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Cephalometric analysis is used to identify problems in the development of the skull, evaluate their treatment, and plan for possible surgical interventions. The paper aims to develop a Convolutional Neural Network that will analyze the head position on an X-ray image. It takes place in such a way that it recognizes whether the image is suitable and, if not, suggests a change in the position of the head for correction. This paper addresses the exact rotation of the head with a change in the range of a few degrees of rotation. The objective is to predict the correct head position to take an X-ray image for further Cephalometric analysis. The changes in the degree of rotations were categorized into 5 classes. Deep learning models predict the correct head position for Cephalometric analysis. An X-ray image dataset on the head is generated using CT scan images. The generated images are categorized into 5 classes based on a few degrees of rotations. A set of four deep-learning models were then used to generate the generated X-Ray images for analysis. This research work makes use of four CNN-based networks. These networks are trained on a dataset to predict the accurate head position on generated X-Ray images for analysis. Two networks of VGG-Net, one is the U-Net and the last is of the ResNet type. The experimental analysis ascertains that VGG-4 outperformed the VGG-3, U-Net, and ResNet in estimating the head position to take an X-ray on a test dataset with a measured accuracy of 98%. It is due to the incorrectly classified images are classified that are directly adjacent to the correct ones at intervals and the misclassification rate is significantly reduced. [ABSTRACT FROM AUTHOR]

Breast cancer is the second leading cause of mortality among women. Early and accurate detection plays a crucial role in lowering its mortality rate. Timely detection and classification of breast cancer enable the most effective treatment. Convolutional neural networks (CNNs) have significantly improved the accuracy of tumor detection and classification in medical imaging compared to traditional methods. This study proposes a comprehensive classification technique for identifying breast cancer, utilizing a synthesized CNN, an enhanced optimization algorithm, and transfer learning. The primary goal is to assist radiologists in rapidly identifying anomalies. To overcome inherent limitations, we modified the Ant Colony Optimization (ACO) technique with opposition-based learning (OBL). The Enhanced Ant Colony Optimization (EACO) methodology was then employed to determine the optimal hyperparameter values for the CNN architecture. Our proposed framework combines the Residual Network-101 (ResNet101) CNN architecture with the EACO algorithm, resulting in a new model dubbed EACO–ResNet101. Experimental analysis was conducted on the MIAS and DDSM (CBIS-DDSM) mammographic datasets. Compared to conventional methods, our proposed model achieved an impressive accuracy of 98.63%, sensitivity of 98.76%, and specificity of 98.89% on the CBIS-DDSM dataset. On the MIAS dataset, the proposed model achieved a classification accuracy of 99.15%, a sensitivity of 97.86%, and a specificity of 98.88%. These results demonstrate the superiority of the proposed EACO–ResNet101 over current methodologies. [ABSTRACT FROM AUTHOR]

We developed a framework to detect and grade knee RA using digital X-radiation images and used it to demonstrate the ability of deep learning approaches to detect knee RA using a consensus-based decision (CBD) grading system. The study aimed to evaluate the efficiency with which a deep learning approach based on artificial intelligence (AI) can find and determine the severity of knee RA in digital X-radiation images. The study comprised people over 50 years with RA symptoms, such as knee joint pain, stiffness, crepitus, and functional impairments. The digitized X-radiation images of the people were obtained from the BioGPS database repository. We used 3172 digital X-radiation images of the knee joint from an anterior–posterior perspective. The trained Faster-CRNN architecture was used to identify the knee joint space narrowing (JSN) area in digital X-radiation images and extract the features using ResNet-101 with domain adaptation. In addition, we employed another well-trained model (VGG16 with domain adaptation) for knee RA severity classification. Medical experts graded the X-radiation images of the knee joint using a consensus-based decision score. We trained the enhanced-region proposal network (ERPN) using this manually extracted knee area as the test dataset image. An X-radiation image was fed into the final model, and a consensus decision was used to grade the outcome. The presented model correctly identified the marginal knee JSN region with 98.97% of accuracy, with a total knee RA intensity classification accuracy of 99.10%, with a sensitivity of 97.3%, a specificity of 98.2%, a precision of 98.1%, and a dice score of 90.1% compared with other conventional models. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) are used for recording the information from the physical surroundings and transmitting the gathered records to a principal location via extensively disbursed sensor nodes. The proliferation of sensor devices and advances in size, deployment costs, and user-friendly interfaces have spawned numerous WSN applications. The WSN should use a routing protocol to send information to the sink over a low-cost link. One of the foremost vital problems is the restricted energy of the sensing element and, therefore, the high energy is consumed throughout the time. An energy-efficient routing may increase the lifetime by consuming less energy. Taking this into consideration, this paper provides a multi-criteria clustering and optimal bio-inspired routing algorithmic rule to reinforce network lifetime, to increase the operational time of WSN-based applications and make robust clusters. Clustering is a good methodology of information aggregation that increases the lifetime by group formation. Multi-criteria clustering is used to select the optimal cluster head (CH). After proper selection of the CH, moth flame and salp swarm optimization algorithms are combined to analyze the quality route for transmitting information from the CH to the sink and expand the steadiness of the network. The proposed method is analyzed and contrasted with previous techniques, with parameters such as energy consumption, throughput, end-to-end delay, latency, lifetime, and packet delivery rate. Consumption of energy is minimized by up to 18.6% and network life is increased up to 6% longer compared to other routing protocols. [ABSTRACT FROM AUTHOR]

