SF-CNN: Deep Text Classification and Retrieval for Text Documents

Researchers and scientists need rapid access to text documents such as research papers,source code and dissertations.Many research documents are available on the Internet and need more time to retrieve exact documents based on keywords.An efficient classification algorithm for retrieving documents based on keyword words is required.The traditional algorithm performs less because it never considers words’polysemy and the relationship between bag-of-words in keywords.To solve the above problem,Semantic Featured Convolution Neural Networks(SF-CNN)is proposed to obtain the key relationships among the searching keywords and build a structure for matching the words for retrieving correct text documents.The proposed SF-CNN is based on deep semantic-based bag-of-word representation for document retrieval.Traditional deep learning methods such as Convolutional Neural Network and Recurrent Neural Network never use semantic representation for bag-of-words.The experiment is performed with different document datasets for evaluating the performance of the proposed SF-CNN method.SF-CNN classifies the documents with an accuracy of 94%than the traditional algorithms.

An Imbalanced Dataset and Class Overlapping Classification Model for Big Data

Most modern technologies,such as social media,smart cities,and the internet of things(IoT),rely on big data.When big data is used in the real-world applications,two data challenges such as class overlap and class imbalance arises.When dealing with large datasets,most traditional classifiers are stuck in the local optimum problem.As a result,it’s necessary to look into new methods for dealing with large data collections.Several solutions have been proposed for overcoming this issue.The rapid growth of the available data threatens to limit the usefulness of many traditional methods.Methods such as oversampling and undersampling have shown great promises in addressing the issues of class imbalance.Among all of these techniques,Synthetic Minority Oversampling TechniquE(SMOTE)has produced the best results by generating synthetic samples for the minority class in creating a balanced dataset.The issue is that their practical applicability is restricted to problems involving tens of thousands or lower instances of each.In this paper,we have proposed a parallel mode method using SMOTE and MapReduce strategy,this distributes the operation of the algorithm among a group of computational nodes for addressing the aforementioned problem.Our proposed solution has been divided into three stages.Thefirst stage involves the process of splitting the data into different blocks using a mapping function,followed by a pre-processing step for each mapping block that employs a hybrid SMOTE algo-rithm for solving the class imbalanced problem.On each map block,a decision tree model would be constructed.Finally,the decision tree blocks would be com-bined for creating a classification model.We have used numerous datasets with up to 4 million instances in our experiments for testing the proposed scheme’s cap-abilities.As a result,the Hybrid SMOTE appears to have good scalability within the framework proposed,and it also cuts down the processing time.

Suicide Ideation Detection of Covid Patients Using Machine Learning Algorithm

During Covid pandemic,many individuals are suffering from suicidal ideation in the world.Social distancing and quarantining,affects the patient emotionally.Affective computing is the study of recognizing human feelings and emotions.This technology can be used effectively during pandemic for facial expression recognition which automatically extracts the features from the human face.Monitoring system plays a very important role to detect the patient condition and to recognize the patterns of expression from the safest distance.In this paper,a new method is proposed for emotion recognition and suicide ideation detection in COVID patients.This helps to alert the nurse,when patient emotion is fear,cry or sad.The research presented in this paper has introduced Image Processing technology for emotional analysis of patients using Machine learning algorithm.The proposed Convolution Neural Networks(CNN)architecture with DnCNN preprocessing enhances the performance of recognition.The system can analyze the mood of patients either in real time or in the form of video files from CCTV cameras.The proposed method accuracy is more when compared to other methods.It detects the chances of suicide attempt based on stress level and emotional recognition.

