challenge. Bone cancer is highly common for children and teenagers. For earlier detection of this cancer the correlation of medical imaging has been adapted with image processing and machine learning techniques where maximum accuracy can be obtained similarly even for bone cancer. This paper proposes the detection of bone cancer from the dataset taken from clinical dataset. Here the proposed design comprises of 2 phases in predicting the disorder with higher accuracy. The first stage is extracting the feature of segmented bone image using Gray-Level Co-occurrence Matrix (GLCM) method is applied to extract the features in terms of statistical texture-based and the second phase is classification of extracted feature using K-NN with decision tree algorithm. The simulation results show the enhanced classification results and extracted output with higher accuracy.
Comparative Study of Hybrid Optimizations Technique for On-Chip Interconnect in Multimedia SoCs
Authors: Jayshree  , Seetharaman Gopalakrishnan , Pati Debadatta
Published: October 2021

This paper presents the design and analysis of on-chip interconnect architectures for real time Multimedia Systems-on-Chip (MSoC) targeting Internet of Things (IoT) applications. The interconnect architecture provides high flexibility in connection for hardware implementation of reconfigurable neural network. Due to technology’s miniaturization in ultra-deep submicron technology, the on-chip interconnect performance and power consumption become a bottle-neck. In this paper, the hybrid optimization technique is proposed to address these challenges using schmitt trigger as a repeater and tapering. Here, the proposed optimization technique is incorporated with a dedicated point to point based interconnection (PTP-BI) configuration. A comparative study with others without optimization technique (Model–I) shows the effectiveness of the proposed optimization technique (Model–II). The technology node scaling impacts are also analyzed for both techniques. Finally, the percentage reduction of latency and power consumption are evaluated in two different cases to observe the impacts of varying the interconnect length.
An Effective Denoising and Enhancement Strategy for Medical Image Using Rl-Gl-Caputo Method
Authors: Ganga M. , Janakiraman N. , Kumar Sivaraman Arun , Vincent Rajiv , Muralidhar A. , Ravindran Priya
Published: October 2021

At present, fractional differential is the effective mathematical approach which deals with the factual problems. This projected technique employs the fractional derivatives definitions Riemann-Liouville (R-L), Grunwald-Letnikov (G-L) and the caputo technique for denoising medical image. The presented method based on fractional derivative which in turn improves the quality of image. The input image is processed on integer order method such as pre-processing operation, image conversion and noise image. The fractional differential mask method is to be applied with the help of Riemann Liouville, and Caputo algorithm. After denoising the medical image enhanced using Anisotropic diffusion process and the result is analyzed to finally get denoised and predicted image.
Balanced and Energy Efficient Multi Core-Aware Virtual Machine Placement Algorithm Using Multi-Dimensional Resource Space Partition Model
Authors: Nagadevi  , Raja Kasmir
Published: October 2021

Optimal resource management is required in a data center to allocate the resources to users in a balanced manner. Balanced resource allocation is one of the key challenges in the data center. The multi-dimensional resources of a data center must be allocated in a balanced manner in all the dimensions of physical machines. The unbalanced resource allocation leads to unused residual resource fragments. The unused residual resource fragments leads to resource wastage. If the multi-dimensional data center resources are allocated in a balanced manner, the resource wastage does not occur. Also, the balanced allocation improves the power consumption. The balanced resource allocation reduces the resource wastage as well as reduces the power consumption. In this paper, we have designed a Balanced Energy Efficient Multi-Core Aware Virtual Machine Placement algorithm (MCA-BEE-VMP) using multi-dimensional resource space partition model to balance the resources like CPU and memory and also to reduce the power consumption. We used Google Cloud Jobs (GoCJ) dataset for the simulation. In our simulation of MCA-BEE-VMP using Cloud Sim simulation tool we have achieved balanced CPU and memory resources allocation in two dimensions of a physical machine. The resource wastage and power consumption is improved and the simulation results were analyzed.
LoRaWAN Based Manhole Cover Monitoring System
Authors: Kumar Tiwari Aman , Chaudhari Priyanka , Pattewar Shardul , Deshmukh Rohini
Published: October 2021

An on-line monitoring system using LoRa based wireless technology for manhole cover is proposed. The system includes sensor sensing nodes, LoRaWAN network and application. LoRaWAN based IoT has very low power consumption for long-distance transmission. We use the accelerometer sensor to monitor the position, displacement or damage of manhole covers used in sewage systems. If these covers are moved or damaged, then LoRa board alerts the authorities LoRa gateway. The gateway is connected to The Things Network (TTN), a cloud-based crowd-funded open source LoRaWAN platform. The data is uploaded to the cloud and stored, and it will alert to the maintenance department. On TTN, our application will be launched and integrated with different features such as SMS.
Design of Compact Branch Line Coupler
Authors: Jutru Vijay , Maheswari S
Published: October 2021

