A Novel QKD Approach to Enhance IIOT Privacy and Computational Knacks.

The industry-based internet of things (IIoT) describes how IIoT devices enhance and extend their capabilities for production amenities, security, and efficacy. IIoT establishes an enterprise-to-enterprise setup that means industries have several factories and manufacturing units that are dependent on other sectors for their services and products. In this context, individual industries need to share their information with other external sectors in a shared environment which may not be secure. The capability to examine and inspect such large-scale information and perform analytical protection over the large volumes of personal and organizational information demands authentication and confidentiality so that the total data are not endangered after illegal access by hackers and other unauthorized persons. In parallel, these large volumes of confidential industrial data need to be processed within reasonable time for effective deliverables. Currently, there are many mathematical-based symmetric and asymmetric key cryptographic approaches and identity- and attribute-based public key cryptographic approaches that exist to address the abovementioned concerns and limitations such as computational overheads and taking more time for crucial generation as part of the encipherment and decipherment process for large-scale data privacy and security. In addition, the required key for the encipherment and decipherment process may be generated by a third party which may be compromised and lead to man-in-the-middle attacks, brute force attacks, etc. In parallel, there are some other quantum key distribution approaches available to produce keys for the encipherment and decipherment process without the need for a third party. However, there are still some attacks such as photon number splitting attacks and faked state attacks that may be possible with these existing QKD approaches. The primary motivation of our work is to address and avoid such abovementioned existing problems with better and optimal computational overhead for key generation, encipherment, and the decipherment process compared to the existing conventional models. To overcome the existing problems, we proposed a novel dynamic quantum key distribution (QKD) algorithm for critical public infrastructure, which will secure all cyber-physical systems as part of IIoT. In this paper, we used novel multi-state qubit representation to support enhanced dynamic, chaotic quantum key generation with high efficiency and low computational overhead. Our proposed QKD algorithm can create a chaotic set of qubits that act as a part of session-wise dynamic keys used to encipher the IIoT-based large scales of information for secure communication and distribution of sensitive information.

Diagnosis and combating COVID-19 using wearable Oura smart ring with deep learning methods.

Since the coronavirus (COVID-19) outbreak keeps on spreading all through the world, scientists have been crafting varied technologies mainly focusing on AI for an approach to acknowledge the difficulties of the epidemic. In this current worldwide emergency, the clinical business is searching for new advancements to screen and combat COVID-19 contamination. Strategies used by artificial intelligence can stretch screen the spread of the infection, distinguish highly infected patients, and be compelling in supervising the illness continuously. The artificial intelligence anticipation can further be used for passing dangers by sufficiently dissecting information from past sufferers. International patient support with recommendations for population testing, medical care, notification, and infection control can help fight this deadly virus. We proposed the hybrid deep learning method to diagnose COVID-19. The layered approach is used here to measure the symptom level of the patients and to analyze the patient image data whether he/she is positive with COVID-19. This work utilizes smart AI techniques to predict and diagnose the coronavirus rapidly by the Oura smart ring within 24 h. In the laboratory, a coronavirus rapid test is prepared with the help of a deep learning model using the RNN and CNN algorithms to diagnose the coronavirus rapidly and accurately. The result shows the value 0 or 1. The result 1 indicates the person is affected with coronavirus and the result 0 indicates the person is not affected with coronavirus. X-Ray and CT image classifications are considered here so that the threshold value is utilized for identifying an individual's health condition from the initial stage to a severe stage. Threshold value 0.5 is used to identify coronavirus initial stage condition and 1 is used to identify the coronavirus severe condition of the patient. The proposed methods are utilized for four weighting parameters to reduce both false positive and false negative image classification results for rapid and accurate diagnosis of COVID-19.

Correction to: Diagnosis and combating COVID-19 using wearable Oura smart ring with deep learning methods.

[This corrects the article DOI: 10.1007/s00779-021-01541-4.].

A Novel Blockchain and Bi-Linear Polynomial-Based QCP-ABE Framework for Privacy and Security over the Complex Cloud Data.

