Key schedule algorithm based on coordinate geometry of a three-dimensional hybrid cube

Cryptographic algorithms play an important role in information security where it ensures the security of data across the network or storage. A key schedule algorithm is the mechanism that generates and schedules all session-keys for the encryption process. The 2-dimensional hybrid cube is generated based on permutation and combination of integer numbers that are utilized in the construction of encryption and decryption key in the non-binary block cipher. The generation of key space by using the 2-dimensional hybrid cubes are not sufficient to resist attacks and could easily be exploited. Therefore, the large key space is more desirable to resist any attack on the secret key. This research proposed a new Key Schedule Algorithm based on the coordinate geometry of a Hybrid Cube (KSAHC) for the non-binary block cipher. By using the three-dimensional hybrid cube in KSAHC transformation, encryption keys are represented as n × n × n matrix of integer numbers and used in the development of the permutation and substitution of order 4 square matrix. Triangular Coordinate Extraction (TCE) technique has also been introduced to extract the coordinates during the rotation of Hybrid Cube surface (HCs) and plays an important role in the development of KSAHC algorithm. The Hybrid Cube Encryption Algorithm (HiSea) has been implemented to validate the encryption keys that are generated from the proposed algorithm. The strength of the keys and ciphertext are compared with the Advanced Encryption Standard (AES), HiSea, and Dynamic Key Schedule Algorithm (DKSA). The proposed KSAHC algorithm has been validated using the randomness test proposed and recommended by NIST, the average result of avalanche test is 93%, entropy is 0.9968, correlation assessment test is -0.000601 and having large key space 2.70 × 1067 keys that makes the Brute Force attack difficult and time-consuming. Therefore, it can be concluded that the strength and validity of KSAHC algorithm have been enhanced as compared to other algorithms and can serve as the alternative algorithm in designing security systems

A comprehensive survey on pi-sigma neural network for time series prediction

Prediction of time series grabs received much attention because of its effect on the vast range of real life applications. This paper presents a survey of time series applications using Higher Order Neural Network (HONN) model. The basic motivation behind using HONN is the ability to expand the input space, to solve complex problems it becomes more efficient and perform high learning abilities of the time series forecasting. Pi-Sigma Neural Network (PSNN) includes indirectly the capabilities of higher order networks using product cells as the output units and less number of weights. The goal of this research is to present the reader awareness about PSNN for time series prediction, to highlight some benefits and challenges using PSNN. Possible fields of PSNN applications in comparison with existing methods are presented and future directions are also explored in advantage with the properties of error feedback and recurrent networks

A Comprehensive Survey on Pi-Sigma Neural Network for Time Series Prediction

Prediction of time series grabs received much attention because of its effect on the vast range of real life applications. This paper presents a survey of time series applications using Higher Order Neural Network (HONN) model. The basic motivation behind using HONN is the ability to expand the input space, to solve complex problems it becomes more efficient and perform high learning abilities of the time series forecasting. Pi-Sigma Neural Network (PSNN) includes indirectly the capabilities of higher order networks using product cells as the output units and less number of weights. The goal of this research is to present the reader awareness about PSNN for time series prediction, to highlight some benefits and challenges using PSNN. Possible fields of PSNN applications in comparison with existing methods are presented and future directions are also explored in advantage with the properties of error feedback and recurrent networks

Key generation technique based on triangular coordinate extraction for hybrid cubes

Cryptographic algorithms play an important role in information security where it ensures the security of data across the network or storage. The generation of Hybrid Cubes (HC) based on permutation and combination of integer numbers are utilized in the construction of encryption and decryption key in the non-binary block cipher. In this study, we extend the hybrid cube encryption algorithm (HiSea) and our earlier Triangular Coordinate Extraction (TCE) technique for HC by increasing the complexity in the mathematical approaches. We proposed a new key generation technique based on TCE for the security of data. In this regard, the Hybrid Cube surface (HCs) is divided into four quarters by the intersection of primary and secondary diagonal and each quarter is rotated by using the rotation points. The overall security of HC is improved by the rotation of HCs and enhanced the complexity in the design of key schedule algorithm. The brute force and entropy test are applied in experimental results which proved that the proposed technique is suitable for implementing a key generation technique and free from any predicted keys pattern

