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Biometric identification technology has become increasingly common in our daily lives as the requirement for information protection and control legislation has grown around the world. The unimodal biometric systems use only biometric traits to authenticate the user which is trustworthy but it possesses various limitations such as susceptibility to attacks, noise occurring in a dataset, non-universality challenges, etc. Multimodal biometrics technology has the potential to avoid the various fundamental constraints of unimodal biometric systems and also it has garnered interest and popularity in this respect. In this research, an efficient multimodal biometric recognition system based on a deep learning approach is proposed. The structure is implemented around convolutional neural networks (CNN) in which feature extraction and Softmax classifier are used to identify images. This method employs three CNN models for iris, face, and fingerprint were integrated to create the system. The two levels of fusion strategy such as feature level fusion and score level fusion were employed. The efficiency of the proposed model is evaluated based on the two most popular multimodal datasets as SDUMLA-HMT and BiosecureID biometric dataset. The result analysis demonstrates that the proposed multimodal biometric recognition provides the enhanced result with higher accuracy of 99.92%, a lower equal error rate of 0.10% on feature level, and 0.08% on score level fusion. Similarly, the average FAR is 0.09% and the average FRR is 0.06%. Because of this enhanced result, the proposed approach is computationally efficient.

Transmission of the information in any form requires security. Security protocols used for communication rely on the use of random numbers. Pseudo-random numbers are required with good statistical properties and efficiency. The use of a single chaotic map may not produce enough randomness. The turbulence is padded into the existing map to improve its chaotic behaviour and increase the periodicity. A Pseudo-random number generator (PRNG) with this architecture is devised to generate random bit sequences from secret keys. The statistical properties of newly constructed PRNG are tested with NIST SP 800–22 statistical test suite and were shown to have good randomness. To ensure its usability in cryptographic applications, we analysed the size of its key space, key sensitivity, and performance speed. The test results show that the newly designed PRNG has a 3.6% increase in key space and a 5% increase in its performance speed compared to existing chaotic PRNGs. The novel PRNG with faster performance is found suitable for lightweight cryptographic applications.

Biometric recognition approaches have overwhelmed major issues in traditional authentication systems. Privacy invasion and security are the needed features in the effective development of biometric recognition systems. Recently some biometric techniques have been suffered to hacking trails also. Hence there is a despairing need to propose a novel cancelable iris recognition scheme. The prime objective of this research is to create a transformed cancelable biometric template through an encryption function and a one-way transformation function. The random projection matrix is employed here to generate the first feature vector. Then an encrypted cancelable iris code is generated using Double Random Phase Encryption (DRPE) in the Fractional Fourier Transform (FFT) domain. The proposed framework utilizes both the left and right iris image of a person and generates a single cancelable iris template. This feature guarantees the privacy-preserving cancelable iris code generation for secure authentication. The experiment is carried on two state-of-art datasets CASIA Iris V4 and IITD iris to affirm the efficacy of the proposed methodology. The result analysis witnessed a promising accuracy of 99.59%, a recognition rate of 99.88% with a lower Equal Error Rate (ERR) of 0.46%. It has also been proved that the proposed approach is computationally efficient as it recognizes the iris code with less recognition time of 7 ms with a maximum true positive and true negative rate of 100%.

Ayurvedic medicines are categorized into seven constitutional forms ‘Prakriti’ which is a constituent in the Ayurvedic system of medicine to determine drought tolerance and drug responsiveness. Prakriti assessment entails a thorough physical examination as well as queries about physiological or behavioral characteristics. The prevalence of certain "doshas" is attributed by Ayurveda to the fundamental constituent of a person. Vata, pitta, and Kapha are the three main doshas mentioned. Ayurveda-dosha studies have been used for a long time, but the quantitative reliability measurement of these diagnostic methods still lags. The careful and appropriate analysis leads to an effective treatment. In this paper, we demonstrate the result of certain machine learning methods like Support Vector Machine (SVM), Naive Bayes (NB), Decision Tree (DT), K-Nearest Neighbour (KNN), Artificial Neural Network (ANN), and Adaboost algorithm for various performance characteristics to predict human body constituencies. From the observations of results it is shown that the AdaBoost algorithm with hyperparameter tuning provides enhanced accuracy and recall of 0.97, precision and F-score of 0.96, the lower RSME value obtained is 0.64. The experimental results reveal that the improved model, which is based on ensemble learning methods, outperforms traditional methods significantly. According to the findings, advancements in the proposed algorithms could give machine learning a promising future.