The objective is to design a compact branch line coupler to operate at a 2.4GHz frequency. The branch line coupler focuses on -3dB power division with 900 phase difference. T-shaped structure is used to construct the compact branch line coupler. Conventional branch line coupler is also designed and counterfeit using ADS software along with the compact branch line coupler for comparison. The S-parameters for the both coupler are counterfeit and compared. The simulation results of compact branch line coupler had Better return loss, isolation, amplitude and phase difference are all accomplished. Using the proposed method 44% size reduction is achieved. Many microwave applications and integrated circuits, such as amplifier, phase shifters and balanced mixers use branch line couplers.
Improvement of Multipath Channel Performance for Optical MIMO-OFDM in LED Modulation with Fully Generalized Spatial-Frequency
Authors: Nandhini Devi R  , Leones Sherwin VimalrajS  , Lydia J
Published: October 2021

This paper suggests a scheme to generalize the idea of LED index modulation concept by using the spatial multiplexing principle to relay complex OFDM signals through various channels such as AWGN, Rayleigh and Rician by splitting these signals into their real-imaginary and positive-negative components. In order to combat ISI as well as to increase the channel capacity. The MIMO-OFDM efficiency analysis, taking into account the constraint of the forward current of the LED is extracted. The accuracy of the theoretical results is verified by comparing the Bit Error Ratio (BER) reduction and improvement to the (SNR) results under varying condition of the channel. Using MIMO-OFDM as next-generation techniques, along with QAM aims to provide development of new concepts that will lead to the growth of future optical communication. Simulation results validate data rates gained over optical communication using LED modulation scheme and the pure transmission diversity method.
Improved Linearly Constrained Minimum Variance Algorithm for 5G Communications System
Authors: Kaviya K R  , Deepa S
Published: October 2021

Beamforming is a process formulated to produce the radiated beam patterns of the antennas by completely building up the processed signals in the direction of the desired terminals and cancelling beams of interfering signals. Adaptive beamforming is a key technology of smart antenna. The core is to obtain optimum weights of the antenna array by some adaptive beamforming algorithms and finally adjust the main lobe to focus on the arriving direction of the desired signal as well as suppressing the interfering signal. There are several beamforming algorithms that includes Linearly Constrained Minimum Variance (LCMV) algorithm in which Self Nulling Issue is further reduced by adding multiplier to the MCMV algorithm and it is referred as Improved LCMV (IMPLCMV). A Comparative analysis is done for different multipliers and it is found that w=0.15 gives best result with minimum interference of flat response and also self-nulling issues can be reduced.
Scraping and Analysing YouTube Trending Videos for BI
Authors: Sowmiya K  , Supriya S  , Subhashini R.
Published: October 2021

Analysis of structured data has seen tremendous success in the past. However, large scale of an unstructured data have been analysed in the form of video format remains a challenging area. YouTube, a Google company, has over a billion users and it used to generate billions of views. Since YouTube data is getting created in a very huge amount with a lot of views and with an equally great speed, there is a huge demand to store the data, process the data and carefully study the data in this large amount of it usable. The project utilizes the YouTube Data API (Application Programming Interface) which allows the applications or websites to incorporate functions in which they are used by YouTube application to fetch and view the information. The Google Developers Console which is used to generate an unique access key which is further required to fetch the data from YouTube public channel. Process the data and finally data stored in AWS. This project extracts the meaningful output of which can be used by the management for analysis, these methodologies helpful for business intelligence.
Tomato Leaf Disease Detection Using Hybrid CNN-RNN Model
Authors: David Hepzibah Elizabeth , Ramalakshmi K. , Venkatesan R. , Hemalatha G.
Published: October 2021

Tomato crops are infected with various diseases that impair tomato production. The recognition of the tomato leaf disease at an early stage protects the tomato crops from getting affected. In the present generation, the emerging deep learning techniques Convolutional Neural Network (CNNs), Recurrent Neural Network (RNNs), Long-Short Term Memory (LSTMs) has manifested significant progress in image classification, image identification, and Sequence Predictions. Thus by using these computer vision-based deep learning techniques, we developed a new method for automatic leaf disease detection. This proposed model is a robust technique for tomato leaf disease identification that gives accurate and better results than other traditional methods. Early tomato leaf disease detection is made possible by using the hybrid CNN-RNN architecture which utilizes less computational effort. In this paper, the required methods for implementing the disease recognition model with results are briefly explained. This paper also mentions the scope of developing more reliable and effective means of classifying and detecting all plant species.
MRI Image Classification of Brain Tumor Using Deep Neural Network and Deployment using Web Framework
Authors: Sumithra M  , Shruthi S  , SmithiRam  , Swathi S  , Deepika T
Published: October 2021

