Reviewerly/exHarmony huggingface dataset:
Secure Sharing of Documents using Image Encryption and Key Image

This Paper Presents Two Strategies For Encryption And Decryption Of Pictures Utilizing Xor Activity. The First Picture Is Encoded By The Key Picture Utilizing The Xor Activity And The Decoding Procedure Likewise Utilizes A Similar Key Picture With Xor Activity Is Done In The Primary Technique. In The Second Strategy One Of The Bit Planes, Rgb Technique, Filter Strategy For The Key Picture Is Utilized For Encoding The First Picture And Rearranging Is Accomplished For Getting The Scrambled Picture. This Technique Additionally Utilizes Xor Activity The Two Strategies Utilize A Picture Of A Similar Size As The Key For Scrambling The First Picture. Trials Have Demonstrated That The Two Calculations Are Appropriate For 2d Just As 3d Pictures. In The Matlab Condition, These Calculations Are Actualized And Tried On Different Pictures That Have Indicated Great Outcomes. For Scrambling Different Pictures These Strategies Are Likewise Utilized.
Artificial neural network based image encryption technique

Cryptography is used for the information transformation in safety manner. Encryption is the best method to convert the data one form to another. The cipher data cannot be understood by a third person. So to understand these various types of encryption schemes are available. In this present study, various encryptions, decryption techniques and they are proposed using chaotic neural network (CNN). CNN produces the same results in case of same input data. If the applied inputs are same, it is difficult to predict the system's behaviour, which will changes for any small change in input to that system. The objective is efficient use of artificial neural network with chaotic cryptography. With the help of chaotic sequence we found the weights of neural network. They are continuously updated on the basis of the generation of the key in the encryption algorithm. We used the Matlab tool/software to implement the encryption algorithm.
Prediksi Perubahan Penggunaan Lahan dan Pola Berdasarkan Citra Landsat Multi Waktu dengan Land Change Modeler (LCM)

Land use/cover greatly affect the quality of an area. Therefore, many regional planners need assistance byother fields, such as geoinformatics, computer science, environment, and others. Although prediction and forecasting have been widely studied, in regardto real conditions (geospatial)itstill needmoredevelopment, especially thoseinvolving a combination of regional types, such as urban and suburban areas. This study uses a remote sensing base and geographic information system in predicting land in the city and district of Bekasi, West Java, Indonesia. With two scenarios compared (business as usual and vegetation conservation), the model that has been created and validated (with an AUC accuracy result of 0.828) is used to predict land use change until 2030. Scenarios with vegetation conservation are able to keep green areas to switch to land types others, such as buildings and industry
Phishing classification models: issues and perspectives

The never-ending threats of phishing to the cyberspace motivate researchers to develop more proficient phishing classification models for the survival of cyber-security with safe web services. However, these models remain variable in their reaction and incompetent in their performance against novel phishes at the real-time of application. This attributed to their partial or full deficiency of inductive factors including a rich set of decisive features, actively learned and adaptive machine learning based classification model. Upon this issue, our paper revisits the current machine learning-based phishing classification models and analyses their performance qualitatively and quantitatively across three benchmarking data sets. Empirical results and observations emphasised the causality between the models' limitations and their lack of inductive factors. Accordingly, future outlooks are recommended as a navigating taxonomy to serve the researchers at developing their upcoming achievements in both academia and industry.
AUTHENTICATION OF DIGITAL MP4 VIDEO RECORDINGS USING FILE CONTAINERS AND METADATA PROPERTIES

The authentication of digital video recording plays a very important role in forensic science as well as for other crime investigation purposes. The field of forensic examination of digital video is continuously facing new challenges. At present the authentication of the video is carried out on the basis of pixel-based analysis. Due to the change in technology, it was felt that a new approach is required for the authentication of digital video recordings. In the present work a new approach i.e. analysis of media Information and structural analysis of video containers (boxes/ atoms) of mp4 file format have been applied for identification of original and edited videos. This work is limited only for Mp4 file format because the MP4 compressed format is widely used in most of the mobile phone for video recording and transmission purposes. For this purpose, we recorded more than 200 video samples using more than 20 different mobile phones of different make and models and more than 12 video editors, which are available in open source used for editing purpose. The original and edited MP4 video files were analyzed for their different metadata and structural contents analysis of different file containers (boxes/atoms) using different freeware tools. The details of the work are described below.
Reduction of TNFR2 Expression in Brain Endothelial Cells in Cerebral Malaria Mice Models after Hyperbaric Oxygen Exposure