Biometrics combined with cryptography can be employed to solve the conceptual and factual identity frauds in digital authentication. Biometric traits are proven to provide enhanced security for detecting crimes because of its interesting features such as accuracy, stability, and uniqueness. Although diverse techniques have been raised to address this objective, limitations such as higher computational time, minimal accuracy, and maximum recognition time remain. To overcome these challenges, an enhanced iris recognition approach has been proposed based on hyperelliptic curve cryptography (HECC). The proposed study uses the 2D Gabor filter approach for perfect feature extraction in iris preprocessing. A lightweight cryptographic scheme called HECC was employed to encrypt the iris template to avoid intentional attack by the intruders. The benchmark CASIA Iris V-4 and IITD iris datasets were used in the proposed approach for experimental analysis. The result analysis witnessed that the prime objective of the research such as lesser false acceptance rate, lesser false rejection rate, maximum accuracy of 99.74%, maximum true acceptance rate of 100%, and minimal recognition time of 3 seconds has been achieved. Also, it has been identified that the proposed study outperforms other existing well-known techniques.

Agriculture is a vital part of every country’s economy, and India is regarded an agro-based nation. One of the main purposes of agriculture is to yield healthy crops without any disease. Cotton is a significant crop in India in relation to income. India is the world’s largest producer of cotton. Cotton crops are affected when leaves fall off early or become afflicted with diseases. Farmers and planting experts, on the other hand, have faced numerous concerns and ongoing agricultural obstacles for millennia, including much cotton disease. Because severe cotton disease can result in no grain harvest, a rapid, efficient, less expensive and reliable approach for detecting cotton illnesses is widely wanted in the agricultural information area. Deep learning method is used to solve the issue because it will perform exceptionally well in image processing and classification problems. The network was built using a combination of the benefits of both the ResNet pre-trained on ImageNet and the Xception component, and this technique outperforms other state-of-the-art techniques. Every convolution layer with in dense block is tiny, so each convolution kernel is still in charge of learning the tiniest details. The deep convolution neural networks for the detection of plant leaf diseases contemplate utilising a pre-trained model acquired from usual enormous datasets, and then applying it to a specific task educated with their own data. The experimental results show that for ResNet-50, a training accuracy of 0.95 and validation accuracy of 0.98 is obtained whereas training loss of 0.33 and validation loss of 0.5.

Biometric security is a major emerging concern in the field of data security. In recent years, research initiatives in the field of biometrics have grown at an exponential rate. The multimodal biometric technique with enhanced accuracy and recognition rate for smart cities is still a challenging issue. This paper proposes an enhanced multimodal biometric technique for a smart city that is based on score-level fusion. Specifically, the proposed approach provides a solution to the existing challenges by providing a multimodal fusion technique with an optimized fuzzy genetic algorithm providing enhanced performance. Experiments with different biometric environments reveal significant improvements over existing strategies. The result analysis shows that the proposed approach provides better performance in terms of the false acceptance rate, false rejection rate, equal error rate, precision, recall, and accuracy. The proposed scheme provides a higher accuracy rate of 99.88% and a lower equal error rate of 0.18%. The vital part of this approach is the inclusion of a fuzzy strategy with soft computing techniques known as an optimized fuzzy genetic algorithm.

