Due to the Internet's explosive growth, network security is now a major concern; as a result, tra... more Due to the Internet's explosive growth, network security is now a major concern; as a result, tracking network traffic is essential for a variety of uses, including improving system efficiency, fixing bugs in the network, and keeping sensitive data secure. Firewalls are a crucial component of enterprise-wide security architectures because they protect individual networks from intrusion. The efficiency of a firewall can be negatively impacted by issues with its design, configuration, monitoring, and administration. Recent firewall security methods do not have the rigor to manage the vagueness that comes with filtering packets from the exterior. Knowledge representation and reasoning are two areas where fuzzy Petri nets (FPNs) receive extensive usage as a modeling tool. Despite their widespread success, FPNs' limitations in the security engineering field stem from the fact that it is difficult to represent different kinds of uncertainty. This article details the construction of a novel packet-filtering firewall model that addresses the limitations of current FPN-based filtering methods. The primary contribution is to employ Simplified Neutrosophic Petri nets (SNPNs) as a tool for modeling discrete event systems in the area of firewall packet filtering that are characterized by imprecise knowledge. Because of SNPNs' symbolic ability, the packet filtration model can be quickly and easily established, examined, enhanced, and maintained. Based on the idea that the ambiguity of a packet's movement can be described by if-then fuzzy production rules realized by the truth-membership function, the indeterminacy-membership function, and the falsity-membership functional, we adopt the neutrosophic logic for modelling PN transition objects. In addition, we simulate the dynamic behavior of the tracking system in light of the ambiguity inherent in packet filtering by presenting a two-level filtering method to improve the ranking of the filtering rules list. Results from experiments on a local area network back up the efficacy of the proposed method and illustrate how it can increase the firewall's susceptibility to threats posed by network traffic.
In recent years, Sign Language Recognition (SLR) has become an additional topic of discussion in ... more In recent years, Sign Language Recognition (SLR) has become an additional topic of discussion in the human-computer interface (HCI) field. The most significant difficulty confronting SLR recognition is finding algorithms that will scale effectively with a growing vocabulary size and a limited supply of training data for signer-independent applications. Due to its sensitivity to shape information, automated SLR based on hidden Markov models (HMMs) cannot characterize the confusing distributions of the observations in gesture features with sufficiently precise parameters. In order to simulate uncertainty in hypothesis spaces, many scholars provide an extension of the HMMs, utilizing higher-order fuzzy sets to generate interval-type-2 fuzzy HMMs. This expansion is helpful because it brings the uncertainty and fuzziness of conventional HMM mapping under control. The neutrosophic sets are used in this work to deal with indeterminacy in a practical SLR setting. Existing interval-type-2 fuzzy HMMs cannot consider uncertain information that includes indeterminacy. However, the neutrosophic hidden Markov model successfully identifies the best route between states when there is vagueness. This expansion is helpful because it brings the uncertainty and fuzziness of conventional HMM mapping under control. The neutrosophic three membership functions (truth, indeterminate, and falsity grades) provide more layers of autonomy for assessing HMM's uncertainty. This approach could be helpful for an extensive vocabulary and hence seeks to solve the scalability issue. In addition, it may function independently of the signer, without needing data gloves or any other input devices. The experimental results demonstrate that the neutrosophic HMM is nearly as computationally difficult as the fuzzy HMM but has a similar performance and is more robust to gesture variations.
Establishing internet access for mobile ad hoc networks (MANET) is a job that is both vital and c... more Establishing internet access for mobile ad hoc networks (MANET) is a job that is both vital and complex. MANET is used to build a broad range of applications, both commercial and non-commercial, with the majority of these apps obtaining access to internet resources. Since the gateways (GWs) are the central nodes in a MANET’s ability to connect to the internet, it is common practice to deploy numerous GWs to increase the capabilities of a MANET. Current routing methods have been adapted and optimized for use with MANET through the use of both conventional routing techniques and tree-based network architectures. Exploring new or tacking-failure GWs also increases network overhead but is essential given that MANET is a dynamic and complicated network. To handle these issues, the work presented in this paper presents a modified gateway discovery approach inspired by the quantum swarm intelligence technique. The suggested approach follows the non-root tree-based GW discovery category to ...