A brain tumor is a mass or growth of abnormal cells in our brain. Many different types of brain tumors exist. Some brain tumors are noncancerous (benign), and some brain tumors are cancerous (malignant). Brain tumors can begin in your brain (primary brain tumors), or cancer can begin in other parts of your body and spread to your brain (secondary, or metastatic, brain tumors). Brain tumor treatment options depend on the type of brain tumor you have, as well as its size and location. The classification of brain tumors is performed by biopsy, which is not usually conducted before definitive brain surgery. The improvement of technology and machine learning can help radiologists in tumor diagnostics without invasive measures. A machine-learning algorithm that has achieved substantial results in image classification is the convolution neural network (CNN). It is predicted that the success of the obtained results will increase if the CNN method is supported by adding extra feature extraction methods and classify successfully brain tumor normal and abnormal image.
Performance comparison of the PFA-200 and Anysis-200: Assessment of bleeding risk screening in cardiology patients
Authors: Piao Jinxiang , Yoo Chaeyoung , Kim SunYoung , Whang Youn-Wha , Choi Cheol Ung , Shin Sehyun
Published: October 2021

BACKGROUND: Assessment of platelet function is important in the management of patients who are subject to operation as well as at potential risk of hemorrhagic complications. OBJECTIVE: This study aimed to evaluate a new platelet assays (Anysis-Epinephrine, Anysis-ADP) and to compare them with PFA-200 in cardiology visiting patients and inpatients. METHODS: Citrated blood samples were collected from 184 patients for ADP test and 163 patients for EPI test, who visited Korea University Guro Hospital with written consent. The PFA-200 assay gives a test result the closure time (CT) until the blood flow rate decreases to 10% of the initial value, whereas Anysis-200 assay does a blood flow migration distance (MD) until blood flow completely stops. According to the results of PFA closure time (CT), the tested samples were classified as either negative control or positive group. The measurements were simultaneously conducted with two devices and compared. RESULTS: The sensitivity and specificity of Anysis-200 C/EPI kit in comparison to PFA-200 C/EPI kit was 87.5% and 85.7%, respectively. Regarding C/ADP kit, the sensitivity and specificity of Anysis-200 was 96.9% and 87.5%, respectively. In addition, the sums of sensitivity and specificity are greater than 150% for both of EPI and ADP. Also, it was found that likelihood ratio and odd ratio for each assay provide useful additional information. Since the Cohen’s kappa coefficients value between the two devices was relatively high, the equivalence between the two devices was confirmed. CONCLUSIONS: Anysis-200, a novel platelet function analyzer has showed excellent agreements with PFA-200 with high agreement rates and precision. Anysis-200 assay would be useful in assessing bleeding risk management as well as abnormal platelet reactivity at point of care.
Crowdsourcing transcription of historical manuscripts: Citizen science as a force of revealing historical evidence from Croatian Glagolitic manuscripts
Authors: Tomić Marijana , Grzunov Laura , Dragija Ivanović Martina
Published: October 2021

This paper is a report on the project Civil Science in the Field of Glagolitics: from crowdsourcing to knowledge and it describes its first phase. The project is being conducted by the scientific Centre for Research in Glagolitism of the University of Zadar, Croatia, from 2021 to 2022. The researchers come from the Centre, as well as from the Department of Information Sciences of the University of Zadar and State Archive in Zadar, Croatia. The main objective of the project is to examine the possibilities and benefits of citizen participation in the scholarly projects in humanities, particularly the projects whose object of research are manuscripts written in historical script that present a valuable source for local history. The term historical script refers to a script that is not used nowadays as an official script in any country or community but was in use a particular period of history on a certain territory. The corpus for the pilot study conducted within this project consists of manuscripts and their fragments written in cursive form of the Croatian Glagolitic script. Glagolitic script is the oldest known Slavic script, introduced in the 9th century and being used in Croatia up until the 19th century, simultaneously with Latin and Cyrillic scripts. The citizen participation is researched on the example of crowdsourcing transcription of manuscripts written in cursive form of the Croatian Glagolitic script. In the first phase of the project, the pilot study was conducted. The aim of the pilot study presented in this paper is to create a solid basis for involving the public in scientific projects within the disciplines of humanities whose object of research are documents written in historical scripts, namely within the field of the Croatian Glagolitics.
Space Debris Mitigation: Some Lessons from International Environmental Law
Authors: Hosseini Seyed Mohammad , Fathpour Fatemeh , Chanda Subhrajit
Published: October 2021