As a result of the limited resources available in IoT local devices, the large scale cloud consumer's data that are produced by IoT related machines are contracted out to the cloud. Cloud computing is unreliable, using it can compromise user privacy, and data may be leaked. Because cloud-data and grid infrastructure are both growing exponentially, there is an urgent need to explore computational sources and cloud large-data protection. Numerous cloud service categories are assimilated into numerous fields, such as defense systems and pharmaceutical databases, to compute information space and allocation of resources. Attribute Based Encryption (ABE) is a sophisticated approach which can permit employees to specify a higher level of security for data stored in cloud storage facilities. Numerous obsolete ABE techniques are practical when applied to small data sets to generate cryptograms with restricted computational properties; their properties are used to generate the key, encrypt it, and decrypt it. To address the current concerns, a dynamic non-linear polynomial chaotic quantum hash technique on top of secure block chain model can be used for enhancing cloud data security while maintaining user privacy. In the proposed method, customer attributes are guaranteed by using a dynamic non- polynomial chaotic map function for the key initialization, encryption, and decryption. In the proposed model, both organized and unorganized massive clinical data are considered to be inputs for reliable corroboration and encoding. Compared to existing models, the real-time simulation results demonstrate that the stated standard is more precise than 90% in terms of bit change and more precise than 95% in terms of dynamic key generation, encipherment, and decipherment time.

Using Multiple Antibiotic Resistance Profiles of Coliforms as a Tool to Investigate Combined Sewer Overflow Contamination.

Studies have shown that fecal contamination can be determined by conducting multiple antibiotic resistance (MAR) analyses. The hypothesis is if bacteria exhibit resistance, they are likely to be derived from organisms exposed to antimicrobial agents. Therefore, this project seeks to apply MAR analysis to nonpoint source (NPS) and combined sewer overflow (CSO) areas along the Anacostia River in Washington, DC. Presumptive E. coli was isolated from NPS and CSO samples and tested with eight different antimicrobial agents to assess MAR indices. Isolates from CSO sources showed significantly greater resistance (p < .05) and higher MAR indices, with an average MAR index of 0.36 for CSO samples and 0.07 for NPS samples. It was also revealed that 96.9% of CSO isolates exhibited resistance, versus only 43.8% of NPS isolates. Our study on the Anacostia River using this approach clearly shows fecal coliforms are associated with CSO overflows, indicating that pollution-derived coliform levels are strongly linked to antimicrobial resistance. The implementation of this method as an index for water quality in the remediation of the Anacostia River has the ability to serve as a model and monitoring tool for the rehabilitation of urban watersheds.

Conflict of interest in online point-of-care clinical support websites.

Point-of-care evidence-based medicine websites allow physicians to answer clinical queries using recent evidence at the bedside. Despite significant research into the function, usability and effectiveness of these programmes, little attention has been paid to their ethical issues. As many of these sites summarise the literature and provide recommendations, we sought to assess the role of conflicts of interest in two widely used websites: UpToDate and Dynamed. We recorded all conflicts of interest for six articles detailing treatment for the following conditions: erectile dysfunction, fibromyalgia, hypogonadism, psoriasis, rheumatoid arthritis and Crohn's disease. These diseases were chosen as their medical management is either controversial, or they are treated using biological drugs which are mostly available by brand name only. Thus, we hypothesised that the role of conflict of interest would be more significant in these conditions than in an illness treated with generic medications or by strict guidelines. All articles from the UpToDate articles demonstrated a conflict of interest. At times, the editor and author would have a financial relationship with a company whose drug was mentioned within the article. This is in contrast with articles on the Dynamed website, in which no author or editor had a documented conflict. We offer recommendations regarding the role of conflict of interest disclosure in these point-of-care evidence-based medicine websites.

Adaptive Guided Equilibrium Optimizer with Spiral Search Mechanism to Solve Global Optimization Problems.

The equilibrium optimizer (EO) is a recently developed physics-based optimization technique for complex optimization problems. Although the algorithm shows excellent exploitation capability, it still has some drawbacks, such as the tendency to fall into local optima and poor population diversity. To address these shortcomings, an enhanced EO algorithm is proposed in this paper. First, a spiral search mechanism is introduced to guide the particles to more promising search regions. Then, a new inertia weight factor is employed to mitigate the oscillation phenomena of particles. To evaluate the effectiveness of the proposed algorithm, it has been tested on the CEC2017 test suite and the mobile robot path planning (MRPP) problem and compared with some advanced metaheuristic techniques. The experimental results demonstrate that our improved EO algorithm outperforms the comparison methods in solving both numerical optimization problems and practical problems. Overall, the developed EO variant has good robustness and stability and can be considered as a promising optimization tool.