Random numbers are essential to generate secret keys, initialization vector, one-time pads, sequence number for packets in network and many other applications. Though there are many Pseudo Random Number Generators available they are not suitable for highly secure applications that require high quality randomness. This paper proposes a cryptographically secure pseudorandom number generator with its entropy source from sensor housed on mobile devices. The sensor data are processed in 3-step approach to generate random sequence which in turn fed to Advanced Encryption Standard algorithm as random key to generate cryptographically secure random numbers.

The need for security is a challenging task nowadays due to the transition from wired to wireless networks, emergence of Internet of Things (IoT), Mobile Networks, Wireless Sensor Networks (WSN) and Radio Frequency Identification System (RFID). Generally wireless systems are prone to insecurity and resource (power) constraint, to deal with these challenges many solutions has been proposed in cryptography. One such important development is light weight cryptography particularly signcryption.Signcryption is a logical combination of digital signature and encryption in a single step therefore the cost of communication and computation is very less compared to the existing signature then encryption scheme. Till date many signcryption techniques were raised based on El-Gamal, RSA and Elliptic Curve Cryptography (ECC). The proposed research work highlights the limitations of existing signcryption based on ECC and it proposes the efficient light weight cryptographic scheme of signcryption based on Hyper Elliptic Curve Cryptography (HECC) and Keccak hashing. Further the proposed research work achieves all the security metrics such as confidentiality, integrity, non- repudiation, forward secrecy and public verifiability.

Due to the massive use of wireless Internet of Things (IoT), the advent of multimedia-big-data (MBD) in recent decades poses numerous obstacles for successful contact with the virtual era. Mobile Adhoc Network-based IoT (MANETIoT) framework is increasingly common in this regard owing to its increased communication protocols and economic efficiency. MANET comprises arbitrary, battery-driven, roaming nodes that do not have an architecture that can handle the traffic and control the IoT network. In MANET-IoT, energy usage and traffic management for the handling of MBD information are significant issues. For rapid and precise response, it is essential to route or forward information like the locations of happenings and defected in a disaster. However, it is difficult to transfer this information to the Wireless Sensor Network in disaster areas because the current networks are a disaster that has been removed. In these situations, the transmission of opportunistic knowledge may play a vital role. Current opportunistic protocols need large messages for the restoration of the cluster that leads to more energy consumption and packet loss. To overcome these issues, this work proposes the reliable, energy-efficient opportunistic protocol known as Density Clustering Opportunistic Routing Protocol (DCOR). This method sends information opportunistically in emergencies and disasters through a density-clustering protocol. Results from simulations demonstrate that the designed protocol exceeds several well-known current routing mechanisms for network energy usage and dissemination of information.

Random numbers play a key role in diverse fields of cryptography, stochastic simulations, gaming, etc. Random numbers used in cryptography must satisfy additional properties of forward secrecy. Chaotic systems have been a potential source of random number generators. Both lower (One-dimension) and higher (two, three-dimension) chaotic systems are popularized in the generation of random bit sequences. Higher-order chaotic systems have a higher resistance to attacks owing to multiple dimensional outputs. Logistic map initially designed in one-dimension has been extended to two- dimensions to improve security. This paper proposes to use the concept of biological Gene Dominance to further improvise the randomness of 2D Logistic map. The sequences X and Y are considered to be parent genes that determine the value of parameter ‘r’ for the next iteration. The scatter plot of the proposed 2D Logistic Map with Gene Dominance (2DLMGD) shows almost uniform distribution of points in the region. The generated sequences are statistically tested using NIST SP 800-22 test suite and the results show that all sequences pass the tests. The random sequences are analysed for key sensitivity, information entropy, linear complexity, correlation to verify their conformity for use in cryptographic applications.