A Novel Approach to Design Distribution Preserving Framework for Big Data

In several fields like financial dealing,industry,business,medicine,et cetera,Big Data(BD)has been utilized extensively,which is nothing but a collection of a huge amount of data.However,it is highly complicated along with time-consuming to process a massive amount of data.Thus,to design the Distribution Preserving Framework for BD,a novel methodology has been proposed utilizing Manhattan Distance(MD)-centered Partition Around Medoid(MD–PAM)along with Conjugate Gradient Artificial Neural Network(CG-ANN),which undergoes various steps to reduce the complications of BD.Firstly,the data are processed in the pre-processing phase by mitigating the data repetition utilizing the map-reduce function;subsequently,the missing data are handled by substituting or by ignoring the missed values.After that,the data are transmuted into a normalized form.Next,to enhance the classification performance,the data’s dimensionalities are minimized by employing Gaussian Kernel(GK)-Fisher Discriminant Analysis(GK-FDA).Afterwards,the processed data is submitted to the partitioning phase after transmuting it into a structured format.In the partition phase,by utilizing the MD-PAM,the data are partitioned along with grouped into a cluster.Lastly,by employing CG-ANN,the data are classified in the classification phase so that the needed data can be effortlessly retrieved by the user.To analogize the outcomes of the CG-ANN with the prevailing methodologies,the NSL-KDD openly accessible datasets are utilized.The experiential outcomes displayed that an efficient result along with a reduced computation cost was shown by the proposed CG-ANN.The proposed work outperforms well in terms of accuracy,sensitivity and specificity than the existing systems.

Neural Network Based Normalized Fusion Approaches for Optimized Multimodal Biometric Authentication Algorithm

A multimodal biometric system is applied to recognize individuals for authentication using neural networks. In this paper multimodal biometric algorithm is designed by integrating iris, finger vein, palm print and face biometric traits. Normalized score level fusion approach is applied and optimized, encoded for matching decision. It is a multilevel wavelet, phase based fusion algorithm. This robust multimodal biometric algorithm increases the security level, accuracy, reduces memory size and equal error rate and eliminates unimodal biometric algorithm vulnerabilities.

Analysis of Reduced Switch Topology Multilevel Inverter with Different Pulse Width Modulation Technique and Its Application with DSTATCOM

Multilevel inverter has played a vital role in medium and high power applications in the recent years. In this paper, Reduced Switch Count Multi Level Inverter structure (RSCMLI) topology is presented with different pulse width modulation techniques. The harmonic level analysis is carried out for the reduced switch count multilevel inverter with the different PWM technique such as with Alternate Phase Opposition Disposition (APOD) method, In Phase Disposition (IPD) method and multi reference pulse width modulation method for five level, seven level , nine level and eleven level inverter. The simulation results are compared with the cascaded H Bridge Multi Level Inverter (CHBMLI). The nine level RSCMLI inverter with APOD method is used for the Distribution Static Synchronous Compensator (DSTATCOM) application in the nonlinear load connected system for power factor improvement. The result shows that the harmonic level and the number of switches required for RSCMLI is reduced compared to CHBMLI. RSCMLI employed in DSTATCOM improves the power factor and harmonic level of the system when it is connected to the nonlinear load.

Diagnosis of Neem Leaf Diseases Using Fuzzy-HOBINM and ANFIS Algorithms

This paper proposes an approach to detecting diseases in neem leaf that uses a Fuzzy-Higher Order Biologically Inspired Neuron Model(F-HOBINM)and adaptive neuro classifier(ANFIS).India exports USD 0.28-million worth of neem leaf to the UK,USA,UAE,and Europe in the form of dried leaves and powder,both of which help reduce diabetesrelated issues,cardiovascular problems,and eye disorders.Diagnosing neem leaf disease is difficult through visual interpretation,owing to similarity in their color and texture patterns.The most common diseases include bacterial blight,Colletotrichum and Alternaria leaf spot,blight,damping-off,powdery mildew,Pseudocercospora leaf spot,leaf web blight,and seedling wilt.However,traditional color and texture algorithms fail to identify leaf diseases due to irregular lumps and surfaces,and rough ridges,as the classification time involved takes as long as a week.The proposed F-HOBINM algorithm recognizes the leaf intensity through the leaky capacitor,and uses subjective intensity and physical stimulus to interpret the diagnosis.Further,the processed leaf images from the HOBINM algorithm are applied to the ANFIS classifier to identify neem leaf diseases.The experimental results show 92.18%accuracy from a database of 1,462 neem leaves.