Kookaburra Optimization Algorithm: A New Bio-Inspired Metaheuristic Algorithm for Solving Optimization Problems.

In this paper, a new bio-inspired metaheuristic algorithm named the Kookaburra Optimization Algorithm (KOA) is introduced, which imitates the natural behavior of kookaburras in nature. The fundamental inspiration of KOA is the strategy of kookaburras when hunting and killing prey. The KOA theory is stated, and its mathematical modeling is presented in the following two phases: (i) exploration based on the simulation of prey hunting and (ii) exploitation based on the simulation of kookaburras' behavior in ensuring that their prey is killed. The performance of KOA has been evaluated on 29 standard benchmark functions from the CEC 2017 test suite for the different problem dimensions of 10, 30, 50, and 100. The optimization results show that the proposed KOA approach, by establishing a balance between exploration and exploitation, has good efficiency in managing the effective search process and providing suitable solutions for optimization problems. The results obtained using KOA have been compared with the performance of 12 well-known metaheuristic algorithms. The analysis of the simulation results shows that KOA, by providing better results in most of the benchmark functions, has provided superior performance in competition with the compared algorithms. In addition, the implementation of KOA on 22 constrained optimization problems from the CEC 2011 test suite, as well as 4 engineering design problems, shows that the proposed approach has acceptable and superior performance compared to competitor algorithms in handling real-world applications.

Lyrebird Optimization Algorithm: A New Bio-Inspired Metaheuristic Algorithm for Solving Optimization Problems.

In this paper, a new bio-inspired metaheuristic algorithm called the Lyrebird Optimization Algorithm (LOA) that imitates the natural behavior of lyrebirds in the wild is introduced. The fundamental inspiration of LOA is the strategy of lyrebirds when faced with danger. In this situation, lyrebirds scan their surroundings carefully, then either run away or hide somewhere, immobile. LOA theory is described and then mathematically modeled in two phases: (i) exploration based on simulation of the lyrebird escape strategy and (ii) exploitation based on simulation of the hiding strategy. The performance of LOA was evaluated in optimization of the CEC 2017 test suite for problem dimensions equal to 10, 30, 50, and 100. The optimization results show that the proposed LOA approach has high ability in terms of exploration, exploitation, and balancing them during the search process in the problem-solving space. In order to evaluate the capability of LOA in dealing with optimization tasks, the results obtained from the proposed approach were compared with the performance of twelve well-known metaheuristic algorithms. The simulation results show that LOA has superior performance compared to competitor algorithms by providing better results in the optimization of most of the benchmark functions, achieving the rank of first best optimizer. A statistical analysis of the performance of the metaheuristic algorithms shows that LOA has significant statistical superiority in comparison with the compared algorithms. In addition, the efficiency of LOA in handling real-world applications was investigated through dealing with twenty-two constrained optimization problems from the CEC 2011 test suite and four engineering design problems. The simulation results show that LOA has effective performance in handling optimization tasks in real-world applications while providing better results compared to competitor algorithms.

A chaos-based adaptive equilibrium optimizer algorithm for solving global optimization problems.

The equilibrium optimizer (EO) algorithm is a newly developed physics-based optimization algorithm, which inspired by a mixed dynamic mass balance equation on a controlled fixed volume. The EO algorithm has a number of strengths, such as simple structure, easy implementation, few parameters and its effectiveness has been demonstrated on numerical optimization problems. However, the canonical EO still presents some drawbacks, such as poor balance between exploration and exploitation operation, tendency to get stuck in local optima and low convergence accuracy. To tackle these limitations, this paper proposes a new EO-based approach with an adaptive gbest-guided search mechanism and a chaos mechanism (called a chaos-based adaptive equilibrium optimizer algorithm (ACEO)). Firstly, an adaptive gbest-guided mechanism is injected to enrich the population diversity and expand the search range. Next, the chaos mechanism is incorporated to enable the algorithm to escape from the local optima. The effectiveness of the developed ACEO is demonstrated on 23 classical benchmark functions, and compared with the canonical EO, EO variants and other frontier metaheuristic approaches. The experimental results reveal that the developed ACEO method remarkably outperforms the canonical EO and other competitors. In addition, ACEO is implemented to solve a mobile robot path planning (MRPP) task, and compared with other typical metaheuristic techniques. The comparison indicates that ACEO beats its competitors, and the ACEO algorithm can provide high-quality feasible solutions for MRPP.