The emergence of geospatial big data has opened up new avenues for identifying urban environments... more The emergence of geospatial big data has opened up new avenues for identifying urban environments. Although both geographic information systems (GIS) and expert systems (ES) have been useful in resolving geographical decision issues, they are not without their own shortcomings. The combination of GIS and ES has gained popularity due to the necessity of boosting the effectiveness of these tools in resolving very difficult spatial decision-making problems. The clustering method generates the functional effects necessary to apply spatial analysis techniques. In a symmetric clustering system, two or more nodes run applications and monitor each other simultaneously. This system is more efficient than an asymmetric system since it utilizes all available hardware and does not maintain a node in a hot standby state. However, it is still a major issue to figure out how to expand and speed up clustering algorithms without sacrificing efficiency. The work presented in this paper introduces an ...
There is no doubting that contemporary technology creates new hazards, and these threats are many... more There is no doubting that contemporary technology creates new hazards, and these threats are many and significant, directly harming people's lives and threatening their stability. Because of the increased use of computers and Internet-connected cellphones in recent years, the problem of cybercrime has expanded substantially. Unquestionably, this kind of crime is now a reality that jeopardizes people's reputations and lives, therefore we must be aware of it to prevent being a victim. The exponential growth in internet connectedness is closely tied to a rise in cyberattack incidences, frequently with significant consequences. Malware is the weapon of choice for carrying out malicious intent in cyberspace, whether by exploiting pre-existing flaws or exploiting the unique properties of new technology. There is an urgent need in the cybersecurity area to develop more inventive and effective virus defense techniques. To do this, we first give an overview of the most often exploite...
It is crucial, while using healthcare data, to assess the advantages of data privacy against the ... more It is crucial, while using healthcare data, to assess the advantages of data privacy against the possible drawbacks. Data from several sources must be combined for use in many data mining applications. The medical practitioner may use the results of association rule mining performed on this aggregated data to better personalize patient care and implement preventive measures. Historically, numerous heuristics (e.g., greedy search) and metaheuristics-based techniques (e.g., evolutionary algorithm) have been created for the positive association rule in privacy preserving data mining (PPDM). When it comes to connecting seemingly unrelated diseases and drugs, negative association rules may be more informative than their positive counterparts. It is well-known that during negative association rules mining, a large number of uninteresting rules are formed, making this a difficult problem to tackle. In this research, we offer an adaptive method for negative association rule mining in vertically partitioned healthcare datasets that respects users' privacy. The applied approach dynamically determines the transactions to be interrupted for information hiding, as opposed to predefining them. This study introduces a novel method for addressing the problem of negative association rules in healthcare data mining, one that is based on the Tabu-genetic optimization paradigm. Tabu search is advantageous since it removes a huge number of unnecessary rules and item sets. Experiments using benchmark healthcare datasets prove that the discussed scheme outperforms state-of-the-art solutions in terms of decreasing side effects and data distortions, as measured by the indicator of hiding failure.
International Journal of Electrical and Computer Engineering (IJECE)
In recent times, fitness trackers and smartphones equipped with different sensors like gyroscopes... more In recent times, fitness trackers and smartphones equipped with different sensors like gyroscopes, accelerometers, global positioning system sensors and programs are used for recognizing human activities. In this paper, the results collected from these devices are used to design a system that can assist an application in monitoring a person’s health. The proposed system takes the raw sensor signals as input, preprocesses it and using machine learning techniques outputs the state of the user with minimum error. The objective of this paper is to compare the performance of different algorithms logistic regression (LR), support vector machine (SVM), k-nearest neighbor (k-NN) and random forest (RF). The algorithms are trained and tested with an original number of features as well as with transformed number of features (using linear discriminant analysis). The data with a smaller number of features is then used to visualize the high dimensional data. In this paper, each data point is mapp...
Face identification and biometric analytics is a modern domain of study and enormous algorithms i... more Face identification and biometric analytics is a modern domain of study and enormous algorithms in this aspect. Perceptual interface can be described as: highly immersive, multi-modal interfaces focused on normal human-to-human interactions, with the purpose of allowing users to communicate with software in a similar way to how they communicate with one another and the physical environment. It is nowadays quite effectual in face smile detection. Smile detection is a two-stage process. First you feel a face and then wait for a grin and in thousands of zones a motion detector splits the clip, Analyzing criteria like auto focus and facial flash level. When a human smile, the camera identifies a facial defect by identifying various parameters, It involves shutting your eyes, making your teeth transparent, folding your mouth, and lifting your lips. You should adjust the camera parameters to increase the sensitivity of the Smile automatic feature. When participants (with bangs, etc.) do not cover their faces, especially their eyes, authentic smile recognition is more successful. Helmets, masks or sunglasses can also be obstructed. For your subjects, you should have a wide and openmouthed smile. When the teeth are open and clear, the camera can even detect a smile better. The presented work focuses on analytics of face smile through Perceptual User Interfaces in biometric analytics for cumulative results.