Modified Mackenzie Equation and CVOA Algorithm Reduces Delay in UASN

In Underwater Acoustic Sensor Network(UASN),routing and propagation delay is affected in each node by various water column environmental factors such as temperature,salinity,depth,gases,divergent and rotational wind.High sound velocity increases the transmission rate of the packets and the high dissolved gases in the water increases the sound velocity.High dissolved gases and sound velocity environment in the water column provides high transmission rates among UASN nodes.In this paper,the Modified Mackenzie Sound equation calculates the sound velocity in each node for energy-efficient routing.Golden Ratio Optimization Method(GROM)and Gaussian Process Regression(GPR)predicts propagation delay of each node in UASN using temperature,salinity,depth,dissolved gases dataset.Dissolved gases,rotational and divergent winds,and stress plays a major problem in UASN,which increases propagation delay and energy consumption.Predicted values from GPR and GROM leads to node selection and Corona Virus Optimization Algorithm(CVOA)routing is performed on the selected nodes.The proposed GPR-CVOA and GROM-CVOA algorithm solves the problem of propagation delay and consumes less energy in nodes,based on appropriate tolerant delays in transmitting packets among nodes during high rotational and divergent winds.From simulation results,CVOA Algorithm performs better than traditional DF and LION algorithms.

A Secure IoT-Cloud Based Healthcare System for Disease Classification Using Neural Network

The integration of the Internet of Things(IoT)and cloud computing is the most popular growing technology in the IT world.IoT integrated cloud com-puting technology can be used in smart cities,health care,smart homes,environ-mental monitoring,etc.In recent days,IoT integrated cloud can be used in the health care system for remote patient care,emergency care,disease prediction,pharmacy management,etc.but,still,security of patient data and disease predic-tion accuracy is a major concern.Numerous machine learning approaches were used for effective early disease prediction.However,machine learning takes more time and less performance while classification.In this research work,the Attribute based Searchable Honey Encryption with Functional Neural Network(ABSHE-FNN)framework is proposed to analyze the disease and provide stronger security in IoT-cloud healthcare data.In this work,the Cardiovascular Disease and Pima Indians diabetes dataset are used for heart and diabetic disease classification.Initi-ally,means-mode normalization removes the noise and normalizes the IoT data,which helps to enhance the quality of data.Rectified Linear Unit(RLU)was applied to adjust the feature weight to reduce the training cost and error classifi-cation.This proposed ABSHE-FNN technique provides better security and achieves 92.79%disease classification accuracy compared to existing techniques.

Influence of Quantal and Statistical Fluctuations on Phase Transitions in Finite Nuclei

Investigations on thermal evolution of pairing-phase transition and shape-phase transition in light nuclei are made as a function of pair gap, deformation, temperature and angular momentum using a finite temperature statistical approach with main emphasis to fluctuations. The occurrence of a peak structure in the specific heat predicted as signals of the pairing-phase and shape-phase transitions are reviewed and it is found that they are not actually true phase transitions and it is only an artifact of the mean field models. Since quantal number and spin fluctuations and statistical fluctuations in pair gap, deformation degrees of freedom and energy when incorporated, it wash out the pairing-phase transition and smooth out the shape-phase transition. Phase transitions due to collapse of pair gap and deformation is discussed and a clear picture of pairing-phase transition in light nuclei is presented in which pairing transition is reconciled.

Deep Learning Framework for the Prediction of Childhood Medulloblastoma

This research work develops new and better prognostic markers for predicting Childhood MedulloBlastoma(CMB)using a well-defined deep learning architecture.A deep learning architecture could be designed using ideas from image processing and neural networks to predict CMB using histopathological images.First,a convolution process transforms the histopathological image into deep features that uniquely describe it using different two-dimensional filters of various sizes.A 10-layer deep learning architecture is designed to extract deep features.The introduction of pooling layers in the architecture reduces the feature dimension.The extracted and dimension-reduced deep features from the arrangement of convolution layers and pooling layers are used to classify histopathological images using a neural network classifier.The performance of the CMB classification system is evaluated using 1414(10×magnification)and 1071(100×magnification)augmented histopathological images with five classes of CMB such as desmoplastic,nodular,large cell,classic,and normal.Experimental results show that the average classification accuracy of 99.38%(10×)and 99.07%(100×)is attained by the proposed CNB classification system.