Comparative analysis of TF-IDF and loglikelihood method for keywords extraction of twitter data

Twitter has become the foremost standard of social media in today’s world. Over 335 million users are online monthly, and near about 80% are accessing it through their mobiles. Further, Twitter is now supporting 35+ which enhance its usage too much. It facilitates people having different languages. Near about 21% of the total users are from US and 79% of total users are outside of US. A tweet is restricted to a hundred and forty characters; hence it contains such information which is more concise and much valuable. Due to its usage, it is estimated that five hundred million tweets are sent per day by different categories of people including teacher, students, celebrities, officers, musician, etc. So, there is a huge amount of data that is increasing on a daily basis that need to be categorized. The important key feature is to find the keywords in the huge data that is helpful for identifying a twitter for classification. For this purpose, Term Frequency-Inverse Document Frequency (TF-IDF) and Loglikelihood methods are chosen for keywords extracted from the music field and perform a comparative analysis on both results. In the end, relevance is performed from 5 users so that finally we can take a decision to make assumption on the basis of experiments that which method is best. This analysis is much valuable because it gives a more accurate estimation which method’s results are more reliable

Key Generation Technique based on Triangular Coordinate Extraction for Hybrid Cubes

Cryptographic algorithms play an important role in information security where it ensures the security of data across the network or storage. The generation of Hybrid Cubes (HC) based on permutation and combination of integer numbers are utilized in the construction of encryption and decryption key in the non-binary block cipher. In this study, we extend the hybrid cube encryption algorithm (HiSea) and our earlier Triangular Coordinate Extraction (TCE) technique for HC by increasing the complexity in the mathematical approaches. We proposed a new key generation technique based on TCE for the security of data. In this regard, the Hybrid Cube surface (HCs) is divided into four quarters by the intersection of primary and secondary diagonal and each quarter is rotated by using the rotation points. The overall security of HC is improved by the rotation of HCs and enhanced the complexity in the design of key schedule algorithm. The brute force and entropy test are applied in experimental results which proved that the proposed technique is suitable for implementing a key generation technique and free from any predicted keys pattern

Effects of a high-dose 24-h infusion of tranexamic acid on death and thromboembolic events in patients with acute gastrointestinal bleeding (HALT-IT): an international randomised, double-blind, placebo-controlled trial

Background: Tranexamic acid reduces surgical bleeding and reduces death due to bleeding in patients with trauma. Meta-analyses of small trials show that tranexamic acid might decrease deaths from gastrointestinal bleeding. We aimed to assess the effects of tranexamic acid in patients with gastrointestinal bleeding. Methods: We did an international, multicentre, randomised, placebo-controlled trial in 164 hospitals in 15 countries. Patients were enrolled if the responsible clinician was uncertain whether to use tranexamic acid, were aged above the minimum age considered an adult in their country (either aged 16 years and older or aged 18 years and older), and had significant (defined as at risk of bleeding to death) upper or lower gastrointestinal bleeding. Patients were randomly assigned by selection of a numbered treatment pack from a box containing eight packs that were identical apart from the pack number. Patients received either a loading dose of 1 g tranexamic acid, which was added to 100 mL infusion bag of 0·9% sodium chloride and infused by slow intravenous injection over 10 min, followed by a maintenance dose of 3 g tranexamic acid added to 1 L of any isotonic intravenous solution and infused at 125 mg/h for 24 h, or placebo (sodium chloride 0·9%). Patients, caregivers, and those assessing outcomes were masked to allocation. The primary outcome was death due to bleeding within 5 days of randomisation; analysis excluded patients who received neither dose of the allocated treatment and those for whom outcome data on death were unavailable. This trial was registered with Current Controlled Trials, ISRCTN11225767, and ClinicalTrials.gov, NCT01658124. Findings: Between July 4, 2013, and June 21, 2019, we randomly allocated 12 009 patients to receive tranexamic acid (5994, 49·9%) or matching placebo (6015, 50·1%), of whom 11 952 (99·5%) received the first dose of the allocated treatment. Death due to bleeding within 5 days of randomisation occurred in 222 (4%) of 5956 patients in the tranexamic acid group and in 226 (4%) of 5981 patients in the placebo group (risk ratio [RR] 0·99, 95% CI 0·82–1·18). Arterial thromboembolic events (myocardial infarction or stroke) were similar in the tranexamic acid group and placebo group (42 [0·7%] of 5952 vs 46 [0·8%] of 5977; 0·92; 0·60 to 1·39). Venous thromboembolic events (deep vein thrombosis or pulmonary embolism) were higher in tranexamic acid group than in the placebo group (48 [0·8%] of 5952 vs 26 [0·4%] of 5977; RR 1·85; 95% CI 1·15 to 2·98). Interpretation: We found that tranexamic acid did not reduce death from gastrointestinal bleeding. On the basis of our results, tranexamic acid should not be used for the treatment of gastrointestinal bleeding outside the context of a randomised trial