Preventive Approach against HULK Attacks in Network Environment, 2020
With the increasing network based communication, the security and privacy are in concern from a l... more With the increasing network based communication, the security and privacy are in concern from a long time. Even the network assaults related to ransom ware are prevalent to get the extortion money from system administrators. In addition, the novel algorithm based zero attacks are in process very frequently by the cyber terrorists. With all such information, it is desirable to integrate the unique mechanisms to guard against the web portals against such attacks. One of the very powerful attacks is Distributed Denial of Service Attack (DDoS) which becomes more dangerous when associated with HTTP Unbearable Load King (HULK) as this attack choke down the network bandwidth and communication channels using malicious attempts of network access with the execution of specialized scripts. This research manuscript underlines the assorted dimensions of HULK attacks with the penetration level along with the tools and approaches which can be used to protect the network environment against such attacks.
Medical image segmentation has consistently been a significant topic of research and a prominent ... more Medical image segmentation has consistently been a significant topic of research and a prominent goal, particularly in computer vision. Brain tumor research plays a major role in medical imaging applications by providing a tremendous amount of anatomical and functional knowledge that enhances and allows easy diagnosis and disease therapy preparation. To prevent or minimize manual segmentation error, automated tumor segmentation, and detection became the most demanding process for radiologists and physicians as the tumor often has complex structures. Many methods for detection and segmentation presently exist, but all lack high accuracy. This paper's key contribution focuses on evaluating machine learning techniques that are supposed to reduce the effect of frequently found issues in brain tumor research. Furthermore, attention concentrated on the challenges related to level set segmentation. The study proposed in this paper uses the Population-based Artificial Bee Colony Clustering (P-ABCC) methodology to reliably collect initial contour points, which helps minimize the number of iterations and segmentation errors of the level-set process. The proposed model measures cluster centroids (ABC populations) and uses a level-set approach to resolve contour differences as brain tumors vary as they have irregular form, structure , and volume. The suggested model comprises of three major steps: first, pre-processing to separate the brain from the head and improves contrast stretching. Secondly, P-ABCC is used to obtain tumor edges that are utilized as an initial MRI sequence contour. The level-set segmentation is then used to detect tumor regions from all volume slices with fewer iterations. Results suggest improved model efficiency compared to state-of-the-art methods for both datasets BRATS 2019 and BRATS 2017. At BRATS 2019, dice progress was achieved for Entire Tumor (WT), Tumor Center (TC), and Improved This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 962 CMC, 2021, vol.68, no.1 Tumor (ET) by 0.03%, 0.03%, and 0.01% respectively. At BRATS 2017, an increase in precision for WT was reached by 5.27%.
Face Smile Detection is one of the critical spaces of research in the surge of digital image proc... more Face Smile Detection is one of the critical spaces of research in the surge of digital image processing in relationship with machine learning and predictive analysis. The key thought process of face smile detection is to break down and arrange the movements of a person on the lips and extraction of real feelings amid the situation under scrutiny. The execution of profound learning based classification and forecast approaches in relationship of HAAR based integration might be utilized for the higher level of classification with minimum error factors. This is on account of these methodologies are superior methodologies which can be utilized for face examination. Various algorithms are contrived so far still the execution of nature inspired methodologies and metaheuristic approaches are very noticeable and mindful with the minimum error rate. Grouped regular inspired methodologies contrived so far for taking care of the designing issues in arranged spaces yet there is immense extent of...
Human Face Recognition for forensic investigations and e-governance is widely adopted so that the... more Human Face Recognition for forensic investigations and e-governance is widely adopted so that the specific face points can be trained and further investigations can be done. In this approach, the key points of human face with the dynamic features are extracted and trained in the deep neural network model so that the intrinsic aspects of the human face can be realized and further can be used for the criminal investigation or social analytics based applications. In this research manuscript, the usage of deep learning based convolutional network is integrated for the human face analytics and recognition for diversified applications. It is done to have the cavernous evaluation patterns in multiple domains for the knowledge discovery and predictive features of the human face identification domain.
