ALBERT

Description: This paper represents automatic facial expression analysis method named Local Directional Stigma Mean Patterns (LDSMP) for automatic facial expression analysis and image retrieval using content based facial expression image retrieval and CNN. The traditional local patterns such as Local Binary Patterns (LBP) and Local Ternary Patterns (LTP) are applied for face recognition and expression analysis, calculated using relationship between the center pixel and neighboring pixels. The proposed method calculates the eight directional difference values then divided into the three ranges based on threshold values. Thus, the values are substituted with basic three positive values (+3, +2, +1) and three negative values (-3, -2, -1) to get more sensitive information from an image rather than aforementioned methods. The threshold can be select either static which is selected by user or dynamic is evaluated from image itself and supports to improve the efficiency. The performance of the proposed method is further improved by giving this patterns as input to the Convolutional Neural Networks (CNN) and compared with the existing methods LBP, LTP, and Directional Binary Code (DBC) in terms of Average Precision (AP), Average Recall (AR), and Average Retrieval Rate (ARR) using standard databases COREL 10K (DB1) and JAFFE (The Japanese Female Facial Expression) (DB2) and Extended Cohn-Kanade (CK +) (DB3) dataset.

Description: The objective of this study is to evaluate areas of applicability of Industry 4.0 or the Fourth Industrial Revolution (4IR) to the Agriculture sector in India and how significant benefits can be delivered to the farmers by focusing on selected use cases. Agriculture has been the backbone of Indian economy for centuries, however, its contribution to the India’s economy, measured as a per cent of Gross Domestic Product (GDP), has declined over the last decade. Farm productivity has only marginally improved during this timeframe. At the same time, a surge in expected demand for food grains is expected, posing a significant challenge to the demand-supply parity. To solve this conundrum, 4IR Technologies as the umbrella framework comprising of cognitive internet of things (IoT), big data analytics, drones/spatial technologies and digital user experience, were evaluated in this research study. A baseline assessment of the digital use cases of 4IR Technologies that have already been leveraged in India was conducted. The list of these digital use cases was then enhanced and prioritized using Delphi technique, considering economic value of the use cases to the Indian farmers as well as ease of their implementation.

Description: The growth of web has touched everyone’s life from an economist, entrepreneur, and academician to a farmer. The agriculture sector is quite important for any country’s growth. Farmers can also be benefited if they are provided with relevant information they need. The reason that it is not happening on a large scale is due to many reasons. Information is available in different formats, platforms and it is highly unstructured. The availability and real time usage of this information is prohibited mainly by the way it is represented. Recently, Ontology has emerged as one of very expressive knowledge representation scheme which enables gathering information from heterogeneous sources and creates a common data model that is shared and agreed upon in diverse domains. The interoperability and deduction capabilities that Ontology offers are very useful for generating new knowledge. Most of the work pertaining to ontology development in agriculture domain is crop specific, where information about fertilizers is confined to a particular crop only. In this paper, we have designed and developed a generic ontology for fertilizers. Fertilizer itself is a concept which should be modeled independently. Even for crop specific ontology, fertilizer is one of the most important concept to be captured. So, it needs to be represented independently. Finding this research gap, an ontology in fertilizer subdomain is developed, taking into account the various issues that are faced while constructing an ontology and the enormous amount of data that is available but is not easy to structure and present in the form of ontology. We have also validated the Ontology using an available tool and domain experts have also validated the developed Ontology.

Description: The demand for a providing QoS adaptive routing over IoT networks is always a challenge among current research community. This research work KHAI proposes a framework for QoS-adaptive routing approach, which incorporates Krill Herd optimization model over IoT network. Variable QoS user preference and handling differential service types over a scalable IoT network shows that challenge for designing an adaptive QoS is a must. Research survey suggest that major works have been carried out on bandwidth appreciable services and route management approaches. Hence QoS adaptive user defined services, which adapt to variable service priority levels based on user demand and network resource utilization is proposed in this research work. The performance analysis of proposed approach shows an improved throughput of 97.51 Mbps and minimal packet loss of 37.29% over a session in comparison to traditional computational approaches. Considering large scale of interconnected IoT devices, proposed approach delivers near optimal solution of throughput and adaptive utilization of network resources.

Description: One of the biometric techniques utilized to predict the human is based on the iris. The recognition of iris is performed by discovering an individual without human intervention utilizing the iris of human eyes. Iris offers distinct information about the person. This research presents deep learning strategy for performing iris recognition. Primarily, image is pre-processed to obtain exact region iris. Then, region of iris is extracted using Hough Transform, followed by segmentation and normalization of iris region using Daugman’s rubber sheet model. Once segmentation is performed, the features are generated with ScaT-LOOP that is the combination of Scattering Transform (ST), Tetrolet transforms (TT), Local Gradient Pattern (LGP) and Local Optimal Oriented Pattern (LOOP). Finally, steepest gradient-based Deep Belief Network (DBN) is utilized for recognizing the iris. The performance of iris recognition using the DBN classifier is computed based on accuracy, False Rejection Rate (FRR), and False Acceptance Rate (FAR). The proposed method achieves maximum accuracy of 97.96%, minimal FAR of 0.493%, and minimal FRR of 0.48% that indicates its superiority.

