Information Technology and Applied Mathematics

Signature, encryption, and signcryption are three basic cryptographic primitives in the public key cryptography. In this paper, we discuss identity-based combined public key schemes in three cryptographic primitives environment, signature, encryption, and signcryption. The advantage of using combined public key scheme is to reduce the task of key management, where the same key pair is applied for signature, encryption, and signcryption. We give an identity-based combined signature and encryption (IBCSE) method based on Cha and Cheon’s signature and Boneh and Franklin’s encryption. In addition, we point out that the security notions for combined signature, encryption and signcryption defined by Paterson et al. in ASIACRYPT 2011 are too strong. We define relatively weak but more reasonable security notions for identity-based combined signature, encryption and signcryption (IBCSESC). We give a weakly secure IBCSESC scheme that satisfies our weak security notions and a strongly secure IBCSESC scheme that satisfies strong security notions.

To get inexpensive resolution of real-world problems such as weather forecasting, measurement of underground water label, traffic monitoring, activity of enemies, animals counting in forest, and so on, wireless sensor networks (WSNs) are widely used. Energy-efficient routing protocol is needed to provide the longevity of network lifetime by reducing power consumption of sensor nodes as well as whole networks. Besides, authenticity of sensor nodes and privacy of sensed data are needed in routing protocol for WSNs to provide secure communications, i.e., sensor-to-sensor as well as sensors-to-base station. Clustering technique provides an energy-efficient topology control approach. A minimum connected dominating set (MCDS) can be discovered by applying clustering technique which reduces power consumption in inter-cluster network routing. Cluster head and route selection can be used to provide an energy-efficient outer-cluster routing in WSNs. Identity-based scheme can be used to provide a secure secret message passing mechanism in WSNs. This paper has ng in wireless sensor network framework (E2SDRSNF) which consists of three proposed components. The proposed Algorithm 1 creates a MCDS which is used to build virtual backbone for energy-efficient inter-cluster routing. Data flows through the discovered virtual backbone nodes to base station via cluster heads which is discovered by applying proposed Algorithm 2. The proposed signcryption technique is used for secure communication in WSNs. The analysis of the proposed framework shows that it can save 2720 nJ/bit/m\(^2\) energy than LEACH protocol for one communication (transmission/receiving) along with the security.

The presence of non-text components in the document image hinders the result of an optical character recognition (OCR)-based document analysis system. Thus, text and non-text separation has become an essential task in the domain of document image processing. To address this issue, in the present work, a simple two-stage method is developed to separate the text and the non-text components from the images of handwritten scientific documents. Before starting the actual process, connected components from the document pages are extracted. Then, in the first stage, some commonly occurred components are identified and separated out as graphics. In the second stage, remaining components are passed through feature extraction and subsequent classification processes. Evaluating the system on handwritten scientific document images, it is found that 87.16% components are classified correctly as text or non-text.

Nowadays, to survive and promote the market competition, multi-item business strategy is more effective for any production/manufacturing sector. Many physical problems can be best model by using fractional differential equation (FDE). In this paper, we propose the approximate scheme to solve multi-term fractional order initial value problem. The proposed scheme is based on collocation method and shifted Chebyshev polynomials (SCP). The fractional derivatives are utilized in the Caputo sense. The fractional order initial value problem can be reduced to a system of algebraic equations by utilizing the properties of SCP, which is solved numerically. The collocation point is chosen in such a way as to attain stability and convergence. The main theme of the proposal is to centralize the upper bound of the derived formula and convergence analysis. The numerical examples are achieved good accuracy using proposed scheme even by using small number of shifted Chebyshev polynomials.

As of now, the performance of keystroke dynamics biometric in user recognition is not acceptable in practice due to intra-class variations, high failure to enroll rate (FER) or various troubles in data acquisition methods or diverse use of sensing devices. As per the previous study, the performance of this technique can be improved by incorporation of gender information, a soft biometric characteristic, extracted from the typing pattern on a computer keyboard that provides some additional information about the user. This soft biometric trait has low user discriminating power but can be used to enhance the performance of user recognition in accuracy and time efficiency. Furthermore, it has been observed that the age group (18–30/30+ or <18/18+), gender (male/female), handedness (left-handed/right-handed), hand(s) used (one hand/both hands), typing skill (touch/others), and emotional states (anger/excitation) can be extracted from the way of typing on a computer keyboard for single predefined text. In this paper, we are interested in identifying multiple soft biometric traits using two leading machine learning methods: support vector machine with radial basis function (SVM-RBF) and fuzzy-rough nearest neighbor with vaguely quantified rough set (FRNN-VQRS) on multiple publicly available authentic and recognized keystroke dynamics datasets collected through a computer keyboard as well as touchscreen phone. The performance of machine learning methods are changed significantly in changing dataset in keystroke dynamics domain, but the evaluation performance of FRNN-VQRS in our experiment is promising and consistent in identifying traits. At the end, the impacts of the incorporation of soft biometric traits with primary biometric characteristics in user recognition are presented and compared the evaluation performance of nine anomaly detectors.