International Journal of Machine Learning and Computing, 2014
Gait based recognition is one of the emerging new biometric technology for human identification, ... more Gait based recognition is one of the emerging new biometric technology for human identification, surveillance and other security applications. Gait is a potential behavioral feature to identify humans at a distance based on their motion. The use of new methods for handling inaccurate information about gait features is of fundamental important. This paper deals with the design of an intelligent gait recognition system using interval type-2 fuzzy K-nearest neighbor (IT2FKNN) for diminishing the effect of uncertainty formed by variations in energy deviation image (EDI). The proposed system is built on top of the well-known principal component analysis (PCA) method that is utilized to remove correlation between the features and also to reduce its dimensionality. Our system employs IT2FKNN to compute fuzzy within and in-between class scatter matrices of PCA to refine classification results. This employment makes the system able to distinguish between normal, abnormal and suspicious walk of a person so that an alarming action may be taken well in time. Interval type-2 fuzzy set is involved to extend the membership values of each gait signatures by using several initial K in order to handle and manage uncertainty that exist in choosing initial K. The result of the experiments conducted on gait database show that the proposed gait recognition approach can obtain encouraging accurate recognition rate.
The smile detection approach is quite prominent with the face detection and thereby the enormous ... more The smile detection approach is quite prominent with the face detection and thereby the enormous implementations are prevalent so that the higher degree of accuracy can be achieved. The face smile detection is widely associated to have the forensic of faces of human beings so that the future predictions can be done. In chaos theory, the main strategy is to have the cavernous analytics on the single change and then to predict the actual faces in the analysis. In addition, the integration of Principal Component Analysis (PCA) is integrated to have the predictions with more accuracy. This work proposes to use the analytics on the parallel integration of PCA and chaos theory to enable the face smile and fake identifications to be made possible. The projected work is analyzed using assorted parameters and it has been found that the deep learning integration approach for chaos and PCA is quite important and performance aware in the multiple parameters with the different datasets in evalua...
Face detection is gaining the interest of marketers. A webcam can be integrated into a television... more Face detection is gaining the interest of marketers. A webcam can be integrated into a television and detect any face that walks by. The system then calculates the race, gender, and age range of the face. Once the information is collected, a series of advertisements can be played that is specific toward the detected race/gender/age. Face detection is used in biometrics, often as a part of (or together with) a facial recognition system. It is also used in video surveillance, human computer interface and image database management. This manuscript underlines the face smile detection approaches with the related dimensions in assorted domains and dimensions.
In the development of human–machine interfaces, facial expression analysis has attracted consider... more In the development of human–machine interfaces, facial expression analysis has attracted considerable attention, as it provides a natural and efficient way of communication. Congruence between facial and behavioral inference in face processing is considered a serious challenge that needs to be solved in the near future. Automatic facial expression is a difficult classification issue because of the high interclass variability caused by the significant interdependence of the environmental conditions on the face appearance caused by head pose, scale, and illumination occlusions from their variances. In this paper, an adaptive model for smile classification is suggested that integrates a row-transform-based feature extraction algorithm and a cascade classifier to increase the precision of facial recognition. We suggest a histogram-based cascade smile classification method utilizing different facial features. The candidate feature set was designed based on the first-order histogram proba...
International Journal of Computing and Business Research (IJCBR), 2017
Face Smile Detection is one of the critical spaces of research in the surge of digital image proc... more Face Smile Detection is one of the critical spaces of research in the surge of digital image processing in relationship with machine learning and predictive analysis. The key thought process of face smile detection is to break down and arrange the movements of a person on the lips and extraction of real feelings amid the situation under scrutiny. The execution of profound learning based classification and forecast approaches in relationship of HAAR based integration might be utilized for the higher level of classification with minimum error factors. This is on account of these methodologies are superior methodologies which can be utilized for face examination. Various algorithms are contrived so far still the execution of nature inspired methodologies and metaheuristic approaches are very noticeable and mindful with the minimum error rate. Grouped regular inspired methodologies contrived so far for taking care of the designing issues in arranged spaces yet there is immense extent of...