Tumour Necrosis Factor Receptor 2/ TNFR2 is the main receptor of TNF-α expressed by brain endothelial cells and contributes to Nuclear Factor Kappa Beta/ NFκB activation which induces sequestration of leucocyte cells. The role of hyperbaric oxygen/HBO as a regulator of the inflammatory process can be used as a supportive therapeutic approach in cerebral malaria infection. The research aimed to examine the impact of HBO administration on expression of TNFR-2 in brain endothelial cells of mice infected with P. berghei ANKA. P.berghei ANKA / PbA infected C57BL/6 divided into 3 groups with 13 mice per group: G1 (negative control, normal mice); G2 (positive control; mice infected with PbA without HBO); G3 (treatment group; mice infected with PbA with HBO). Observation of TNFR2 expression using immunohistochemical techniques on the last day of treatment. Study results revealed that there has been a significant decrease in TNFR2 expression in brain vascular endothelial cells in the group that received HBO (p &lt;0.05). Exposure to HBO 2.4 ATA for 10 sessions can prevent cerebral malaria by decreasing TNFR2 expression in vascular endothelial cells of the brain of the mice model.
Introduction to Bootstrap

Bootstrap 4.5 is the latest version of the Bootstrap framework, formerly known as Twitter Bootstrap. It was built by Twitter for the mobile-first apps. It’s a complete and easy-to-use system of styles that helps deal with the daily tasks of a web developer. Compared with other CSS frameworks, the biggest advantage of Bootstrap is the huge selection of additional templates, themes, and boilerplate code. It makes it very easy and fast to create a sophisticated web site without a design agency or without having any professional design skills. There are also endless additions and extensions available to fill the rare gaps. Moreover, Bootstrap can handle desktop web sites and mobile device pages equally as well, so it’s really a one-stop solution.
Hindi word correction using micro-parsing

Hindi has become very popular on the internet in recent times. The Hindi literatures, like stories, poems, etc., are attracting people world-wide. Though lots of work has been done to computerise Hindi, still there are few areas no one has attempted so far, word analysis is one of them. To analyse any language, one is required to possess proper knowledge of symbols and grammar rules of the language. A word is one of the basic meaningful entities and hence correctness of it is of utmost importance in the parlance of natural language processing (NLP). For Hindi word analysis, micro-parsing algorithm has already been developed. This algorithm only detects an illegal Hindi word. In this paper we present an algorithm that is based on micro-parsing and is capable of correcting an illegal Hindi word along with suggestion for possible correct substitution(s). This will definitely encourage and help people write correct Hindi on the internet, and otherwise, too.
Android-Based Shortest Path Finding Using A-Star (A*) Algorithm in Bekasi City

Getting information on routes can be he main problem for visitors. For example in determining the route to a proper place for eating and how to find the closest route to a mall. Based on the existing problems, this study proposes an application for finding information about places that visitors want to go based on the closest route. Algorithm A-Star (A*) was implemented that uses the distance estimation by finding the closest path to the destination using a heuristic function as a basis to select from several alternatives effectively. The result showed that an android application can give the information about the location of places to visit for eating and malls by calculating the distance from the starting point to the end point.
Image Security using Digital Image Watermarking and Visual Cryptography Techniques

In today’s world, the enhancement in internet technologies, digital data are mostly used to share the information in public networks. There are many traditional security techniques used to provide security to the digital information. But the existing methods don’t provide much of the security to digital media like image, video, audio, etc. The digital watermarking is employed in the protection of digital information. This paper gives a review on digital image watermarking based on the visual cryptography to reach secure protection for the images. The secret information can be inserted in the original images. The secret key is generated from the watermark image with the help of visual cryptography to claim the ownership of images. Various types of Visual Cryptography and Digital Image Watermarking techniques are explained in real time application.
On the judgement of full-period sequences and a novel congruential map with double modulus on Z(p<sup>n</sup>)

This paper studies the judgement problem of full-period maps on Z(p <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> ) and proposes a novel congruential map with double modulus on Z(p <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> ). The full-period properties of the sequences generated by the novel map are studied completely. We prove some theorems including full-period judgement theorem on Z(p <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> ) and validate them by some numerical experiments. In the experiments, full-period sequences are generated by a full-period map on Z(p <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> ). By the binarization, full-period sequences are transformed into binary sequences. Then, we test the binary sequences with the NIST SP 800-22 software package and make the poker test. The passing rates of the statistical tests are high in NIST test and the sequences pass the poker test. So the randomness and statistic characteristics of the binary sequences are good. The analysis and experiments show that these full-period maps can be applied in the pseudo-random number generation (PRNG), cryptography, spread spectrum communications and so on.
ISVS3CE: Incremental Support Vector Semi-Supervised Subspace Clustering Ensemble and ENhanced Bat Algorithm (ENBA) for High Dimensional Data Clustering