Cloud Computing is quickly developing and a lot more cloud suppliers are arising. This project says how the incoming tasks are scheduled in the cloud. In this paper, presenting a classifier algorithmic technique that integrates the K-Nearest Neighbor(KNN) algorithm with the Naive Bayes algorithm. Calculation offloading innovation offers a feasible arrangement by offloading some calculation serious assignments of the K-Nearest Neighbor algorithm and the Naive Bayes algorithm to edges or distant mists that are furnished with adequate assets. However, the offloading process might lead to excessive delays and thus seriously affect the user experience. To address this significant issue, we first respect the average response time of multi-task parallel scheduling as our streamlining objective. Finally, the K-Nearest Neighbor and Naive Bayes algorithm based applications are proposed to solve the problem. Offloading a task means how the task is scheduled in Virtual Machine, first, the task is separated into types(small, medium, large and extra-large) after that it sees which Virtual Machine is available to load the task. Suppose, if the incoming task is too-large then we have to offload it by separating the task into subtask as 6 operations (split, combine, merge, demerge, promote and demote). To avoid the burden in the Virtual Machine, We analyze the above operations by using the K-Nearest Neighbour (KNN) algorithm with the Naive Bayes algorithm and then scheduled in the Virtual Machine. We want to show that the K-Nearest Neighbour (KNN) algorithm with the Naive Bayes algorithm shows better performance than Deep Neural Network(DNN).

It is a web-based application designed commonly for use in Food & beverage based enterprises. These applications tackle the issues of Manual Labour cost in the food court bringing about a rapid expansion in the Overall business Model. It also provides the proprietor of the restaurant to administer and make modifications as per the stip-ulations in their restaurant. The consumer can order their food by accessing the application through their mobile browser in an optimized manner. The retailer can easily complete the order in time, as there will be a streamlined check-in for the availability of ingredients. In this paper, let's exemplify the overall description and generalized soft-ware requirements specification of the entire systematic structure designed for use in different food courts. Our appli-cation will impart the restaurant employees to manage their online cuisines and keep track of all the orders placed by their consumers. Furthermore, the Customer can make Payment using the Existing Payment Gateways as well as they can go for blockchain-based Payment Methodologies. Eventually, this idea is projected to reduce the overheads of Payment gateways and improvise Data security in a precise manner.

Biometrics combined with cryptography can be employed to solve the conceptual and factual identity frauds in digital authentication. Biometric traits are proven to provide enhanced security for detecting crimes because of its interesting features such as accuracy, stability and uniqueness. Although diverse techniques have been raised to address this objective, limitations such as higher computational time, minimal accuracy and maximum recognition time remain. To overcome these challenges an enhanced iris recognition approach has been proposed based on Hyper Elliptic Curve Cryptography (HECC). The proposed study uses 2D Gabor filter approach for perfect feature extraction in iris preprocessing. Light weight cryptographic scheme called HECC was employed to encrypt the iris template to avoid intentional attack by the intruders. The benchmark CASIA Iris V-4 and IITD Iris datasets were used in the proposed approach for experimental analysis. The result analysis witnessed that the prime objective of the research such as lesser false acceptance rate, lesser false rejection rate, maximum accuracy of 99.74%, maximum true acceptance rate of 100%, and minimal recognition time of 3 seconds has been achieved. Also it has been identified that the proposed study outperforms other existing well known techniques.

The major need of remote user authentication is to verify the authenticity of the user through insecure channel. Till today enormous remote user authentication schemes have been proposed but still some security flaws remains. Some of them are vulnerable to password guessing attack, Id guessing attack, client and server impersonation attack, replay attack, Denial of Service (DoS) attack etc. Besides the security issues many such schemes are supposed to have higher computational and communicational cost. To overcome these challenges a lightweight cryptographic scheme called signcryption has evolved. Signcryption is a logical combination of encryption and digital signature in single step. Thereby it provides necessary security features in less computational cost of 0.97 ms and communication cost of 824 bits. The proposed research work outlines the weakness in Dharminder et al's authentication scheme which is prone to biometric recognition error, offline password guessing attack, impersonation attack and replay attack. Furthermore the proposed study provides a secure multifactor authentication scheme using signcryption based on Hyper Elliptic Curve Cryptography (HECC) and Bio-hash function. The formal security analysis of proposed scheme is done using Burrows-Abadi-Needham logic. The analysis reveals that the proposed scheme is computational and communication efficient and satisfies all the needed security goals. The scheme is also been formally verified using AVISPA tool that confirms that it is resilient to security attacks.