Identification of rice leaf diseases and deficiency disorders using a novel DeepBatch technique.

Rice is one of the most widely consumed foods all over the world. Various diseases and deficiency disorders impact the rice crop's growth, thereby hampering the rice yield. Therefore, proper crop monitoring is very important for the early diagnosis of diseases or deficiency disorders. Diagnosis of diseases and disorders requires specialized manpower, which is not scalable and accessible to all farmers. To address this issue, machine learning and deep learning (DL)-driven automated systems are designed, which may help the farmers in diagnosing disease/deficiency disorders in crops so that proper care can be taken on time. Various studies have used transfer learning (TL) models in the recent past. In recent studies, further improvement in rice disease and deficiency disorder diagnosis system performance is achieved by performing the ensemble of various TL models. However, in all these DL-based studies, the segmentation of the region of interest is not done beforehand and the infected-region extraction is left for the DL model to handle automatically. Therefore, this article proposes a novel framework for the diagnosis of rice-infected leaves based on DL-based segmentation with bitwise logical AND operation and DL-based classification. The rice diseases covered in this study are bacterial leaf blight, brown spot, and leaf smut. The rice nutrient deficiencies like nitrogen (N), phosphorous (P), and potassium (K) were also included. The results of the experiment conducted on these datasets showed that the performance of DeepBatch was significantly improved as compared to the conventional technique.

Golf Optimization Algorithm: A New Game-Based Metaheuristic Algorithm and Its Application to Energy Commitment Problem Considering Resilience.

In this research article, we uphold the principles of the No Free Lunch theorem and employ it as a driving force to introduce an innovative game-based metaheuristic technique named Golf Optimization Algorithm (GOA). The GOA is meticulously structured with two distinctive phases, namely, exploration and exploitation, drawing inspiration from the strategic dynamics and player conduct observed in the sport of golf. Through comprehensive assessments encompassing fifty-two objective functions and four real-world engineering applications, the efficacy of the GOA is rigorously examined. The results of the optimization process reveal GOA's exceptional proficiency in both exploration and exploitation strategies, effectively striking a harmonious equilibrium between the two. Comparative analyses against ten competing algorithms demonstrate a clear and statistically significant superiority of the GOA across a spectrum of performance metrics. Furthermore, the successful application of the GOA to the intricate energy commitment problem, considering network resilience, underscores its prowess in addressing complex engineering challenges. For the convenience of the research community, we provide the MATLAB implementation codes for the proposed GOA methodology, ensuring accessibility and facilitating further exploration.

Early diagnosis and meta-agnostic model visualization of tuberculosis based on radiography images.

Despite being treatable and preventable, tuberculosis (TB) affected one-fourth of the world population in 2019, and it took the lives of 1.4 million people in 2019. It affected 1.2 million children around the world in the same year. As it is an infectious bacterial disease, the early diagnosis of TB prevents further transmission and increases the survival rate of the affected person. One of the standard diagnosis methods is the sputum culture test. Diagnosing and rapid sputum test results usually take one to eight weeks in 24 h. Using posterior-anterior chest radiographs (CXR) facilitates a rapid and more cost-effective early diagnosis of tuberculosis. Due to intraclass variations and interclass similarities in the images, TB prognosis from CXR is difficult. We proposed an early TB diagnosis system (tbXpert) based on deep learning methods. Deep Fused Linear Triangulation (FLT) is considered for CXR images to reconcile intraclass variation and interclass similarities. To improve the robustness of the prognosis approach, deep information must be obtained from the minimal radiation and uneven quality CXR images. The advanced FLT method accurately visualizes the infected region in the CXR without segmentation. Deep fused images are trained by the Deep learning network (DLN) with residual connections. The largest standard database, comprised of 3500 TB CXR images and 3500 normal CXR images, is utilized for training and validating the recommended model. Specificity, sensitivity, Accuracy, and AUC are estimated to determine the performance of the proposed systems. The proposed system demonstrates a maximum testing accuracy of 99.2%, a sensitivity of 98.9%, a specificity of 99.6%, a precision of 99.6%, and an AUC of 99.4%, all of which are pretty high when compared to current state-of-the-art deep learning approaches for the prognosis of tuberculosis. To lessen the radiologist's time, effort, and reliance on the level of competence of the specialist, the suggested system named tbXpert can be deployed as a computer-aided diagnosis technique for tuberculosis.