In recent years, finding the optimal solution for image segmentation has become more important in many applications. The whale optimization algorithm (WOA) is a metaheuristic optimization technique that has the advantage of achieving the global optimal solution while also being simple to implement and solving many real-time problems. If the complexity of the problem increases, the WOA may stick to local optima rather than global optima. This could be an issue in obtaining a better optimal solution. For this reason, this paper recommends a hybrid algorithm that is based on a mixture of the WOA and gray wolf optimization (GWO) for segmenting the brain sub regions, such as the gray matter (GM), white matter (WM), ventricle, corpus callosum (CC), and hippocampus (HC). This hybrid mixture consists of two steps, i.e., the WOA and GWO. The proposed method helps in diagnosing Alzheimer's disease (AD) by segmenting the brain sub regions (SRs) by using a hybrid of the WOA and GWO (H-WOA-GWO, which is represented as HWGO). The segmented region was validated with different measures, and it shows better accuracy results of 92%. Following segmentation, the deep learning classifier was utilized to categorize normal and AD images. The combination of WOA and GWO yields an accuracy of 90%. As a result, it was discovered that the suggested method is a highly successful technique for identifying the ideal solution, and it is paired with a deep learning algorithm for classification. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy (DR) and adult vitelliform macular dystrophy (AVMD) may cause significant vision impairment or blindness. Prompt diagnosis is essential for patient health. Photographic ophthalmoscopy checks retinal health quickly, painlessly, and easily. It is a frequent eye test. Ophthalmoscopy images of these two illnesses are challenging to analyse since early indications are typically absent. We propose a deep learning strategy called ActiveLearn to address these concerns. This approach relies heavily on the ActiveLearn Transformer as its central structure. Furthermore, transfer learning strategies that are able to strengthen the low-level features of the model and data augmentation strategies to balance the data are incorporated owing to the peculiarities of medical pictures, such as their limited quantity and generally rigid structure. On the benchmark dataset, the suggested technique is shown to perform better than state-of-the-art methods in both binary and multiclass accuracy classification tasks with scores of 97.9% and 97.1%, respectively. [ABSTRACT FROM AUTHOR]

Electroencephalography (EEG) is a reliable method for identifying the onset of sleepiness behind the wheel. Using EEG technology for driving fatigue detection still presents challenges in extracting informative elements from noisy EEG signals. Due to their extensive computational parallelism, which is similar to how the brain processes information, neural networks have been explored as potential solutions for extracting relevant information from EEG data. The existing machine learning frameworks suffer from high computing costs and slow convergence, both of which contribute to low classification accuracy and efficiency due to the large number of hyper parameters that need to be improved. It is necessary to automate this micronap detection process before it can be used in real-time scenarios. To distinguish between micronap and non-micronap states, a deep neural network (DNN) framework is developed in this research using different EEG representations as input. Additional EEG representations utilized in this investigation include cleaned EEG as a time series, log-power spectrum, 2D-spatial map of log-power spectrum, and raw EEG. Finally, traditional machine learning algorithms are evaluated for their effectiveness in detecting micronaps from these EEG inputs. The findings suggest that micronap detection can be greatly improved by combining cleaned EEG with DNN. [ABSTRACT FROM AUTHOR]