Description: One of the familiar distributed technologies for sharing computing resources through internet is a cloud computing technology. One need not setup all computing resources on their own to design their applications. They can own as much they want by requesting computing resources through net. These resources are shared between users upon request by properly scheduling tasks in cloud. The process of scheduling tasks is to be optimized to share the resources very fast. The paper proposes a cluster medoid based task scheduling technique KMPS (K-medoid particle swarm approach) for minimizing the makespan. KMPS uses the merits of both Particle Swarm Optimization (PSO) and k-medoid approaches with added weights concept. Experimental results have shown that KMPS has optimized the results of make span and it is most suitable one for cloud computing.

Description: Extraction of knowledge data from knowledge database using natural language query is a difficult task. Different types of natural language processing (NLP) techniques have been developed to handle this knowledge data extraction task. This paper proposes an automated query-response model termed Extended Automated Knowledge Provider System (EAKPS) that can manage various types of natural language queries from user. The EAKPS uses combination based technique and it can handle assertive, interrogative, imperative, compound and complex type query sentences. The algorithm of EAKPS generates structure query language (SQL) for each natural language query to extract knowledge data from the knowledge database resident within the EAKPS. Extraction of noun or noun phrases is another issue in natural language query processing. Most of the times, determiner, preposition and conjunction are prefixed to a noun or noun phrase and it is difficult to identify the noun/noun phrase with prefix during query processing. The proposed system is able to identify these prefixes and extract exact noun or noun phrases from natural language queries without any manual intervention.

Description: The internet of things (IoT) is a multiple devices, which connects with the internet for communication, in order to obtain the updated from the cloud. The fog can act as a controller and it is located between the IoT devices and cloud. The major attacks like de-synchronization, and disclosure has arises in the devices, this has been prevented. The major contribution in this work is key generation and authentication, for key generation the “advanced encryption standard algorithm” is developed, in which the new and old keys are generated. The encryption is done under the source side, and decryption is done under the device side. The fog security is maintained through “device tag, and bit wise XOR rotational algorithm”. The security, and the computational complexity is defined in this work and it is given in table format. The implementations are carried out in the MATLAB R2016 a. The proposed algorithm is compared with the existing protocols like LMAP, M2AP, EMAP, SASI, and RAPP, from the comparison the proposed methodology makes the better knowledge about the security and prevents from various attacks.

Description: Named Data networking is an instance of information centric networking, aims to improve the performance of the Internet by using in-network caching at storage-enabled routers and provide name based content access. However, name based content access and in-network caching make Name Data network vulnerable to new security attacks like cache pollutions, cache poisoning, false locality, cache snooping and interest flooding, etc. In this paper, we have evaluated NDN security principles, the impact of threats, ratified various security enablers, and built-in mitigation actions to combat security attacks. We have systematically applied SecRam in NDN for statistical security risk assessment, identification of run time threats, and assessment of available methods to mitigate these threats, as SecRam considers operational focus areas and proved useful for identification and severity assessment of run time threats. We have modified SecRam and used it in an entirely different domain, i.e., to a computer network, as SESAR proposed SecRam specifically for ATM systems that cannot be directly applied to another context. According to the best of our knowledge, it is the first attempt for a complete risk assessment of NDN. We have concluded this paper by defining a set of open security challenges that should be considered by future researchers.

Description: This paper proposes a diagnostic system, ESECFDS, to solve the problem of excessive smoke emission from a faulty car, based on Case-Based Reasoning (CBR) Methodology that accepts a user query as a new case, compares it with several stored cases in a Case-Base (CB) based on a similarity score using Jaccard Similarity method and provides feedback result to the user. The CBR cycle R4 Model of Aamodt and Plaza has been modified to R5 Model to improve the time complexity of the proposed algorithm of the system over the popular R4 Model.

Description: Recommendation systems provide reliable and relevant recommendations to users and also enable users’ trust on the website. This is achieved by the opinions derived from reviews, feedbacks and preferences provided by the users when the product is purchased or viewed through social networks. This integrates interactions of social networks with recommendation systems which results in the behavior of users and user’s friends. The techniques used so far for recommendation systems are traditional, based on collaborative filtering and content based filtering. This paper provides a novel approach called User-Opinion-Rating (UOR) for building recommendation systems by taking user generated opinions over social networks as a dimension. Two tripartite graphs namely User-Item-Rating and User-Item-Opinion are constructed based on users’ opinion on items along with their ratings. Proposed approach quantifies the opinions of users and results obtained reveal the feasibility.

