Cryptic Mining in Light of Artificial Intelligence

Single columnar transposition is an encryption technique used in text encryption to produce cipher text which is a permutation of its original text. In this study, cryptanalysis of Single Columnar Transposition is performed and the novelty in this research is the cipher text given as only input, is deciphered without knowing the encrypting key to reconstructing the plain text before encryption. The key length currently unknown, can be determined as a factor of the cipher text length, thereby reducing the number of computations for decryption. The finite set of key lengths determined will be used to construct the matrix for decryption. The novelty in this research is the use of the longest prefix match which matches the longest word and checks the word if it exists in the English dictionary. If all the words that were constructed from the decryption by going through all the permutations of the key length determined by the matrix dimension earlier, we would get the plain text. This research performs a time analysis of the algorithm, including the worst-case and best-case scenarios for a particular ciphertext across key lengths. The keyless decryption time vs the key length, the graph is exponential in the experiments conducted with limited computing power. With better computing systems, the algorithm can be improved and restructured to give a linear time graph. The study can also be extended to include other languages, variable text sentences, and paragraphs in a document.

Cloud computing technologies comprise various kinds of significant desirable constrains such as security, liability, government surveillance, telecommunications capacity, anonymity and privacy. The usage of cipher text technology is considered as a desired technique for performing the process of encryption in order to solve the issue of granting security to the data that are shared in the cloud. Similarly, the architecture of multi-tenant in the cloud computing system grants benefits to both the service providers and end-users which shares a common cloud platform to multiple tenants (i.e.) users and suitable resources are also computed by implementing proposed architecture. Therefore in this research work, the concept of cipher text multi-tenant are integrated for providing enhanced security to the data shared in the cloud environment. Hence a Modified Elliptic Curve Cryptography (MECC) based on Diffee Hellman algorithm is proposed in this research paper which provides enhanced security using alternate key generation. The encryption, decryption, upload and download time are calculated and it is concluded that the algorithm that is proposed in this research paper consumes less time for all these measures when comparing with other existing algorithms. Characteristics like less memory, high operational performance, small sized keys and rapid key generation process, and effective resource savings enable the modified ECC to obtain high efficiency. The encryption time of the proposed MECC system was 51ms with the key length 4096 bits, whereas existing method had 92ms as encryption time. Likewise With the key length of 4096 bits, the decryption time for the proposed model was 159ms. Accordingly the proposed system has reduced cipher text key size of 836KB when compared with the existing AES, Blow Fish, and Two Fish. Additionally, the key generation time (35 s) also seems to be considerably reduced when compared with the existing methods. These statements reveal that the proposed system outshine the state of art methods in terms of key generation time, encryption and decryption time and computational complexity.

BackgroundRecent developments in the domain of biomedical knowledge bases (KBs) open up new ways to exploit biomedical knowledge that is available in the form of KBs. Significant work has been done in the direction of biomedical KB creation and KB completion, specifically, those having gene-disease associations and other related entities. However, the use of such biomedical KBs in combination with patients’ temporal clinical data still largely remains unexplored, but has the potential to immensely benefit medical diagnostic decision support systems.ResultsWe propose two new algorithms, LOADDx and SCADDx, to combine a patient’s gene expression data with gene-disease association and other related information available in the form of a KB, to assist personalized disease diagnosis. We have tested both of the algorithms on two KBs and on four real-world gene expression datasets of respiratory viral infection caused by Influenza-like viruses of 19 subtypes. We also compare the performance of proposed algorithms with that of five existing state-of-the-art machine learning algorithms (k-NN, Random Forest, XGBoost, Linear SVM, and SVM with RBF Kernel) using two validation approaches: LOOCV and a single internal validation set. Both SCADDx and LOADDx outperform the existing algorithms when evaluated with both validation approaches. SCADDx is able to detect infections with up to 100% accuracy in the cases of Datasets 2 and 3. Overall, SCADDx and LOADDx are able to detect an infection within 72 h of infection with 91.38% and 92.66% average accuracy respectively considering all four datasets, whereas XGBoost, which performed best among the existing machine learning algorithms, can detect the infection with only 86.43% accuracy on an average.ConclusionsWe demonstrate how our novel idea of using the most and least differentially expressed genes in combination with a KB can enable identification of the diseases that a patient is most likely to have at a particular time, from a KB with thousands of diseases. Moreover, the proposed algorithms can provide a short ranked list of the most likely diseases for each patient along with their most affected genes, and other entities linked with them in the KB, which can support health care professionals in their decision-making.