In the recent work, Incremental Soft Subspace Based Semi-Supervised Ensemble Clustering (IS4EC) framework was proposed which helps in detecting clusters in the dataset. IS4EC framework also increases the results of clustering by reducing the intra-cluster distance and increasing the inter-cluster distance with increased cluster quality. It cannot attain acceptable results while handling high dimensional data. However, decreasing the dimensional subspace becomes extremely difficult issue. In IS4EC framework, to choose the optimal ensemble members also extremely becomes challenging issue. In order to solve these issues of traditional cluster ensemble methods, first propose an Incremental Support vector Semi-Supervised Subspace Clustering Ensemble (ISVS3CE) framework which makes utilized of benefits of the random subspace algorithm and the Constraint Propagation (CP) algorithm. Here the centroid values were selected by using the Support Vector Machine (SVM) classifier. In the ISVS3CE framework, Incremental Ensemble Member Chosen (IEMC) process is performed by using the ENhanced Bat Algorithm (ENBA), and the normalized cut algorithm is introduced to perform high dimensional data clustering. The ISVS3CE framework is successful for solving high dimensional data issue, at the same time as the CP algorithm is valuable for incorporating the prior information. Results demonstrate that the proposed ISVS3CE framework performs well on datasets by means of very high dimensionality, and better than the traditional clustering ensemble methods.
New wave of ransomware hits datacentre and healthcare targets

The latest victims of ransomware attacks include one of the largest datacentre operators in the US and a number of organisations in the healthcare sector.
Breast cancer identification based on artificial intelligent system

Worldwide, breast cancer causes a high mortality rate. Early diagnosis is important for treatment, but high density breast tissues are difficult to analyze. Computer-assisted identification systems were introduced to classify is fine needle aspirates (fna) , with features that better represent the images to be classified as a major challenge. This work is fully automated, and it does not require any manual intervention from user. In this analysis, various texture definitions for the portrayal of breast tissue density on mammograms are examined within addition to contrasting them with other techniques. We have created an algorithm that can be divided into three classes: fatty, fatty-glandular and dense-glandular, The suggested system works in a spatial-related domain and it results extremely immunity to noise and background area, with a high rate of precision.
High dimensional sentiment classification of product reviews using evolutionary computation

Feature selection is an important process in text classification. In general, traditional feature selection approaches are based on exhaustive search hence become inefficient due to a large search space. Further, this task becomes more challenging as the number of features increases. Recently, evolutionary computation (EC)-based search techniques have received a lot of attention in solving feature selection problem in high-dimensional feature space. This paper proposes a particle swarm optimisation (PSO)-based feature selection approach which is capable of generating the desired number of high-quality features from a large feature space. The proposed algorithm is tested on a large dataset and compared with several existing state-of-the-art algorithms used for feature selection. The accuracy of the underlying classifier has been considered as a measure of performance. Our obtained results demonstrated that the proposed PSO-based feature selection approach outperforms the other traditional feature selection algorithms in all the considered classifiers.
Stationary (Wide Sense) Random Sequences: L 2-Theory

The [spectral] decomposition provides grounds for considering any stationary stochastic process in the wide sense as a superposition of a set of non-correlated harmonic oscillations with random amplitudes and phases.
Non-Invasive Prediction Model to Detect Sepsis using Supervised Machine Learning Algorithms

Sepsis is a life-threatening disease that causes tissue damage, organ failure and results in the death of millions of people. Sepsis is one of the highest risky diseases identified globally. A large proportion of these deaths occur in developing countries due to inaccessibility of hospitals or lack of resources. Blood samples are taken to confirm sepsis, but it requires the presence of laboratory and is time-consuming. The aim and objective of this study is to develop a practical, non-invasive sepsis prediction model that can be used to detect sepsis using supervised machine Learning algorithms. For this retrospective analysis, we used the data available from Physio-Net database.
Extracting Non-Functional Requirements Based On Agile Software Development(ASD)

In present senario Agile methodology perform very popular role in software design &amp; development with the help of customer requirement. In development process we generally consider functional requirement more than non-functional requirement but non-functional requirement also contain equal weightage in Agile Software Development. This paper incorporates to find non functional requirements based on priority algorithm. With the help of this algorithm author tries to find comparisons different non functional requirement
The Impact of Artificial Intelligence on Customer Experience and the Purchasing Process