Appositeness of Optimized and Reliable Machine Learning for Healthcare: A Survey.

Machine Learning (ML) has been categorized as a branch of Artificial Intelligence (AI) under the Computer Science domain wherein programmable machines imitate human learning behavior with the help of statistical methods and data. The Healthcare industry is one of the largest and busiest sectors in the world, functioning with an extensive amount of manual moderation at every stage. Most of the clinical documents concerning patient care are hand-written by experts, selective reports are machine-generated. This process elevates the chances of misdiagnosis thereby, imposing a risk to a patient's life. Recent technological adoptions for automating manual operations have witnessed extensive use of ML in its applications. The paper surveys the applicability of ML approaches in automating medical systems. The paper discusses most of the optimized statistical ML frameworks that encourage better service delivery in clinical aspects. The universal adoption of various Deep Learning (DL) and ML techniques as the underlying systems for a variety of wellness applications, is delineated by challenges and elevated by myriads of security. This work tries to recognize a variety of vulnerabilities occurring in medical procurement, admitting the concerns over its predictive performance from a privacy point of view. Finally providing possible risk delimiting facts and directions for active challenges in the future.

ADOPT: automatic deep learning and optimization-based approach for detection of novel coronavirus COVID-19 disease using X-ray images.

In the hospital, because of the rise in cases daily, there are a small number of COVID-19 test kits available. For this purpose, a rapid alternative diagnostic choice to prevent COVID-19 spread among individuals must be implemented as an automatic detection method. In this article, the multi-objective optimization and deep learning-based technique for identifying infected patients with coronavirus using X-rays is proposed. J48 decision tree approach classifies the deep feature of corona affected X-ray images for the efficient detection of infected patients. In this study, 11 different convolutional neural network-based (CNN) models (AlexNet, VGG16, VGG19, GoogleNet, ResNet18, ResNet50, ResNet101, InceptionV3, InceptionResNetV2, DenseNet201 and XceptionNet) are developed for detection of infected patients with coronavirus pneumonia using X-ray images. The efficiency of the proposed model is tested using k-fold cross-validation method. Moreover, the parameters of CNN deep learning model are tuned using multi-objective spotted hyena optimizer (MOSHO). Extensive analysis shows that the proposed model can classify the X-ray images at a good accuracy, precision, recall, specificity and F1-score rates. Extensive experimental results reveal that the proposed model outperforms competitive models in terms of well-known performance metrics. Hence, the proposed model is useful for real-time COVID-19 disease classification from X-ray chest images.Communicated by Ramaswamy H. Sarma.

COVID-19: Challenges and its Technological Solutions using IoT.

COVID-19 is a global pandemic that has affected many countries in a short span of time. People worldwide are susceptible to this deadly disease. To control the prevailing havoc of coronavirus, researchers are adopting techniques like plasma therapy, proning, medicines, etc. To stop the rapid spread of COVID-19, contact tracing is one of the important ways to check the infected people. This paper explains the various challenges people and health practitioners are facing due to COVID-19. In this paper, various ways with which the impact of COVID-19 can be controlled using IoT technology have been discussed. A six-layer architecture of IoT solutions for containing the deadly COVID-19 has been proposed. In addition to this, the role of machine learning techniques for diagnosing COVID-19 has been discussed in this paper, and a quick explanation of the unmanned aerial vehicle (UAVs) applications for contact tracing has also been specified. From the study conducted, it is evident that IoT solutions can be used in various ways for restricting the impact of COVID-19. Furthermore, IoT can be used in the healthcare sector to assure people's safety and good health with minimal costs.

The Application of Convolutional Neural Network Model in Diagnosis and Nursing of MR Imaging in Alzheimer's Disease.