Every year, cervical cancer is a leading cause of mortality in women all over the world. This cancer can be cured if it is detected early and patients are treated promptly. This study proposes a new strategy for the detection of cervical cancer using cervigram pictures. The associated histogram equalization (AHE) technique is used to improve the edges of the cervical image, and then the finite ridgelet transform is used to generate a multi-resolution picture. Then, from this converted multi-resolution cervical picture, features such as ridgelets, gray-level run-length matrices, moment invariant, and enhanced local ternary pattern are retrieved. A feed-forward backward propagation neural network is used to train and test these extracted features in order to classify the cervical images as normal or abnormal. To detect and segment cancer regions, morphological procedures are applied to the abnormal cervical images. The cervical cancer detection system's performance metrics include 98.11% sensitivity, 98.97% specificity, 99.19% accuracy, a PPV of 98.88%, an NPV of 91.91%, an LPR of 141.02%, an LNR of 0.0836, 98.13% precision, 97.15% FPs, and 90.89% FNs. The simulation outcomes show that the proposed method is better at detecting and segmenting cervical cancer than the traditional methods. [ABSTRACT FROM AUTHOR]

With the advancement of Internet technologies and the rapid increase of World Wide Web applications, there has been tremendous growth in the volume of digital data. This takes the digital world into a new era of big data. Various existing data processing technologies are not consistent and scalable in handling the complexity as well as the large-size datasets. Recently, there are many distributed data processing, and programming models have been proposed and implemented to handle big data applications. The open-source-implemented MapReduce programming model in Apache Hadoop is the foremost model for data exhaustive and also computational-intensive applications due to its inherent characteristics of scalability, fault tolerance, and simplicity. In this research article, a new approach for the prediction of target labels in big data applications is developed using a multiple linear regression algorithm and MapReduce programming model, named as MR-MLR. This approach promises optimum values for MAE, RMSE, and determination coefficient (R2) and thus shows its effectiveness in predictions in big data applications. [ABSTRACT FROM AUTHOR]

In this paper, trajectory planning and navigation control problems have been addressed for a mobile robot. To achieve the objective of the research, an adaptive PSO (Particle Swarm Optimization) motion algorithm is developed using a penalty-based methodology. To deliver the best or collision-free position to the robot, fitness values of the all-random-positioned particles are compared at the same time during the target search action. By comparing the fitness values, the robot occupies the best position in the search space till it reaches the target. The new work integrated with conventional PSO is varying a velocity event that plays a vital role during the position acquisition (continuous change in position during the obstacle negotiation with the communication through random-positioned particles). The obstacle-negotiating angle and positional velocity of the robot are considered as input parameters of the controller whereas the robot's best position according to the target position is considered as the output of the controller. Simulation results are presented through the MATLAB environment. To validate simulation results, real-time experiments have been conducted in a similar workspace. The results of the adaptive PSO technique are also compared with the results of the existing navigational techniques. Improvements in results between the proposed navigation technique and existing navigation techniques are found to be 4.66% and 11.30%, respectively. [ABSTRACT FROM AUTHOR]

In recent times, the early detection of brain tumour analysis and classification has become a very vital part of the medical field. The MRI scan image is the most significant tool to study brain tissue for proper diagnosis and efficient treatment planning to detect the early stages. In this research study, the two contributions were executed in the preprocessing mode. (a) Using wavelet transform to apply decomposed sub-bands of a low-frequency signal to control and adapt the spatial and intensity parameters in a bilateral filter and (b) to detect texture regions and block boundary to control and adapt the spatial and intensity parameters in a bilateral filter When compared to other image resolution methods, the adaptive bilateral method restores the original image quality and has a higher accuracy rate. Using the hybrid segmentation method of GCPSO (Guaranteed Convergence Particle Swarm Optimization) -FCM (Fuzzy C-Mean) techniques, the results were compared with various segmentation. The proposed segmentation gives a better accuracy rate of 95.32%. [ABSTRACT FROM AUTHOR]

Many scientific researchers' study focuses on enhancing automated systems to identify emotions and thus relies on brain signals. This study focuses on how brain wave signals can be used to classify many emotional states of humans. Electroencephalography (EEG)-based affective computing predominantly focuses on emotion classification based on facial expression, speech recognition, and text-based recognition through multimodality stimuli. The proposed work aims to implement a methodology to identify and codify discrete complex emotions such as pleasure and grief in a rare psychological disorder known as alexithymia. This type of disorder is highly elicited in unstable, fragile countries such as South Sudan, Lebanon, and Mauritius. These countries are continuously affected by civil wars and disaster and politically unstable, leading to a very poor economy and education system. This study focuses on an adolescent age group dataset by recording physiological data when emotion is exhibited in a multimodal virtual environment. We decocted time frequency analysis and amplitude time series correlates including frontal alpha symmetry using a complex Morlet wavelet. For data visualization, we used the UMAP technique to obtain a clear district view of emotions. We performed 5-fold cross validation along with 1 s window subjective classification on the dataset. We opted for traditional machine learning techniques to identify complex emotion labeling. [ABSTRACT FROM AUTHOR]