The most challenging task in the design of wireless sensor networks (WSN) is increasing the lifetime of sensor nodes. The most crucial factor to increase the lifetime of wireless sensor network is to reduce the energy consumption. In this paper, a multi objective Bat algorithm is used to find the optimal cluster formation and routing model in wireless sensor networks. The proposed scheme optimizes the energy consumption in wireless sensor networks by selecting the best fittest node as cluster head and modeling of the communication distance by Bat’s loudness parameter. The proposed scheme outperforms the LEACH and other clustering protocols. It also extends the lifetime of sensor networks and achieves good reliability. The experimental results show that this scheme achieves considerable improvement in accuracy and convergence when compared to Particle swarm optimization.

Security is a key factor in today’s fast communicating world. Many cryptographic algorithms are tested and put into use efficiently. Random numbers are used in diverse forms like nonces, secret key, initialization vector, etc. They find place in encryption, digital signature, hashing algorithms. A deterministic algorithms takes an intial seed value as input and produces pseudo random numbers with falsely induced randomness. This research work extensively surveys large set of state-of-the-art PRNGs and categorizes them based on methodology used to produce them. We compared their statistical results obtained from various statistical test tools like NIST SP 800-22, TestU01. Finally, security analyses of various PRNGs were done quantitatively for their key space, key sensitivity, entropy, speed of bit generation, linear complexity. At last, we concluded the results with some future directions for researchers to carry out their research in improving the PRNGs.

Phishing is a network attack in which attacker generates a replica of current legitimate site to contrivance the users into submitting their personal and sensitive information. A good anti-phishing solution should have rapid access time and rapid detection rate, but most of the existing solutions fail to satisfy it. A new method is proposed to prevent phishing attack which evaluates the URL Characteristics and cross check with the white-list. The proposed method has higher access time and higher detection rate than other methods. Whenever the user opens a website for the first time which is not available in the white-list, the algorithm proposed here examines the webpage using URL characteristics. The experimental results depict the accuracy of proposed method having accuracy of 93.8% with less negative rate.

The emerging trend now in network security is lightweight cryptography which is the need of the hour due to the growth of wireless technology. As the internet technology grows day by day identity authentication in wireless network is more important. Generally, these wireless systems are in demand of security and resource (power). In order to meet these constraints an important lightweight scheme called Remote user authentication with signcryption is proposed with security features such as confidentiality, integrity (originality of sender and receiver), message and user authentication, non-repudiation, forward secrecy and public verifiability. Signcryption is a new paradigm that fulfils the functions of signature and encryption in one logical step and therefore the cost in terms of computation is reduced compared to the scheme signature-then-encryption. Till today many Remote user authentication schemes based on Elliptic curve, El-Gamal and RSA have been proposed. This paper points out the limitations of existing scheme such as lack in security aspects for example, password guessing attack, impersonation attack, forward secrecy, computation overhead, larger memory requirements. The proposed scheme based on signcryption using hyper elliptic curve that fulfils all the gaps of existing system.

Acquired facial defects caused by extirpation of neoplasms, congenital malformations or traumatic injury results in a huge functional, cosmetic and psychological handicap in those patients. These defects can be restored by facial prosthesis using different materials and retention methods to achieve a lifelike appearance and function. This clinical report describes a treatment schedule using silicone nasal prosthesis, which is mechanically retained for a patient who has undergone a partial rhinectomy due to basal cell carcinoma of the nose. The prosthesis was made to restore the esthetic appearance of patient with a mechanically retained design using a spectacle glass frame without any prosthetic adhesives so that the patient is more comfortable and confident to resume daily activities.