This chapter conceptualizes customer experience and the consumer buying journey through the influence of artificial intelligence (AI) on varied sectors. It briefly reviews the importance of customer experience, the consumer buying journey, and the purchase process phases. Also, the concepts and definitions of elements related to customer experience are identified and explained. The dimensions of customer experience and their sub-dimensions are discussed in this review. Coming to AI, its prominence and applications in various fields like retail sectors, public sector, and B2B e-commerce are also explained. Apart from that, the challenges of AI are also notified to gain a deeper insight on the impacts of AI on human life. Finally, the analysis on both customer experience and consumer buying is explained by two models separately. In that one model explains the influence of AI application chatbots on consumer buying journey is well explained in this chapter.
A Low-Cost Methodology for EM Fault Emulation on FPGA

In embedded systems, the presence of a security layer is now a well-established requirement. In order to guarantee the suitable level of performance and resistance against attacks, dedicated hardware implementations are often proposed to accelerate cryptographic computations in a controllable environment. On the other hand, these same implementations may be vulnerable to physical attacks, such as side channel analysis or fault injections. In this scenario, the designer must hence be able to assess the robustness of the implementation (and of the adopted countermeasures) as soon as possible in the design flow against several different threats. In this paper, we propose a methodology to characterize the robustness of a generic hardware design described at RTL against EM fault injections. Thanks to our framework, we are able to emulate the EM faults on FPGA platforms, without the need of expensive equipment or lengthy experimental campaigns. We present a tool supporting our methodology and the first validations tests done on several AES designs confirming the feasibility of the proposed approach.
Modified Advanced Encryption Standard for Boost Image Encryption

Cryptography is a field of study that deals with converting data from a readable to an unreadable format. It can provide secrecy, data integrity, authenticity, and non-repudiation services. Security has become a concern for the community because of the technology’s potential use in numerous sectors of any company, market, agency, or governmental body, information. The cryptosystems ensure that data are transported securely and only authorized individuals have access to it. Deeply encrypted data that cannot be deciphered through cryptanalysis are in high demand right now. There are a variety of encryption algorithms that can guarantee the confidentiality of data. For multimedia data, standard symmetric encryption algorithms (AES) can give superior protection. However, using the symmetric key encryption approach on more complicated multimedia data (mainly photos) may result in a computational issue. To address this issue, the AES has been modified to satisfy the high computing requirements due to the complex mathematical operations in MixColumns transformation, which slow down the encryption process. The modified AES uses bit permutation to replace the MixColumns transformation in AES because it is simple to construct and does not require any complex mathematical computation. This research focuses on using the Modified Advanced Encryption Standard (MAES) algorithm with 128 and 256 bit key sizes to encrypt and decrypt image data. The algorithms were implemented using the Python programming language without complex mathematical computation. By comparing the MAES algorithm with the original AES algorithm, the results showed that the MAES requires less encrypting and decryption time with higher efficiency for all file sizes.
An Approach of Sentiment Analysis for Movie Reviews

In this work we present an approach for sentiment analysis which uses movie review for determining the polarity of the movie. We extract movie review from popular site which we use as our data set. To determine this We present a unique machine learning strategy for identifying phrases in movie reviews. and to determine the polarity for each of the sentence. The approaches are being used Bag-of-words model, defining adjectives and adverbs, managing negations, bounding word frequencies, and employing Word Net synonyms knowledge are all examples of text aspects that may be extracted. We examined how our classifier works for obtaining best polarity for Tamil movie reviews.
A Privacy Preserving Blockchain Based Framework for AIoT Data Exchange

Thanks to the advances in wireless communication technologies and the popularity of mobile devices, individuals can now record and produce personal videos. Furthermore, with the power of machine learning techniques, one can envision that a new paradigm for AIoT, a combination of AI and IoT, can accomplish many novel IoT applications. A feasible, safe, and trusty data trading environment is necessary to realize this AIoT service paradigm. The classic approach of centralized trust centers is not suitable for the targeted scenario due to privacy, vulnerability, and efficiency concerns. The emerging blockchain technology could be the perfect solution; yet, it remains a challenging issue to ensure data provider's privacy. Thus, this paper envisions a blockchain- based AIoT service paradigm and proposes solutions for preserving privacy. To preserve privacy in data storage, we adopt the approach of a private InterPlanetary File System (IPFS) network. For preserving privacy in on-chain metadata, especially the GPS records of the personal videos, we propose a privacy-preserving geometric search scheme (PPGSS). Through extensive experiments using real- world datasets and settings, we demonstrate the effectiveness of the proposed scheme.