Due to the Internet's explosive growth, network security is now a major concern; as a result, tra... more Due to the Internet's explosive growth, network security is now a major concern; as a result, tracking network traffic is essential for a variety of uses, including improving system efficiency, fixing bugs in the network, and keeping sensitive data secure. Firewalls are a crucial component of enterprise-wide security architectures because they protect individual networks from intrusion. The efficiency of a firewall can be negatively impacted by issues with its design, configuration, monitoring, and administration. Recent firewall security methods do not have the rigor to manage the vagueness that comes with filtering packets from the exterior. Knowledge representation and reasoning are two areas where fuzzy Petri nets (FPNs) receive extensive usage as a modeling tool. Despite their widespread success, FPNs' limitations in the security engineering field stem from the fact that it is difficult to represent different kinds of uncertainty. This article details the construction of a novel packet-filtering firewall model that addresses the limitations of current FPN-based filtering methods. The primary contribution is to employ Simplified Neutrosophic Petri nets (SNPNs) as a tool for modeling discrete event systems in the area of firewall packet filtering that are characterized by imprecise knowledge. Because of SNPNs' symbolic ability, the packet filtration model can be quickly and easily established, examined, enhanced, and maintained. Based on the idea that the ambiguity of a packet's movement can be described by if-then fuzzy production rules realized by the truth-membership function, the indeterminacy-membership function, and the falsity-membership functional, we adopt the neutrosophic logic for modelling PN transition objects. In addition, we simulate the dynamic behavior of the tracking system in light of the ambiguity inherent in packet filtering by presenting a two-level filtering method to improve the ranking of the filtering rules list. Results from experiments on a local area network back up the efficacy of the proposed method and illustrate how it can increase the firewall's susceptibility to threats posed by network traffic.
In recent years, Sign Language Recognition (SLR) has become an additional topic of discussion in ... more In recent years, Sign Language Recognition (SLR) has become an additional topic of discussion in the human-computer interface (HCI) field. The most significant difficulty confronting SLR recognition is finding algorithms that will scale effectively with a growing vocabulary size and a limited supply of training data for signer-independent applications. Due to its sensitivity to shape information, automated SLR based on hidden Markov models (HMMs) cannot characterize the confusing distributions of the observations in gesture features with sufficiently precise parameters. In order to simulate uncertainty in hypothesis spaces, many scholars provide an extension of the HMMs, utilizing higher-order fuzzy sets to generate interval-type-2 fuzzy HMMs. This expansion is helpful because it brings the uncertainty and fuzziness of conventional HMM mapping under control. The neutrosophic sets are used in this work to deal with indeterminacy in a practical SLR setting. Existing interval-type-2 fuzzy HMMs cannot consider uncertain information that includes indeterminacy. However, the neutrosophic hidden Markov model successfully identifies the best route between states when there is vagueness. This expansion is helpful because it brings the uncertainty and fuzziness of conventional HMM mapping under control. The neutrosophic three membership functions (truth, indeterminate, and falsity grades) provide more layers of autonomy for assessing HMM's uncertainty. This approach could be helpful for an extensive vocabulary and hence seeks to solve the scalability issue. In addition, it may function independently of the signer, without needing data gloves or any other input devices. The experimental results demonstrate that the neutrosophic HMM is nearly as computationally difficult as the fuzzy HMM but has a similar performance and is more robust to gesture variations.
Establishing internet access for mobile ad hoc networks (MANET) is a job that is both vital and c... more Establishing internet access for mobile ad hoc networks (MANET) is a job that is both vital and complex. MANET is used to build a broad range of applications, both commercial and non-commercial, with the majority of these apps obtaining access to internet resources. Since the gateways (GWs) are the central nodes in a MANET’s ability to connect to the internet, it is common practice to deploy numerous GWs to increase the capabilities of a MANET. Current routing methods have been adapted and optimized for use with MANET through the use of both conventional routing techniques and tree-based network architectures. Exploring new or tacking-failure GWs also increases network overhead but is essential given that MANET is a dynamic and complicated network. To handle these issues, the work presented in this paper presents a modified gateway discovery approach inspired by the quantum swarm intelligence technique. The suggested approach follows the non-root tree-based GW discovery category to ...