Efficient processing of GRU based on word embedding for text classification

Text classification has become very serious problem for big organization to manage the large amount of online data and has been extensively applied in the tasks of Natural Language Processing (NLP). Text classification can support users to excellently manage and exploit meaningful information require to be classified into various categories for further use. In order to best classify texts, our research efforts to develop a deep learning approach which obtains superior performance in text classification than other RNNs approaches. However, the main problem in text classification is how to enhance the classification accuracy and the sparsity of the data semantics sensitivity to context often hinders the classification performance of texts. In order to overcome the weakness, in this paper we proposed unified structure to investigate the effects of word embedding and Gated Recurrent Unit (GRU) for text classification on two benchmark datasets included (Google snippets and TREC). GRU is a well-known type of recurrent neural network (RNN), which is ability of computing sequential data over its recurrent architecture. Experimentally, the semantically connected words are commonly near to each other in embedding spaces. First, words in posts are changed into vectors via word embedding technique. Then, the words sequential in sentences are fed to GRU to extract the contextual semantics between words. The experimental results showed that proposed GRU model can effectively learn the word usage in context of texts provided training data. The quantity and quality of training data significantly affected the performance. We evaluated the performance of proposed approach with traditional recurrent approaches, RNN, MV-RNN and LSTM, the proposed approach is obtained better results on two benchmark datasets in the term of accuracy and error rate

Neural Network Techniques for Time Series Prediction: A Review

It is important to predict a time series because many problems that are related to prediction such as health prediction problem, climate change prediction problem and weather prediction problem include a time component. To solve the time series prediction problem various techniques have been developed over many years to enhance the accuracy of forecasting. This paper presents a review of the prediction of physical time series applications using the neural network models. Neural Networks (NN) have appeared as an effective tool for forecasting of time series.  Moreover, to resolve the problems related to time series data, there is a need of network with single layer trainable weights that is Higher Order Neural Network (HONN) which can perform nonlinearity mapping of input-output. So, the developers are focusing on HONN that has been recently considered to develop the input representation spaces broadly. The HONN model has the ability of functional mapping which determined through some time series problems and it shows the more benefits as compared to conventional Artificial Neural Networks (ANN). The goal of this research is to present the reader awareness about HONN for physical time series prediction, to highlight some benefits and challenges using HONN

Intelligent Cyber-Security System for IoT-Aided Drones Using Voting Classifier

Developments in drones have opened new trends and opportunities in different fields, particularly in small drones. Drones provide interlocation services for navigation, and this interlink is provided by the Internet of Things (IoT). However, architectural issues make drone networks vulnerable to privacy and security threats. It is critical to provide a safe and secure network to acquire desired performance. Small drones are finding new paths for progress in the civil and defense industries, but also posing new challenges for security and privacy as well. The basic design of the small drone requires a modification in its data transformation and data privacy mechanisms, and it is not yet fulfilling domain requirements. This paper aims to investigate recent privacy and security trends that are affecting the Internet of Drones (IoD). This study also highlights the need for a safe and secure drone network that is free from interceptions and intrusions. The proposed framework mitigates the cyber security threats by employing intelligent machine learning models in the design of IoT-aided drones by making them secure and adaptable. Finally, the proposed model is evaluated on a benchmark dataset and shows robust results