Secret sharing has numerous applications in cryptography field and distributed computing. Threshold secret allotment scheme has been well considered a lot for the last three decades and proposed many such efficient schemes. The main objective of allotment sharing scheme is to deliver the secret to some parties so that only desirable subsets of the parties can get back the secrets whereas secret cannot be leaked by other parties. Nowadays, an ideal scheme shares multiple secrets with perfect security is of high demand. In this paper, we have designed a multi-secret sharing scheme on the basis of geometry in Galois field. The scheme is ideal, perfect without information leakage.

This paper studies the research problem of solving mathematical word problems (MWPs) and reviews the related research and methodologies. Word problems are any numerical problems written in natural languages like English, based on any subject domain (mathematics, physics, chemistry, biology, etc.), and MWPs relate to word problems in the mathematics domain. Solving MWPs has been a long-lasting open research problem in the field of natural language processing (NLP), machine learning (ML), and artificial intelligent (AI); however, unlike other research problems in NLP, ML, and AI, it has not made much progress. MWPs which can be easily solved by second-grade students can often pose serious challenges to MWP solvers due to its diverse problem types and varying degree of complexities. Understanding such problems written in natural language requires proper reasoning toward equation formation and answer generation. We restrict the review in this survey only to research on solving arithmetic word problems from elementary school level mathematics. We analyzed all the important methodologies proposed by researchers along with the datasets they used for training and evaluation. We studied the technical aspects of the system components and the algorithms relevant to their research along with the scopes, constraints, and limitations. This review paper also discusses the performance of different MWP solvers and provides observations on related datasets.

Hand biometrics is globally deployed for automated human identification based on the discriminative geometric characteristics of hand. Advancements in hand biometric technologies are accomplished over several decades. The key objectives of this paper are two-fold. Firstly, it presents a comprehensive study on the state-of-the-art methods based on the hand images collected in an unconstraint environment. Secondly, a pose-invariant hand geometry system is excogitated. The experiments are conducted with the weighted geometric features computed from the fingers. The feature weighted k-nearest neighbor (fwk-NN) classifier is applied on the right- and left-hand images of the 500 subjects of the Bosphorus database for performance evaluation. The classification accuracy of 97% has been achieved for both of the hands using the fwk-NN classifier. Equal error rates (EER) of 5.94% and 6.08% are achieved for the right- and left-hand 500 subjects, respectively.

Pre-bond testing of through silicon vias (TSVs) in 3D ICs is very challenging task. Reliability and correctness of 3D ICs consisting of numbers of TSVs have to be established. This paper presents an innovative pre-bond test method for TSVs in a 3D IC. The logic is devised around the cascadable arrangement of Cellular Automata (CA), discovered by von Neumann in the year of 1960. SACA, a designated class of CA, has been developed to diagnose conductor and insulator defects in TSVs. The segmentation of CA in the design assures more effectiveness, with respect to the number of computations to identify faulty TSVs. The implemented hardware realizes quick test of manufacturing TSV faults in the pre-bond phase.

This paper presents a novel method to represent hours of surveillance video in a pattern-based text log. We present a tag and template-based technique that automatically generates natural language descriptions of surveillance events. We combine the output of some of the existing object tracker, deep learning guided object and action classifiers, and graph-based scene knowledge to assign hierarchical tags and generate natural language description of surveillance events. Unlike some state-of-the-art image and short video descriptor methods, our approach can describe videos, specifically surveillance videos by combining frame-level, temporal-level, and behavior-level target tags/features. We evaluate our method against two baseline video descriptors, and our analysis suggests that supervised scene knowledge and template can improve video descriptions, specially in surveillance videos.