Haemoglobin Measurement from Eye Anterior Ciliary Arteries through Borescope Camera

Nowadays,smartphones are used as self-health monitoring devices for humans.Self-health monitoring devices help clinicians with big data for accurate diagnosis and guidance for treatment through repetitive measurement.Repetitive measurement of haemoglobin requires for pregnant women,pediatric,pulmonary hypertension and obstetric patients.Noninvasive haemoglobin measurement through conjunctiva leads to inaccurate measurement.The inaccuracy is due to a decrease in the density of goblet cells and acinar units in Meibomian glands in the human eye as age increases.Furthermore,conjunctivitis is a disease in the eye due to inflammation or infection at the conjunctiva.Conjunctivitis is in the form of lines in the eyelid and covers the white part of the eyeball.Moreover,small blood vessels in eye regions of conjunctiva inflammations are not visible to the human eye or standard camera.This paper proposes smartphone-based hae-moglobin(SBH)measurement through a borescope camera from anterior ciliary arteries of the eye for the above problem.The proposed SBH method acquires images from the anterior ciliary arteries region of the eye through a smartphone attached with a high megapixel borescope camera.The anterior ciliary arteries are projected through transverse dyadic wavelet transform(TDyWT)and applied with delta segmentation to obtain blood cells from the ciliary arteries of the eye.Furthermore,the Gaussian regression algorithm measures haemoglobin(Hb)with more accuracy based on the person,eye arteries,red pixel statistical parameters obtained from the left and right eye,age,and weight.Furthermore,the experimen-tal result of the proposed SBH method has an accuracy of 96%in haemoglobin measurement.

Spatio Temporal Tourism Tracking System Based on Adaptive Convolutional Neural Network

Technological developments create a lot of impacts in the tourism industry.Emerging big data technologies and programs generate opportunities to enhance the strategy and results for transport security.However,there is a difference between technological advances and their integration into the methods of tourism study.The rising popularity of Freycinet National Park led to a master plan that would not address cultural and environmental issues.This study addresses the gap by using a synthesized application(app)for demographic surveys and Global Navigation Satellite System(GNSS)technology to implement research processes.This article focuses on managing visitors within the famous Freycinet National Park.Extremely comprehensive structured data were analyzed in three phases,(1)identifying groups of visitors who are more likely to use the walking trails,(2)those who are more and less likely to visit during/peak crowding times,and(3)finally creating an integrated Spatio-temporal dependency model via a machine-based learning system for real-time activity.This research examines innovative techniques that can offer energy resources to managers and tourism agencies,especially in detecting,measuring,and potentially relieving crowding and over-tourism.

An Optimized Deep-Learning-Based Low Power Approximate Multiplier Design

Approximate computing is a popularfield for low power consumption that is used in several applications like image processing,video processing,multi-media and data mining.This Approximate computing is majorly performed with an arithmetic circuit particular with a multiplier.The multiplier is the most essen-tial element used for approximate computing where the power consumption is majorly based on its performance.There are several researchers are worked on the approximate multiplier for power reduction for a few decades,but the design of low power approximate multiplier is not so easy.This seems a bigger challenge for digital industries to design an approximate multiplier with low power and minimum error rate with higher accuracy.To overcome these issues,the digital circuits are applied to the Deep Learning(DL)approaches for higher accuracy.In recent times,DL is the method that is used for higher learning and prediction accuracy in severalfields.Therefore,the Long Short-Term Memory(LSTM)is a popular time series DL method is used in this work for approximate computing.To provide an optimal solution,the LSTM is combined with a meta-heuristics Jel-lyfish search optimisation technique to design an input aware deep learning-based approximate multiplier(DLAM).In this work,the jelly optimised LSTM model is used to enhance the error metrics performance of the Approximate multiplier.The optimal hyperparameters of the LSTM model are identified by jelly search opti-misation.Thisfine-tuning is used to obtain an optimal solution to perform an LSTM with higher accuracy.The proposed pre-trained LSTM model is used to generate approximate design libraries for the different truncation levels as a func-tion of area,delay,power and error metrics.The experimental results on an 8-bit multiplier with an image processing application shows that the proposed approx-imate computing multiplier achieved a superior area and power reduction with very good results on error rates.