The emergence of geospatial big data has opened up new avenues for identifying urban environments... more The emergence of geospatial big data has opened up new avenues for identifying urban environments. Although both geographic information systems (GIS) and expert systems (ES) have been useful in resolving geographical decision issues, they are not without their own shortcomings. The combination of GIS and ES has gained popularity due to the necessity of boosting the effectiveness of these tools in resolving very difficult spatial decision-making problems. The clustering method generates the functional effects necessary to apply spatial analysis techniques. In a symmetric clustering system, two or more nodes run applications and monitor each other simultaneously. This system is more efficient than an asymmetric system since it utilizes all available hardware and does not maintain a node in a hot standby state. However, it is still a major issue to figure out how to expand and speed up clustering algorithms without sacrificing efficiency. The work presented in this paper introduces an ...
There is no doubting that contemporary technology creates new hazards, and these threats are many... more There is no doubting that contemporary technology creates new hazards, and these threats are many and significant, directly harming people's lives and threatening their stability. Because of the increased use of computers and Internet-connected cellphones in recent years, the problem of cybercrime has expanded substantially. Unquestionably, this kind of crime is now a reality that jeopardizes people's reputations and lives, therefore we must be aware of it to prevent being a victim. The exponential growth in internet connectedness is closely tied to a rise in cyberattack incidences, frequently with significant consequences. Malware is the weapon of choice for carrying out malicious intent in cyberspace, whether by exploiting pre-existing flaws or exploiting the unique properties of new technology. There is an urgent need in the cybersecurity area to develop more inventive and effective virus defense techniques. To do this, we first give an overview of the most often exploite...
It is crucial, while using healthcare data, to assess the advantages of data privacy against the ... more It is crucial, while using healthcare data, to assess the advantages of data privacy against the possible drawbacks. Data from several sources must be combined for use in many data mining applications. The medical practitioner may use the results of association rule mining performed on this aggregated data to better personalize patient care and implement preventive measures. Historically, numerous heuristics (e.g., greedy search) and metaheuristics-based techniques (e.g., evolutionary algorithm) have been created for the positive association rule in privacy preserving data mining (PPDM). When it comes to connecting seemingly unrelated diseases and drugs, negative association rules may be more informative than their positive counterparts. It is well-known that during negative association rules mining, a large number of uninteresting rules are formed, making this a difficult problem to tackle. In this research, we offer an adaptive method for negative association rule mining in vertically partitioned healthcare datasets that respects users' privacy. The applied approach dynamically determines the transactions to be interrupted for information hiding, as opposed to predefining them. This study introduces a novel method for addressing the problem of negative association rules in healthcare data mining, one that is based on the Tabu-genetic optimization paradigm. Tabu search is advantageous since it removes a huge number of unnecessary rules and item sets. Experiments using benchmark healthcare datasets prove that the discussed scheme outperforms state-of-the-art solutions in terms of decreasing side effects and data distortions, as measured by the indicator of hiding failure.
International Journal of Electrical and Computer Engineering (IJECE)
In recent times, fitness trackers and smartphones equipped with different sensors like gyroscopes... more In recent times, fitness trackers and smartphones equipped with different sensors like gyroscopes, accelerometers, global positioning system sensors and programs are used for recognizing human activities. In this paper, the results collected from these devices are used to design a system that can assist an application in monitoring a person’s health. The proposed system takes the raw sensor signals as input, preprocesses it and using machine learning techniques outputs the state of the user with minimum error. The objective of this paper is to compare the performance of different algorithms logistic regression (LR), support vector machine (SVM), k-nearest neighbor (k-NN) and random forest (RF). The algorithms are trained and tested with an original number of features as well as with transformed number of features (using linear discriminant analysis). The data with a smaller number of features is then used to visualize the high dimensional data. In this paper, each data point is mapp...
Face identification and biometric analytics is a modern domain of study and enormous algorithms i... more Face identification and biometric analytics is a modern domain of study and enormous algorithms in this aspect. Perceptual interface can be described as: highly immersive, multi-modal interfaces focused on normal human-to-human interactions, with the purpose of allowing users to communicate with software in a similar way to how they communicate with one another and the physical environment. It is nowadays quite effectual in face smile detection. Smile detection is a two-stage process. First you feel a face and then wait for a grin and in thousands of zones a motion detector splits the clip, Analyzing criteria like auto focus and facial flash level. When a human smile, the camera identifies a facial defect by identifying various parameters, It involves shutting your eyes, making your teeth transparent, folding your mouth, and lifting your lips. You should adjust the camera parameters to increase the sensitivity of the Smile automatic feature. When participants (with bangs, etc.) do not cover their faces, especially their eyes, authentic smile recognition is more successful. Helmets, masks or sunglasses can also be obstructed. For your subjects, you should have a wide and openmouthed smile. When the teeth are open and clear, the camera can even detect a smile better. The presented work focuses on analytics of face smile through Perceptual User Interfaces in biometric analytics for cumulative results.