The Soviet Union successfully launched Sputnik I in 1957 which led to the era of space activities. Although human race has benefited numerous from space activities, unlimited use of outer space has caused pollutions in outer space and consequently at the earth environment. Space debris has become a threat to the security of space activities. Space debris is the most important of these pollutions that, not only creates numerous threats and risks for Orbiting Satellites, It also has harmful effects on earth environment. During drafting UN space treaties, little attention was paid to environmental problems and these treaties did not mentioned of space debris and its hazards. in recent decades, Ethics of Outer Space activities, paid more attention to the environment of outer space and environmental issue of space activities. Therefore, the experiences of environmental law and its preventive policies can be used to reduce the threat of space debris for peaceful space activities and the environment of space and planet Earth.
Matrix factorization completed multicontext data for tensor-enhanced recommendation
Authors: Deng Shangju , Qin Jiwei
Published: October 2021

Tensors have been explored to share latent user-item relations and have been shown to be effective for recommendation. Tensors suffer from sparsity and cold start problems in real recommendation scenarios; therefore, researchers and engineers usually use matrix factorization to address these issues and improve the performance of recommender systems. In this paper, we propose matrix factorization completed multicontext data for tensor-enhanced algorithm a using matrix factorization combined with a multicontext data method for tensor-enhanced recommendation. To take advantage of existing user-item data, we add the context time and trust to enrich the interactive data via matrix factorization. In addition, Our approach is a high-dimensional tensor framework that further mines the latent relations from the user-item-trust-time tensor to improve recommendation performance. Through extensive experiments on real-world datasets, we demonstrated the superiority of our approach in predicting user preferences. This method is also shown to be able to maintain satisfactory performance even if user-item interactions are sparse.
A Patient-Centered Conceptual Model of Symptoms and Their Impact in Early Parkinson’s Disease: A Qualitative Study
Authors: Staunton Hannah , Kelly Kim , Newton Louise , Leddin Mathias , Rodriguez-Esteban Raul , Chaudhuri K. Ray , Weintraub Daniel , Postuma Ronald B. , Martinez-Martin Pablo
Published: October 2021

Background: Individuals with Parkinson’s disease (PD) develop a significant disease burden over time that contributes to a progressive decline in health-related quality of life. There is a paucity of qualitative research to understand symptoms and impacts in individuals with early-stage PD (i.e., Hoehn and Yahr stage 1–2 and ≤2 years since diagnosis). Objective: The collection of qualitative data to inform the selection of clinical outcome assessments for clinical trials is advocated by regulators. This patient-centered, multistage study sought to create a conceptual model of symptoms and their impact for individuals with early-stage PD. Methods: Symptoms and impacts of PD were gathered from a literature review of qualitative research, a quantitative social media listening analysis, and qualitative patient concept elicitation interviews (n = 35). Clinical experts provided input to validate and finalize the concepts. Results: The final conceptual model consisted of 27 symptoms categorized into ‘motor’ or ‘non-motor’ domains, and 39 impacts divided into five domains. Most frequently reported symptoms in early-stage PD were ‘tremors’ (89%), ‘stiffness and rigidity’, and ‘fatigue’ (69%, both). Most frequently reported impacts included ‘anxiety’ (74%), ‘eating and drinking’ (71%), followed by ‘exercise/sport’ and ‘relationship with family/family life’ (66%, both). Conclusion: This study provides initial insights relating to the symptom and impact burden of early-stage PD patients. The conceptual model can be used to help researchers to develop and select optimal patient-centered outcomes to measure treatment benefit in clinical trials. These findings could inform future qualitative research and the development of outcomes specifically for early-stage PD patients.
Chemical analysis of essential oil component, perfume and synthetic essential oil of narcissus and its harmful compounds
Authors: Azizi Kosar , Konoz Elaheh
Published: October 2021