We propose a new and efficient scheme for broadcast encryption. A broadcast encryption system allows a broadcaster to send an encrypted message to a dynamically chosen subset RS, |RS|=n, of a given set of users, such that only users in this subset can decrypt the message. An important component of broadcast encryption schemes is revocation of users by the broadcaster, thereby updating the subset RS. Revocation may be either temporary, for a specific cipher text, or permanent. In the existing public key schemes which support temporary revocation of the users, the broadcaster is required to keep track of the revoked users. We present the first public key broadcast encryption scheme that supports permanent revocation of users. Unlike previous schemes, the broadcaster in our scheme should not keep track of the revoked users (saving memory and computation power). Our scheme is fully collusion-resistant. In other words, even if all the revoked users collude, the revoked user cannot encrypt messages without receiving new keys from the broadcaster. The procedure is based on Cipher-text Policy Attribute-Based Encryption (CP-ABE). The overhead of revocation in our system is constant in all major performance measures including length of private and public keys, computational complexity, user's storage space, and computational complexity of encryption and decryption. The scheme we construct improves on our original scheme in a poster presentation [7] by a factor of O(log n) in all major performance measures.

How to Improve Text Summarization and Classification by Mutual Cooperation on an Integrated Framework

Discovering the root key bits in the cryptanalysis of 16-rounded Data Encryption Standard (DES-160) is considered to be a hard problem. In this paper we present an approach for cryptanalysis of DES-16 based on Particle Swarm Optimization (PSO) using Known-plaintext attack and some equations that deduced from the relationship between sub-key differences and root key information. In Known-plaintext attack the cryptanalyst possesses one or more plaintext/cipher text pairs formed with the secret key and attempts to deduce the root key that used to produce this cipher text. In our approach, PSO is used as optimization technique to collect the optimal effective plaintexts from a plaintext search space according to the proposed fitness function then the set of collected plaintexts and the corresponding cipher texts used to extract the best eight sub-key differences from which most bits of the root key are discovered.

Phrase2Vec: Phrase embedding based on parsing

The paper presents an online system for clustering and classification of texts in the Polish language. It allows running complex workflows of language and machine learning tools. A high throughput and low latency was achieved by an asynchronous style of programming and a usage of message oriented middleware—RabbitMQ. Authors discuss the architecture assumptions, the language processing modelling notation for a workflow definition and the system architecture. Moreover, a sample Single Page Application is presented that clusters uploaded corpora and shows results online.

Manifold learning algorithms have been demonstrated to be effective for hyperspectral data dimension reduction (DR). However, the low dimensional feature representation resulted by traditional manifold learning algorithms could not preserve the nonnegative property of the hyperspectral data, which leads inconsistency with the psychological intuition of “combining parts to form a whole”. In this paper, we introduce a nonnegative discriminative manifold learning (NDML) algorithm for hyperspectral data DR, which yields a discriminative and low dimensional feature representation, with psychological and physical evidence in the human brain. Our method benefits from both the nonnegative matrix factorization (NMF) algorithm and the discriminative manifold learning (DML) algorithm. We apply the NDML algorithm to hyperspectral remote sensing image classification on HYDICE dataset. Experimental results confirm the efficiency of the proposed NDML algorithm, compared with some existing manifold learning based DR methods.Keywordsmanifold learningnonnegative matrix factorizationdimension reductionhypsrspectral data