Preventive Approach against HULK Attacks in Network Environment, 2020
With the increasing network based communication, the security and privacy are in concern from a l... more With the increasing network based communication, the security and privacy are in concern from a long time. Even the network assaults related to ransom ware are prevalent to get the extortion money from system administrators. In addition, the novel algorithm based zero attacks are in process very frequently by the cyber terrorists. With all such information, it is desirable to integrate the unique mechanisms to guard against the web portals against such attacks. One of the very powerful attacks is Distributed Denial of Service Attack (DDoS) which becomes more dangerous when associated with HTTP Unbearable Load King (HULK) as this attack choke down the network bandwidth and communication channels using malicious attempts of network access with the execution of specialized scripts. This research manuscript underlines the assorted dimensions of HULK attacks with the penetration level along with the tools and approaches which can be used to protect the network environment against such attacks.
Medical image segmentation has consistently been a significant topic of research and a prominent ... more Medical image segmentation has consistently been a significant topic of research and a prominent goal, particularly in computer vision. Brain tumor research plays a major role in medical imaging applications by providing a tremendous amount of anatomical and functional knowledge that enhances and allows easy diagnosis and disease therapy preparation. To prevent or minimize manual segmentation error, automated tumor segmentation, and detection became the most demanding process for radiologists and physicians as the tumor often has complex structures. Many methods for detection and segmentation presently exist, but all lack high accuracy. This paper's key contribution focuses on evaluating machine learning techniques that are supposed to reduce the effect of frequently found issues in brain tumor research. Furthermore, attention concentrated on the challenges related to level set segmentation. The study proposed in this paper uses the Population-based Artificial Bee Colony Clustering (P-ABCC) methodology to reliably collect initial contour points, which helps minimize the number of iterations and segmentation errors of the level-set process. The proposed model measures cluster centroids (ABC populations) and uses a level-set approach to resolve contour differences as brain tumors vary as they have irregular form, structure , and volume. The suggested model comprises of three major steps: first, pre-processing to separate the brain from the head and improves contrast stretching. Secondly, P-ABCC is used to obtain tumor edges that are utilized as an initial MRI sequence contour. The level-set segmentation is then used to detect tumor regions from all volume slices with fewer iterations. Results suggest improved model efficiency compared to state-of-the-art methods for both datasets BRATS 2019 and BRATS 2017. At BRATS 2019, dice progress was achieved for Entire Tumor (WT), Tumor Center (TC), and Improved This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 962 CMC, 2021, vol.68, no.1 Tumor (ET) by 0.03%, 0.03%, and 0.01% respectively. At BRATS 2017, an increase in precision for WT was reached by 5.27%.
Face Smile Detection is one of the critical spaces of research in the surge of digital image proc... more Face Smile Detection is one of the critical spaces of research in the surge of digital image processing in relationship with machine learning and predictive analysis. The key thought process of face smile detection is to break down and arrange the movements of a person on the lips and extraction of real feelings amid the situation under scrutiny. The execution of profound learning based classification and forecast approaches in relationship of HAAR based integration might be utilized for the higher level of classification with minimum error factors. This is on account of these methodologies are superior methodologies which can be utilized for face examination. Various algorithms are contrived so far still the execution of nature inspired methodologies and metaheuristic approaches are very noticeable and mindful with the minimum error rate. Grouped regular inspired methodologies contrived so far for taking care of the designing issues in arranged spaces yet there is immense extent of...
Human Face Recognition for forensic investigations and e-governance is widely adopted so that the... more Human Face Recognition for forensic investigations and e-governance is widely adopted so that the specific face points can be trained and further investigations can be done. In this approach, the key points of human face with the dynamic features are extracted and trained in the deep neural network model so that the intrinsic aspects of the human face can be realized and further can be used for the criminal investigation or social analytics based applications. In this research manuscript, the usage of deep learning based convolutional network is integrated for the human face analytics and recognition for diversified applications. It is done to have the cavernous evaluation patterns in multiple domains for the knowledge discovery and predictive features of the human face identification domain.