Deep learning is a cutting-edge image processing method that is still relatively new but produces reliable results. Leaf disease detection and categorization employ a variety of deep learning approaches. Tomatoes are one of the most popular vegetables and can be found in every kitchen in various forms, no matter the cuisine. After potato and sweet potato, it is the third most widely produced crop. The second-largest tomato grower in the world is India. However, many diseases affect the quality and quantity of tomato crops. This article discusses a deep-learning-based strategy for crop disease detection. A Convolutional-Neural-Network-based technique is used for disease detection and classification. Inside the model, two convolutional and two pooling layers are used. The results of the experiments show that the proposed model outperformed pre-trained InceptionV3, ResNet 152, and VGG19. The CNN model achieved 98% training accuracy and 88.17% testing accuracy. [ABSTRACT FROM AUTHOR]

Pollution is the primary ecological threat to nature and it also raises the temperature of the Earth. The primary cause of air pollution is exhaust emissions from the combustion chamber. This study aims to focus on controlling emissions using an aqueous nanoemulsion method and to analyze positive vibrations for the reduction of pollutants in diesel engines. In this study 3% NWED (nano-based water emulsion diesel), 7% NWED, and 12% NWED emulsion blended diesel were mixed with Tween-20 as a solvent. In addition, this work was accomplished with a multiuser remote control system for the continuous monitoring of emissions with IoT technology applied to diesel engines. Experimental results have been measured with an IoT kit and five gas analyzers and reported that emission of oxides of nitrogen and hydrocarbons can be reduced by using a 7% NWED blend when compared with diesel. In addition, the experimental result also shows that the brake-specific fuel consumption is reduced with an improvement in thermal efficiency at 7% NWED when compared with other blends. [ABSTRACT FROM AUTHOR]

A large array of objects is networked together under the sophisticated concept known as the Internet of Things (IoT). These connected devices collect crucial information that could have a big impact on society, business, and the entire planet. In hostile settings like the internet, the IoT is particularly susceptible to multiple threats. Standard high-end security solutions are insufficient for safeguarding an IoT system due to the low processing power and storage capacity of IoT devices. This emphasizes the demand for scalable, distributed, and long-lasting smart security solutions. Deep learning excels at handling heterogeneous data of varying sizes. In this study, the transport layer of IoT networks is secured using a multilayered security approach based on deep learning. The created architecture uses the intrusion detection datasets from CIC-IDS-2018, BoT-IoT, and ToN-IoT to evaluate the suggested multi-layered approach. Finally, the new design outperformed the existing methods and obtained an accuracy of 98% based on the examined criteria. [ABSTRACT FROM AUTHOR]

The complexity of multimedia content, particularly images, has risen dramatically in recent years, and millions of images are shared on social media every day. Finding or retrieving an appropriate image is becoming more difficult due to the increase in the volume of shared and archived multimedia data. Any image retrieval model must, at a bare minimum, locate and classify images that are visually related to the user's query. The vast majority of Internet search engines employ text algorithms that fetch images using captions as input. Even though there is a lot of study being done to increase the effectiveness of automatic image annotation, retrieval errors can occur due to differences in visual perception. Content-based image retrieval (CBIR) addresses the aforementioned issue because visual analysis of the content is included in the query image. On the other hand, feature extraction is significantly challenging because of semantic gap. This work proposes a strategy for effective retrieval in similarity images using the triadic color scheme RGB, YCbCr, and L ∗ a ∗ b ∗ based on reranking. We want to increase image similarity and encourage more relevant reranking. As a result of the findings, it can be concluded that a triadic color scheme improves precision by 5% more dramatically than existing schemes and also efficiently improves retrieved results while reducing user effort. [ABSTRACT FROM AUTHOR]