Text classification and information mining are two significant objectives of natural language processing. Whereas handcrafting rules for both tasks has an extensive convention, learning strategies increased much attention in the past. Existing work presented concept based mining model for text, sentence mining and does not support text classification. To enhance the text clustering approach, we first presented Conceptual Rule Mining On Text clusters to evaluate the more related and influential sentences contributing the document topic. But this model might discriminate terms with semantic variation and negligible authority on the sentence meaning. In addition, we plan to extend conceptual text clustering to web documents, by assigning sentence weights based on conditional probability. Probability ratio is identified for the sentence similarity from which unique sentence meaning contributing to the document topic are listed. In this work, our plan is to implement ranking of the sentences which are calculated using the weights assigned to each and every sentences. With sentence rank conceptual rules are defined for the text cluster documents. The conceptual rule depicts finer tuned document topic and sentence meaning utilized to evaluate the global document contribution. Experiments are conducted with the web documents extracted from the research repositories to evaluate the efficiency of the proposed efficient conceptual rule mining on web document clusters using sentence ranking conditional probability [CRMSRCP] and compared with an existing Model for Concept Based Clustering and Classification and our previous works in terms of Sentence Term Relation, Cluster Object weights, and cluster quality.

Text document clustering is a task that seeks to cluster together related documents based on content similarity. Clustering helps to improve information diversity retrieval, topic spotting, and sentiment analysis by ordering unstructured text and dividing it into information structures. To address the issue of text-based work and its optimization through more efficient and accurate text classification and clustering, this paper offers a more advanced text classification and clustering framework that incorporates the Adaptive Quantum Bacterial Colony Optimization (AQBCO) algorithm and deep word embedding models, such as Word2Vec, TF-IDF, and FastText. AQBCO presents two innovations: adaptive chemotaxis, which produces the movement of bacteria dynamically controlled by the optimal solution, and quantum-inspired position updating, which increases the ability to search the globe and eliminates premature convergence. The framework was tested using benchmark datasets- BBC, SMS, and DMOZ, and was compared with the available optimization-based learning methods, including Improved BCO (IBCO), standard BCO, GQPSO, IPSO, PSO, GA, and k-means. According to the experimental findings, AQBCO always performs better on Accuracy, Precision, Recall, and F-Measure as compared to other models. The highest accuracy of 97.38% on BBC, 96.79% on SMS, and 93.68% on DMOZ datasets allowed AQBCO to use FastText embeddings. The suggested adaptive and quantum systems preserve exploration-exploitation balance, enhancing the speed of convergence and resiliency. These findings make AQBCO a scalable, stable, and efficient method of text classification as well as document clustering.

The data mining is a task of analyzing the data and their hidden patterns. Therefore the classification and clustering approaches are used for performing this task. The classification of the data needs to be performing training over the similar patterns and using the training knowledge the upcoming patterns are recognized. In the data mining the text analysis is one of the popular aspects of the data mining. In this presented work the text analysis and their classification techniques are studied. In addition of that a new text classification approach is introduced for analysing the social media text. The proposed technique helps for automatically analysis of the text patterns and obtaining their positive and negative orientation according to the learned patterns. The proposed technique utilizes the word probability, sentence formation probability and the weighted indexing of the data for recognizing the orientation of the text data. The proposed technique organized in such manner by which the different combinations of the words can be analysed using the graphical approach. Therefore the computed probability and weights are used for generating the directed weighted graphs. Using this graph the upcoming technique is classified and their orientation is obtained. The implementation of the proposed text classification data model is provided using JAVA technology and their performance is also evaluated. The performance of the system is reported in terms accuracy, error rate, memory usage and the time complexity. According to the different experimental analysis the proposed technique is found accurate and efficient for text classification. Additionally that can also be extendable for finding the classes more than two classes.

Multinomial mixture model with feature selection for text clustering

Semantic text classification: A survey of past and recent advances

In view of the advantages of deep learning model in the extraction of abstract concept, a new text clustering algorithm is designed based on Deep Boltzmann Machines. Based on Replicate Softmax Model and new Deep Boltzmann Machine, energy function of this model is proposed and the detail learning algorithm is introduced.