Prediction of Link Failure in MANET-IoT Using Fuzzy Linear Regression

A Mobile Ad-hoc NETwork(MANET)contains numerous mobile nodes,and it forms a structure-less network associated with wireless links.But,the node movement is the key feature of MANETs;hence,the quick action of the nodes guides a link failure.This link failure creates more data packet drops that can cause a long time delay.As a result,measuring accurate link failure time is the key factor in the MANET.This paper presents a Fuzzy Linear Regression Method to measure Link Failure(FLRLF)and provide an optimal route in the MANET-Internet of Things(IoT).This work aims to predict link failure and improve routing efficiency in MANET.The Fuzzy Linear Regression Method(FLRM)measures the long lifespan link based on the link failure.The mobile node group is built by the Received Signal Strength(RSS).The Hill Climbing(HC)method selects the Group Leader(GL)based on node mobility,node degree and node energy.Additionally,it uses a Data Gathering node forward the infor-mation from GL to the sink node through multiple GL.The GL is identified by linking lifespan and energy using the Particle Swarm Optimization(PSO)algo-rithm.The simulation results demonstrate that the FLRLF approach increases the GL lifespan and minimizes the link failure time in the MANET.

Design of Robust Controller for LFC of Interconnected Power System Considering Communication Delays

The usage of open communication infrastructure for transmitting the control signals in the Load Frequency Control (LFC) scheme of power system introduces time delays. These time delays may degrade the dynamic performance of the power system. This paper proposes a robust method to design a controller for multi-area LFC schemes considering communication delays. In existing literature, the controller values of LFC are designed using time domain approach which is less accurate than the proposed method. In proposed method, the controller values are determined by moving the rightmosteigenvalues of the system to the left half plane in a quasi-continuous way for a preset upper bound of time delay. Then the robustness of the proposed controller is assessed by estimating the maximumtolerable value of time delay for maintaining system stability. Simulation studies are carried out for multi-area LFC scheme equipped with the proposed controller using Matlab/simulink. From the results, it has been concluded that the proposed controller guarantees the tolerance for all time delays smaller than the preset upper bound and provides a bigger delay margin than the existing controllers.

Secured Data Transmission Using Modified LEHS Algorithm in Wireless Sensor Network

In the ancient block Hill cipher, the cipher text is obtained by multiplying the blocks of the plain text with the key matrix. To strengthen the keymatrix, a double guard Hill cipher was proposed with two key matrices, a private key matrix and its modified key matrix along with permutation. In the ancient block Hill cipher, the cipher text is obtained by multiplying the blocks of the plain text with the key matrix. To strengthen the key matrix, a double guard Hill cipher was proposed with two key matrices, a private key matrix and its modified key matrix along with permutation. In this paper a novel modification is performed to the double guard Hill cipher in order to reduce the number of calculation to obtain the cipher text by using non-square matrices. This modified double guard Hill cipher uses a non-square matrix of order (p × q) as its private keymatrix.

Medical Image Compression Using Wrapping Based Fast Discrete Curvelet Transform and Arithmetic Coding

Due to the development of CT (Computed Tomography), MRI (Magnetic Resonance Imaging), PET (Positron Emission Tomography), EBCT (Electron Beam Computed Tomography), SMRI (Stereotactic Magnetic Resonance Imaging), etc. has enhanced the distinguishing rate and scanning rate of the imaging equipments. The diagnosis and the process of getting useful information from the image are got by processing the medical images using the wavelet technique. Wavelet transform has increased the compression rate. Increasing the compression performance by minimizing the amount of image data in the medical images is a critical task. Crucial medical information like diagnosing diseases and their treatments is obtained by modern radiology techniques. Medical Imaging (MI) process is used to acquire that information. For lossy and lossless image compression, several techniques were developed. Image edges have limitations in capturing them if we make use of the extension of 1-D wavelet transform. This is because wavelet transform cannot effectively transform straight line discontinuities, as well geographic lines in natural images cannot be reconstructed in a proper manner if 1-D transform is used. Differently oriented image textures are coded well using Curvelet Transform. The Curvelet Transform is suitable for compressing medical images, which has more curvy portions. This paper describes a method for compression of various medical images using Fast Discrete Curvelet Transform based on wrapping technique. After transformation, the coefficients are quantized using vector quantization and coded using arithmetic encoding technique. The proposed method is tested on various medical images and the result demonstrates significant improvement in performance parameters like Peak Signal to Noise Ratio (PSNR) and Compression Ratio (CR).