High dimensionality of the feature space is one of the problems in the field of text classification. Identification of optimal subset of features can optimize text classification process in terms of processing time and performance. In this paper, we propose a novel Relevant-Based Feature Ranking (RBFR) algorithm which identifies and selects smaller subsets of more relevant features in the feature space. We compared the performance of the RBFR against other existing feature selection methods such as balanced accuracy measure, information gain, Gini index, and odds ratio on 3 datasets, namely, 20 newsgroup, Reuters, and WAP datasets. We have used 5 machine learning models (SVM, NB, kNN, RF, and LR) to test and evaluate the proposed feature selection method. We found that the performance of the proposed feature selection method is 25.4305% times more effective than the existing feature selection methods in terms of accuracy. [ABSTRACT FROM AUTHOR]

As training deep neural networks enough requires a large amount of data, there have been a lot of studies to deal with this problem. Data augmentation techniques are basic solutions to increase training data using existing data. Geometric transformations and color space augmentations are well-known augmentation techniques, but they still require some manual work and can generate limited types of data only. Therefore, there are many interests in generative-model-based augmentation lately, which can learn the distribution of data. This study proposes a set of GAN-based data augmentation methods that can generate good quality training data. The proposed networks, f-DAGAN (data augmentation generative adversarial networks), have been motivated by the DAGAN that learns data distribution from two real data. The basic f-DAGAN uses dual discriminators handling both generated data and generated feature spaces for better learning the given data. The other versions of f-DAGANs have been proposed for generating hard or easy data that have additional dual classifiers for both generated data and feature spaces to control the generator. Hard data is useful for optimized training to increase the target performance such as classification accuracy. Easy data generation can be used especially in few-shot learning. The quality of generated data has been validated in two ways: using t-SNE visualization of generated data and classification accuracy by training with generated data using the MNIST data set. The t-SNE representations show that data generated by f-DAGAN are evenly distributed for every class better than the exiting generative model-based augmentation methods. The f-DAGAN also shows the best classification accuracy by training with generated data. The f-DAGAN version for easy and hard data generation generates data well from five-shot learning and performs well in sample data generation experiments. [ABSTRACT FROM AUTHOR]

Cracks that are detected in concrete structures represent significant damage, and they can lead to a detrimental effect on the structure's durability. Their identification in a timely manner can help ensure structural safety and guide in-depth maintenance operation. Automatic detection of such cracks has been proposed using internal crack detection utilizing ultrasonic sensors in concrete. Cracks within the concrete can be detected using ultrasonic sensors. In this investigation, we introduced an intelligent method that is aimed at developing a crack detection scheme using ultrasonic sensors. These ultrasonic sensors are used for the detection of cracks in buildings which cannot be seen with our naked eyes; they are capable of alerting authorities via SMS message and providing the cracks' location via GSM and GPS modules. To monitor internal cracks in the concrete cubes and cylinders, the ultrasonic sensors can be fixed at the centre of the cube which will be used for interval crack monitoring based on crack detection technology. The grade of concrete used for testing is M25, and it is well mixed with the ingredients of cement, fine aggregate, coarse aggregate, and water. The concrete is placed in the cube moulds having the dimensions 150 mm × 150 mm × 150 mm. The cylinders used in the case of the experimental analysis are of the dimensions of 150 mm diameter and 300 mm height. These specimens are cast and kept in the curing tank for 28 days to attain the maximum strength. After completion of the curing period, the specimens were taken out from the tank and weighed. After this weighing process, the cubes and cylinders are about 8.884 kg and 13.399 kg, respectively. The information about the cracks can be displayed on the LCD, and also, the transmitted short message about the cracks can be exchanged between the devices using IoT. [ABSTRACT FROM AUTHOR]