Description: Mobile cloud computing (MCC) is a technology which provides cloud server resources to mobile users with optimized latency. MCC allows mobile device to access cloud resources and to offload tasks to cloud let servers at any time and from anywhere. The cloud let servers are attached to wireless Access points. But mobility plays an important role which leads to the loss of connectivity of mobile devices because of varying signal strengths. On the other hand, optimal code execution is a challenge. In this paper, a connectivity base mobility prediction method is proposed to assign the cloud resources to the user without loss in the connection. The past accessing history of the users and path loss factors are taken into consideration to predict proper access point. From the performance evaluation the performance of the proposed method is increased by 15.23% when compared to other existing methods.

Description: For emotion recognition, here the features extracted from prevalent speech samples of Berlin emotional database are pitch, intensity, log energy, formant, mel-frequency ceptral coefficients (MFCC) as base features and power spectral density as an added function of frequency. In these work seven emotions namely anger, neutral, happy, Boredom, disgust, fear and sadness are considered in our study. Temporal and Spectral features are considered for building AER(Automatic Emotion Recognition) model. The extracted features are analyzed using Support Vector Machine (SVM) and with multilayer perceptron (MLP) a class of feed-forward ANN classifiers is/are used to classify different emotional states. We observed 91% accuracy for Angry and Boredom emotional classes by using SVM and more than 96% accuracy using ANN and with an overall accuracy of 87.17% using SVM, 94% for ANN.

Description: In new age scenario Prisons are meant not only to punish the convict but also correct the situation and habits of the convicts, who is responsible for inflicting harm on the victim. But family members of the convict are also the victim in the process and situation. Convicts family face the horrific situation during this process. Prisoner’s families are maltreated directly and indirectly by the society. They live in destitution. Imprisonment of family member not only diminishes the earnings of adult men but also reduces familial resources for the basic necessities of life. The family members have to sacrifice their children’s education, ancestor’s property, past savings, in some cases even necessary wants of their life. We always think about the victim on whom the harm has been directly inflicted and completely ignore the harm inflicted on the kin of the convict. The present study is an endeavour to bring to light the economic vulnerability of families of convicts in a prison in Bhubaneswar. After interviewing the family members of prisoners, statistical analysis is done through SPSS. The findings are elaborated in narrative manner so that the findings will be helpful for policy makers in future.

Description: As the shapes of breast cell are diverse and there is adherent between cells, fast and accurate segmentation for breast cell remains a challenging task. In this paper, an automatic segmentation algorithm for breast cell image is proposed, which focuses on the segmentation of adherent cells. First of all, breast cell image enhancement is carried out by the staining regularization. Then, the cells and background are separated by Multi-scale Convolutional Neural Network (CNN) to obtain the initial segmentation results. Finally, the Curvature Scale Space (CSS) corner detection is used to segment adherent cells. Experimental results show that the proposed algorithm can achieve 93.01% accuracy, 93.93% sensitivity and 95.69% specificity. Compared with other segmentation algorithms of breast cell, the proposed algorithm can not only solve the difficulty of segmenting adherent cells, but also improve the segmentation accuracy of adherent cells.

Description: Rapid growth in technology and information lead the human to witness the improved growth in velocity, volume of data, and variety. The data in the business organizations demonstrate the development of big data applications. Because of the improving demand of applications, analysis of sophisticated streaming big data tends to become a significant area in data mining. One of the significant aspects of the research is employing deep learning approaches for effective extraction of complex data representations. Accordingly, this survey provides the detailed review of big data classification methodologies, like deep learning based techniques, Convolutional Neural Network (CNN) based techniques, K-Nearest Neighbor (KNN) based techniques, Neural Network (NN) based techniques, fuzzy based techniques, and Support vector based techniques, and so on. Moreover, a detailed study is made by concerning the parameters, like evaluation metrics, implementation tool, employed framework, datasets utilized, adopted classification methods, and accuracy range obtained by various techniques. Eventually, the research gaps and issues of various big data classification schemes are presented.

Description: Here, an endeavor has been made to predict the correspondence between rainfall and runoff and modeling are demonstrated using Feed Forward Back Propagation Neural Network (FFBPNN), Back Propagation Neural Network (BPNN), and Cascade Forward Back Propagation Neural Network (CFBPNN), for predicting runoff. Various indicators like mean square error (MSE), Root Mean Square Error (RMSE), and coefficient of determination (R2) for training and testing phase are used to appraise performance of model. BPNN performs paramount among three networks having model architecture 4-5-1 utilizing Log-sig transfer function, having R2 for training and testing is correspondingly 96.43 and 95.98. Similarly for FFBPNN, with Tan-sig function preeminent model architecture is seen to be 4-5-1 which possess MSE training and testing value 0.000483, 0.001025, RMSE training and testing value 0.02316, 0.03085 and R2 for training and testing as 0.9925, 0.9611, respectively. But for FFBPNN the value of R2 in training and testing is 0.8765 0.8976. Outcomes on the whole recommend that assessment of runoff is suitable to BPNN as contrasted to CFBPNN and FFBPNN. This consequence helps to plan, arrange and manage hydraulic structures of watershed.