International Journal of Machine Learning and Computing, 2014
Gait based recognition is one of the emerging new biometric technology for human identification, ... more Gait based recognition is one of the emerging new biometric technology for human identification, surveillance and other security applications. Gait is a potential behavioral feature to identify humans at a distance based on their motion. The use of new methods for handling inaccurate information about gait features is of fundamental important. This paper deals with the design of an intelligent gait recognition system using interval type-2 fuzzy K-nearest neighbor (IT2FKNN) for diminishing the effect of uncertainty formed by variations in energy deviation image (EDI). The proposed system is built on top of the well-known principal component analysis (PCA) method that is utilized to remove correlation between the features and also to reduce its dimensionality. Our system employs IT2FKNN to compute fuzzy within and in-between class scatter matrices of PCA to refine classification results. This employment makes the system able to distinguish between normal, abnormal and suspicious walk of a person so that an alarming action may be taken well in time. Interval type-2 fuzzy set is involved to extend the membership values of each gait signatures by using several initial K in order to handle and manage uncertainty that exist in choosing initial K. The result of the experiments conducted on gait database show that the proposed gait recognition approach can obtain encouraging accurate recognition rate.
The smile detection approach is quite prominent with the face detection and thereby the enormous ... more The smile detection approach is quite prominent with the face detection and thereby the enormous implementations are prevalent so that the higher degree of accuracy can be achieved. The face smile detection is widely associated to have the forensic of faces of human beings so that the future predictions can be done. In chaos theory, the main strategy is to have the cavernous analytics on the single change and then to predict the actual faces in the analysis. In addition, the integration of Principal Component Analysis (PCA) is integrated to have the predictions with more accuracy. This work proposes to use the analytics on the parallel integration of PCA and chaos theory to enable the face smile and fake identifications to be made possible. The projected work is analyzed using assorted parameters and it has been found that the deep learning integration approach for chaos and PCA is quite important and performance aware in the multiple parameters with the different datasets in evalua...
Face detection is gaining the interest of marketers. A webcam can be integrated into a television... more Face detection is gaining the interest of marketers. A webcam can be integrated into a television and detect any face that walks by. The system then calculates the race, gender, and age range of the face. Once the information is collected, a series of advertisements can be played that is specific toward the detected race/gender/age. Face detection is used in biometrics, often as a part of (or together with) a facial recognition system. It is also used in video surveillance, human computer interface and image database management. This manuscript underlines the face smile detection approaches with the related dimensions in assorted domains and dimensions.
In the development of human–machine interfaces, facial expression analysis has attracted consider... more In the development of human–machine interfaces, facial expression analysis has attracted considerable attention, as it provides a natural and efficient way of communication. Congruence between facial and behavioral inference in face processing is considered a serious challenge that needs to be solved in the near future. Automatic facial expression is a difficult classification issue because of the high interclass variability caused by the significant interdependence of the environmental conditions on the face appearance caused by head pose, scale, and illumination occlusions from their variances. In this paper, an adaptive model for smile classification is suggested that integrates a row-transform-based feature extraction algorithm and a cascade classifier to increase the precision of facial recognition. We suggest a histogram-based cascade smile classification method utilizing different facial features. The candidate feature set was designed based on the first-order histogram proba...
International Journal of Computing and Business Research (IJCBR), 2017
Face Smile Detection is one of the critical spaces of research in the surge of digital image proc... more Face Smile Detection is one of the critical spaces of research in the surge of digital image processing in relationship with machine learning and predictive analysis. The key thought process of face smile detection is to break down and arrange the movements of a person on the lips and extraction of real feelings amid the situation under scrutiny. The execution of profound learning based classification and forecast approaches in relationship of HAAR based integration might be utilized for the higher level of classification with minimum error factors. This is on account of these methodologies are superior methodologies which can be utilized for face examination. Various algorithms are contrived so far still the execution of nature inspired methodologies and metaheuristic approaches are very noticeable and mindful with the minimum error rate. Grouped regular inspired methodologies contrived so far for taking care of the designing issues in arranged spaces yet there is immense extent of...
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Papers by Oday Ali