The disease Alzheimer is an irrepressible neurologicalbrain disorder. Earlier detection and proper treatment of Alzheimer's disease can help for brain tissue damage prevention. The study was intended to explore the segmentation effects of convolutional neural network (CNN) model on Magnetic Resonance (MR) imaging for Alzheimer's diagnosis and nursing. Specifically, 18 Alzheimer's patients admitted to Indira Gandhi Medical College (IGMC) hospital were selected as the experimental group, with 18 healthy volunteers in the Ctrl group. Furthermore, the CNN model was applied to segment the MR imaging of Alzheimer's patients, and its segmentation effects were compared with those of the fully convolutional neural network (FCNN) and support vector machine (SVM) algorithms. It was found that the CNN model demonstrated higher segmentation precision, and the experimental group showed a higher clinical dementia rating (CDR) score and a lower mini-mental state examination (MMSE) score (P < 0.05). The size of parahippocompalgyrus and putamen was bigger in the Ctrl (P < 0.05). In experimental group, the amplitude of low-frequency fluctuation (ALFF) was positively correlated with the MMSE score in areas of bilateral cingulum gyri (r = 0.65) and precuneus (r = 0.59). In conclusion, the grey matter structure is damaged in Alzheimer's patients, and hippocampus ALFF and regional homogeneity (ReHo) is involved in the neuronal compensation mechanism of hippocampal damage, and the caregivers should take an active nursing method.

Velocity clamping-assisted adaptive salp swarm algorithm: balance analysis and case studies.

Salp swarm algorithm (SSA) is a recently proposed, powerful swarm-intelligence based optimizer, which is inspired by the unique foraging style of salps in oceans. However, the original SSA suffers from some limitations including immature balance between exploitation and exploration operators, slow convergence and local optimal stagnation. To alleviate these deficiencies, a modified SSA (called VC-SSA) with velocity clamping strategy, reduction factor tactic, and adaptive weight mechanism is developed. Firstly, a novel velocity clamping mechanism is designed to boost the exploitation ability and the solution accuracy. Next, a reduction factor is arranged to bolster the exploration capability and accelerate the convergence speed. Finally, a novel position update equation is designed by injecting an inertia weight to catch a better balance between local and global search. 23 classical benchmark test problems, 30 complex optimization tasks from CEC 2017, and five engineering design problems are employed to authenticate the effectiveness of the developed VC-SSA. The experimental results of VC-SSA are compared with a series of cutting-edge metaheuristics. The comparisons reveal that VC-SSA provides better performance against the canonical SSA, SSA variants, and other well-established metaheuristic paradigms. In addition, VC-SSA is utilized to handle a mobile robot path planning task. The results show that VC-SSA can provide the best results compared to the competitors and it can serve as an auxiliary tool for mobile robot path planning.

CNN Based Multiclass Brain Tumor Detection Using Medical Imaging.

Brain tumors are the 10th leading reason for the death which is common among the adults and children. On the basis of texture, region, and shape there exists various types of tumor, and each one has the chances of survival very low. The wrong classification can lead to the worse consequences. As a result, these had to be properly divided into the many classes or grades, which is where multiclass classification comes into play. Magnetic resonance imaging (MRI) pictures are the most acceptable manner or method for representing the human brain for identifying the various tumors. Recent developments in image classification technology have made great strides, and the most popular and better approach that has been considered best in this area is CNN, and therefore, CNN is used for the brain tumor classification issue in this paper. The proposed model was successfully able to classify the brain image into four different classes, namely, no tumor indicating the given MRI of the brain does not have the tumor, glioma, meningioma, and pituitary tumor. This model produces an accuracy of 99%.

Detection of Emotion of Speech for RAVDESS Audio Using Hybrid Convolution Neural Network.

Every human being has emotion for every item related to them. For every customer, their emotion can help the customer representative to understand their requirement. So, speech emotion recognition plays an important role in the interaction between humans. Now, the intelligent system can help to improve the performance for which we design the convolution neural network (CNN) based network that can classify emotions in different categories like positive, negative, or more specific. In this paper, we use the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) audio records. The Log Mel Spectrogram and Mel-Frequency Cepstral Coefficients (MFCCs) were used to feature the raw audio file. These properties were used in the classification of emotions using techniques, such as Long Short-Term Memory (LSTM), CNNs, Hidden Markov models (HMMs), and Deep Neural Networks (DNNs). For this paper, we have divided the emotions into three sections for males and females. In the first section, we divide the emotion into two classes as positive. In the second section, we divide the emotion into three classes such as positive, negative, and neutral. In the third section, we divide the emotions into 8 different classes such as happy, sad, angry, fearful, surprise, disgust expressions, calm, and fearful emotions. For these three sections, we proposed the model which contains the eight consecutive layers of the 2D convolution neural method. The purposed model gives the better-performed categories to other previously given models. Now, we can identify the emotion of the consumer in better ways.