Description: Mobile Grid network connects large number of mobile devices like smartphones, tablets, PDAs, wireless digital medical equipment’s etc for the purpose of sharing their resources and performing the task collaboratively and cooperatively. The mobile nodes participating in the mobile grid are autonomous and open in nature making them more vulnerable to data and control attacks made by malicious or selfish nodes. Preventing these malicious or selfish nodes and identifying the trusted nodes to participate in the network is an NP-hard problem. To recognize trusted nodes in the mobile grid system a novel trust management model is proposed in this paper by applying an elitist multi objective optimization algorithm Non-dominated Sorting Genetic Algorithm-II (NSGA-II). The proposed trust management model assesses the trust index of each mobile node in the network using various evaluation factors or attributes and then obtains the non-dominated set of trusted nodes in each front. Comparative analysis of the proposed trust model shows that the proposed model can be a potential candidate for implementing trust management in mobile grid network.

Description: Recent advancement in technologies such as Cloud, Internet of Things etc., leads to the increase usage of mobile computing. Present day mobile computing are too sophisticated and advancement are reaching great heights. Moreover, the present day mobile network suffers due to external and internal intrusions within and outside networks. The existing security systems to protect the mobile networks are incapable to detect the recent attacks. Further, the existing security system completely depends on the traditional signature and rule based approaches. Recent attacks have the property of not fluctuating its behaviour during attack. Hence, a robust Intrusion Detection System (IDS) is desirable. In order to address the above mentioned issue, this paper proposed a robust IDS using Machine Learning Techniques (MLT). The key of using MLT is to utilize the power of ensembles. The ensembles of classifier used in this paper are Random Forest (RF), KNN, Naïve Bayes (NB), etc. The proposed IDS is experimentally tested and validated using a secure test bed. The experimental results also confirms that the proposed IDS is robust enough to withstand and detect any form of intrusions and it is also noted that the proposed IDS outperforms the state of the art IDS with more than 95% accuracy.

Description: Nowadays, the speech recognition applications can be found in several activities, and their existence as a field of study and research lasts for a long time. Although, many studies deal with different problems, in security-related areas, biometric identification, access to the Smartphone… Etc. In automatic speech recognition (ASR) systems, hidden Markov models (HMMs) have widely used for modeling the temporal speech signal. In order to optimize HMM parameters (i.e., observation and transition probabilities), iterative algorithms commonly used such as Forward-Backward or Baum-Welch. In this article, we propose to use the bacterial foraging optimization algorithm (BFOA) to enhance HMM with Gaussian mixture densities. As a global optimization algorithm of current interest, BFOA has proven itself for distributed optimization and control. Our experimental results show that the proposed approach yields a significant improvement of the transcription accuracy at signal/noise ratios greater than 15 dB.

Description: Many heart diseases can be identified and cured at an early stage by studying the changes in the features of electrocardiogram (ECG) signal. Myocardial Infarction (MI) is the serious cause of death worldwide. If MI can be detected early, the death rate will reduce. In this paper, an algorithm to detect MI in an ECG signal using Daubechies wavelet transform technique is developed. The ECG signal-denoising is performed by removing the corresponding wavelet coefficients at higher scale. After denoising, an important step towards identifying an arrhythmia is the feature extraction from the ECG. Feature extraction is carried out to detect the R peaks of the ECG signal. Since as R peak is having the highest amplitude, and therefore it is detected in the first round, subsequently location of other peaks are determined. Having completed the preprocessing and the feature extraction the MI is detected from the ECG based on inverted T wave logic and ST segment elevation. The algorithm was evaluated using MIT-BIH database and European database satisfactorily.

Description: In image thresholding segmentation, gray level of pixels is the basic element to describe images. Besides, the gradient information of pixels is also a key feature to represent image space distribution. Therefore, the co-occurrence probability of gray and gradient of pixels is an effective information to describe image. In this paper, gray-gradient asymmetrical co-occurrence matrix is constructed, uniformity probability of image region is produced, and a minimum square distance criterion function based on gray-gradient co-occurrence matrix is proposed to measure the deviation between original and binary images. Comparing with gray-gray asymmetrical co-occurrence matrix and relative entropy-based symmetrical co-occurrence matrix method, the proposed method can obtain more complete segmentation results, especially for small-size object extraction. The peak signal to noise ratio probability also shows the better segmentation performance of our proposed method.