Within this work, tazetta, one of the fragrant narcissus species used in the perfume industry, was extracted by steam distillation. The extracts were chemically analyzed by gas chromatography coupled with a mass spectrometer (GC/MS). All substances of perfume, synthetic essence, and essential oil of narcissus flower were prepared and their constituents were identified and compared based on GC/MS results. Their harmful compounds were also identified using MSDS and LD50 methods. According to the obtained information of MSDS and LD50, essential oil of flower, perfume and synthetic essence all contain harmful compounds with many side effects to be considered with extra care for the human health.
Effect of low-cost transcranial magnetic stimulation navigation on hotspot targeting and motor evoked potential variability in the biceps brachii
Authors: Roumengous Thibault , Reutter Alec B. , Peterson Carrie L.
Published: October 2021

Background:

Transcranial magnetic stimulation (TMS) can monitor or modulate brain excitability. However, reliability of TMS outcomes depends on consistent coil placement during stimulation. Neuronavigated TMS systems can address this issue, but their cost limits their use outside of specialist research environments.

Objective:

The objective was to evaluate the performance of a low-cost navigated TMS approach in improving coil placement consistency and its effect on motor evoked potentials (MEPs) when targeting the biceps brachii at rest and during voluntary contractions.

Methods:

We implemented a navigated TMS system using a low-cost 3D camera system and open-source software environment programmed using the Unity 3D engine. MEPs were collected from the biceps brachii at rest and during voluntary contractions across two sessions in ten non-disabled individuals. Motor hotspots were recorded and targeted via two conditions: navigated and conventional.

Results:

The low-cost navigated TMS system reduced coil orientation error (pitch: 1.18°±1.2°, yaw: 1.99°±1.9°, roll: 1.18°±2.2° with navigation, versus pitch: 3.7°±5.7°, yaw: 3.11°±3.1°, roll: 3.8°±9.1° with conventional). The improvement in coil orientation had no effect on MEP amplitudes and variability.

Conclusions:

The low-cost system is a suitable alternative to expensive systems in tracking the motor hotspot between sessions and quantifying the error in coil placement when delivering TMS. Biceps MEP variability reflects physiological variability across a range of voluntary efforts, that can be captured equally well with navigated or conventional approaches of coil locating.
Comprehensive analysis of resting tremor based on acceleration signals of patients with Parkinson’s disease
Authors: Liu Sen , Yuan Han , Liu Jiali , Lin Hai , Yang Cuiwei , Cai Xiaodong
Published: October 2021

BACKGROUND: Resting tremor is an essential characteristic in patients suffering from Parkinson’s disease (PD). OBJECTIVE: Quantification and monitoring of tremor severity is clinically important to help achieve medication or rehabilitation guidance in daily monitoring. METHODS: Wrist-worn tri-axial accelerometers were utilized to record the long-term acceleration signals of PD patients with different tremor severities rated by Unified Parkinson’s Disease Rating Scale (UPDRS). Based on the extracted features, three kinds of classifiers were used to identify different tremor severities. Statistical tests were further designed for the feature analysis. RESULTS: The support vector machine (SVM) achieved the best performance with an overall accuracy of 94.84%. Additional feature analysis indicated the validity of the proposed feature combination and revealed the importance of different features in differentiating tremor severities. CONCLUSION: The present work obtains a high-accuracy classification in tremor severity, which is expected to play a crucial role in PD treatment and symptom monitoring in real life.
Improving accuracy for measuring the ship's magnetic moment in the far field
Authors: Zateev Aleksei A. , Ivanov Yury M. , Semenov Valentin G.
Published: October 2021

The magnetic moment of the ship is measured to control its protection from magnetic mines and to control the stealth by the magnetic field. A modification of the measurement method using a three-component differential magnetometer is considered. Using computer simulation, the errors of the new method are compared with those of the known analogues and its advantage in accuracy is established (almost by an order of magnitude). It is shown that the new method can also be used for magnetic models of ships and standard samples of magnetic materials to determine their components of the hysteresis loops by magnetization.
The global navigation systems satellites signals partial powers measuring method
Authors: Zavgorodnii Aleksei S.
Published: October 2021

Different consumers of global navigation satellite systems signals are able to receive different combinations of them. For this reason, it is important to monitor the signal parameters separately. The existing partial signals characteristics measuring methods are based on correlation processing, which leads to the loss of measurement information and signal averaging. A method for measuring the partial powers of navigation signals with frequency and code division was developed. The initial data are the total spectral power density measurements results, as well as information on the shape of the partial spectral power densities. A mathematical model of the sum of signals with overlapping spectra was investigated. It was shown that the signal powers were related as coefficients at partial spectral power densities. A system of equations for the coefficients is compiled, its solution is presented by the Tikhonov method. A practical study of the metrological characteristics of the method has been carried out. The research results will be used during monitoring the navigation spacecraft technical condition.