The neoteric introduction of 5G technology in mobile internet is transforming Internet of mobile Things (IoMT) massively by addressing low latency, support for a large number of IoMT devices, and less power consumption, thereby delivering cost-effective solutions to low-end devices. This transformational technology enables a ubiquitous connected critical communication network between the healthcare system and IoT, as they largely depend on low-end devices for gathering data at the point of care. Gathering and interpolation of data from things are moved over to the cloud, making the extraction of knowledge and decision-making capabilities more robust. Vocal signals form the basis of communication between human beings with the transfer of complex data with variations in thrust, pitch, and tones. The representation and recognition of these analog signals by digital systems prove to be quite exciting and challenging. The spoken language models are converted to digital signals to be identifiable based on different cues like phonetic, prosodic, phonotactic, and lexical features. Voice patterns tend to be specific for every individual with a slight orientation towards the language spoken by the individual of a particular region. While speech patterns tend to alter the meaning of words with tones, high and low pitches in the utterance of the words, NLP tends to learn specific associations of words through vectors. The focus on learned networks in solving the problem of speech synthesis to text with minimal loss and high predictability of syllable of word, sentence, and paraphrase is needed. The creation of a knowledge base corpus of learned variable prosody of features helps in the learnability of interestingness directly without any perturbations. The learning algorithms to realize the degree of understandability of speech with the word, sentence identified, and transcription with substantial noise interference. The transfer of the acoustic features learned by algorithms proves to be quiet challenging as they are distorted by sudden environmental changes. Syllable extracted from the speech translation may or may not represent the Sentiment of the word, with different phonetical modulation. Utilization of the MobileNets and DistillBERT to transfer the language extraction and the edge reducing the time of processing and reducing the corpus of the size, reducing the adversial learning of the voice features and the patterns, reducing the Transfer of learned corpus and patterns. [ABSTRACT FROM AUTHOR]

Big data is a collection of large volumes of data sets which are more complicated to analyze using standard data processing methods. It also emphasizes parameters like data variety and velocity data. Big data will play a most significant role in our daily life regarding applications like healthcare electronic commerce, agriculture, telecommunication, government, and financial trading. In the agriculture domain, big data is an optimal method to increase the productivity of farming by gathering and processing information like plant growth, farmland monitoring, greenhouse gases monitoring, climate change, soil monitoring and so forth. Virtualization is an emerging technique that can be combined with big data in agriculture. Virtualization has been used extensively in research for a long time, the term "virtual" entities affecting a real-life form. In agriculture, it has many more physical objects, sensors, and devices. This physical object is virtualized and has digital representation to store, communicate and process via the internet. The information from the virtual object has a large volume of data which helps meaningful data analysis or aspects to make application services like decision making, problem notification, and information handling. This paper provides a comprehensive review of big data virtualization in the agriculture domain. The virtualization methodology, and tools used by many researchers is surveyed. [ABSTRACT FROM AUTHOR]

Geospatial images deliver a visual medium to recognize the prompt changes in the environment influenced due to seasonal changes. These changes extend their impact on the soil vegetation and crop growth patterns leading to an increase/decrease in agricultural production. Identification of seasonal changes and their impact using collected geospatial images within a specific time frame to predict land utilized for agriculture. Although identifying land cover utilization through spectral indices and bands are available, they generalize based on one particular region and may misclassify and overlap each other. The utilization of template matching enables in identifying the crop field area based on templates extracted and labelled. Masked images are acquired randomly from images or existing datasets labelled as fields cropped and barren. The specific region of interest is tiled and demarcated through Semi-supervised classification identifying the annual land crop cover. The extracted images are utilized as masked templates to identify the area of crop cover with an accuracy of 98%. The correlations of land cover helps in identify the specific Region of Interest (ROI) providing maximum and minimum utilization of cultivable lands. • A novel and extensive collection of Sentinel-2 images for 18 years with cropped mask images for identifying specific ROI were used. • Class-based classification is prepared for specific ROI used with pixel-level segmentation to identify land cover for a specific period. • The combined utilization of the U-Net and Random forest algorithm was used to create an ROI-based model training and validation, identifying whether the ROI has fields cultivated/uncultivated within the specific period. [ABSTRACT FROM AUTHOR]

In this work, a novel hybrid neuro-fuzzy classifier (HNFC) technique is proposed for producing more accuracy in input data classification. The inputs are fuzzified using a generalized membership function. The fuzzification matrix helps to create connectivity between input pattern and degree of membership to various classes in the dataset. According to that, the classification process is performed for the input data. This novel method is applied for ten number of benchmark datasets. During preprocessing, the missing data is replaced with the mean value. Then, the statistical correlation is applied for selecting the important features from the dataset. After applying a data transformation technique, the values normalized. Initially, fuzzy logic has been applied for the input dataset; then, the neural network is applied to measure the performance. The result of the proposed method is evaluated with supervised classification techniques such as radial basis function neural network (RBFNN) and adaptive neuro-fuzzy inference system (ANFIS). Classifier performance is evaluated by measures like accuracy and error rate. From the investigation, the proposed approach provided 86.2% of classification accuracy for the breast cancer dataset compared to other two approaches. [ABSTRACT FROM AUTHOR]