Today, intrusion detection system using the neural network is an interested and considerable area for the research community. The computational intelligence systems are defined on the basis of the following parameters: fault tolerance and adaptation; adaptable the requirements of make a better intrusion detection model. In this paper, provide an overview of the research progress using computational intelligence to the problem of intrusion detection. The goal of this paper summarized and compared research contributions of Intrusion detection system using computational intelligence and neural network, define existing research challenges and anticipated solution of machine learning.

Here we proposed an efficient dual image-based reversible steganographic technique using (7,4) Hamming code for image authentication and tampered detection. In this approach, the cover image is partitioned into \((1 \times 7)\) pixel blocks. The blocks are copied into two separate images, and three Least Significant Bits (LSB) are collected from each block separately. Then perform odd parity adjustment using Hamming code. Two symmetry keys \(\kappa \) and \(\xi \) are used for data embedding and stego block distribution among dual images, respectively. To embed secret bit complement data bit at \(\kappa \) position, then secret data bit is embedded through error creation at random positions within the block excluding the position of \(\kappa \) such that errors are corrected and cover image is recovered through (7,4) Hamming code. The proposed scheme has been verified through experimental results with existing scheme. The performance of this scheme is comparatively better in terms of visual quality. The experimental results showed that this new steganographic technique can be also applied for authentication and tampered detection.

In the present experiment, we have investigated the effectiveness of two handcrafted feature extraction techniques for the recognition of constituent strokes of online handwritten Bangla character samples. These techniques estimate local and global shape information from a stroke sample. Combined feature vector is fed to Multi-Layer Perceptron (MLP)-based classifier for stroke recognition purpose. We have achieved 91.27% recognition accuracy over test set. In the current experiment, total size of the stroke database is 32,534. Among the samples, 30% of the entire strokes are considered as test set and rest are used to train the recognition model. Afterward, we have implemented a Deterministic Finite Automata (DFA)-based character recognition system from the recognized strokes. Final outcome of the system is satisfactory considering the stroke variation orders while writing Bangla characters.

In this article, an integrated single-vendor single-buyer imperfect production–inventory model in which the vendor makes investment for process quality improvement and the buyer offers price discounts for backorders is studied. It is assumed that the buyer follows a continuous review policy with lot-size-dependent lead-time and a mixture of backorders and lost sales. Under the n-shipment policy, the expected annual total cost of the integrated system is derived. An algorithm is developed to determine numerically the optimal decisions of the model. A numerical example is taken to illustrate the developed model and to examine the sensitivity of the key parameters of the model. Some managerial insights are also provided.

Time series analysis and forecasting depend on the observed historical data. As it deals with numerous number of information, imprecision occurs due to many reasons like truncation error, mechanical fault, noise. In such cases, fuzzy concept works well to handle impreciseness. Fuzzy time series forecasting methodology consists of defining universe of discourse (UOD), fuzzification of time series data points, assigning relationships between consecutive data points and defuzzification to get back the forecasting results in real domain. In this paper, UOD of a time series has been defined, unequal partitions of the UOD are established and then relationship among consecutive data points is evaluated to get the forecasting models. Genetic algorithm has been used in partitioning the UOD unequally and establishing the relationship. Forecasting model is applied on enrollment of University of Alabama, BSE sensex time series, and Shenzhen stock exchange data. The results are compared with conventional fuzzy time series models.

Nowadays, to survive and promote the market competition, multi-item business strategy is more effective for any production/manufacturing sector. Here, an attempt has been made to develop a multi-item production inventory model, especially for seasonal products for a finite time horizon. Production process is not 100% reliable, and for this reason some imperfect quality items are produced, and these items are immediately remanufactured incurring some cost to get back its originality. Both demands and production rate of the items are time-sensitive. It is also assumed that production cost per unit varies with defective rates as well as production rates. Imposing two constraints, space and investment, profit maximization model is formulated. Incorporating inflation and time value of money into the model, total profit is represented as a definite integral with time period as its upper limit. The profit function becomes an optimal control problem. The defective rates of the production process are determined by variational calculus. As today’s competitive business transaction is full of uncertainty, another model is considered with fuzzy constraints and solved. Fuzzy constraints are converted to the crisp one, following fuzzy possibility. For the illustration of the developed models, numerical experiments and also some sensitivity analyses are performed and presented.