Reviewerly/exHarmony huggingface dataset:
Breast cancer :Early diagnosis by using artificial intelligence

Despite several epochal medical breakthroughs, the very mention of word ‘Cancer’ is enough to send shivers down the spine of anyone. It still remains a leading marauding menace for the whole mankind. Among the various forms of cancers plaguing humans, breast cancer is very common form of cancer resulting in huge number of fatalities especially among the women. Its detection in early stages remains a bright glimmer of hope which increases the chances of its cure exponentially. Use of technology involving artificial intelligence for disease detection is a very recent phenomenon but it has unleashed revolutionary changes in detection and cure of many fatal diseases including breast cancer. This study utilizes three classifiers i.e. Support vector, KNN and Naïve Bayes for detection of breast cancer with an appropriate features selection method that selects only the features with high impact and culls out the others not so significant factors. The Wisconsin Diagnosis Breast Cancer data set has been employed as a training and testing set for this study to make a comparative analysis of performance of the various ML algorithms on the pedestal of accuracy and precision. The empirical values so obtained and the results deduced therefrom prove that the Support Vector classifier approach is most successful technique for detection of breast cancer with an accuracy of 98.24 %.
An Advanced Full Stack Decentralized Approach for Secure Election Voting Process

Designing a safe electronic voting system offering secrecy with impartial approach while being transparent and flexible is a challenging task. Full Stack decentralized applications are smart contract decentralized application that runs on a Decentralized Blockhain Network. Problems constructing a vote casting software at the Central Web Server are: the votes at the database will be modified and will be counted extra or eliminated entirely, also the supply code at the net server can also be modified at any time. Rather than having a separate central server database we can deploy Blockchain. Blockchain is a network and database which handles all the transaction. Blocks on the network are added if the transaction is valid and the Blocks also ensure that the data distributed across the Blockchain network are valid. In this paper, we will elaborate the design and development of a Decentralized Approach for Election Voting System using Solidity language to make our smart contract. This Decentralized Approach will have the option for the voter to cast a vote in an election by their Ethereum account address which has public and private key address. This approach aims to provide a secure and trusted voting transaction.
Speech Watermarking Based on the Combination of Frequency QIM and Convolutional Coding

The frequent manipulation of speech information introduces an increasing copyright protection demand in the speech signal processing area. In this paper, we propose a blind speech watermarking method based on the combination of frequency Quantization Index Modulation (QIM0) and convolutional coding to enhance the robustness and watermarked speech quality. In the proposed approach, the input text message is transformed into a bitstream, then its convolutional encoded version is embedded into the frequency spectrum of the host speech signal according to the frame energy and the Human Auditory System (HAS). The detection process is performed on the frame that its energy is higher than a predefined threshold. The detected bitstream is then convolutionally decoded to obtain the watermarked message. The experimental results indicate that the proposed approach perform more robust against many signal processing attacks than the baseline methods. Furthermore, it can achieve more speech quality.
Clustering-Based Anonymization Technique using Agglomerative Hierarchical Clustering

Privacy protection has emerged as one of the most attractive research topics in recent years. Today; with the rapid growth of social networks (SNs), the rate of users’ data collection has increased. Users’ sensitive information of collected data should be protected from adversaries. One of the most commonly used privacy protection techniques is data anonymization. Data anonymization is achieved by changing or deleting some information. By applying all three commonly accepted constraints; K-anonymity, L-diversity, and T-closeness; we introduced a data anonymization strategy based on agglomerative hierarchical clustering to preserve anonymized data from identity disclosure, attribute disclosure, and similarity attacks. The simulation result over the dataset of two popular SNs illustrates the effectiveness of the proposed algorithm.
Recommendation System using Inference-based Graph Learning – Modeling and Analysis

Recently, the development of recommendation systems has been an emerging area where the target is the learner's preferences, and styles should be learned from past interactions to predict the ratings and recommendations of the new items. The significant critiques and challenges are some of the datasets are sparse and the bias in the ratings and popularity of the items that conventional recommenders can't efficiently process. This research constructs the graph model to merge the items and user interaction to enhance user representations. The new inference operator is designed to remove the bias factors and to draw inferences. The users and items are represented with high quality by constructing the semantic space that connects the users and the item's characteristics. The proposed model is simulated on real-world datasets, proving that the model produces significant outcomes better than existing methods.
Presenting a hybrid method based on dark/bright channel, fuzzy and luminance mapping estimation for color image enhancement

The lighting conditions at the environment are not favorable for imaging, and the problem of light is one of the challenges of imaging in the environment. The lack of sufficient light in the images reduces the quality and details of recognition by humans. In this paper, a hybrid method for image enhancement in low light is proposed. At first, dark/bright channel algorithm based on low-light images is used for minimum/maximum brightness images. In the next step, the fuzzy algorithm is used to weight different levels of brightness, and finally, the brightness mapping estimation method called LIME is used along with a denoising to improve the image. To evaluate the proposed method, the Universal Image Quality Index (UQI) indices were used, and the results obtained on the base images proved the performs of the method we proposed is better than the other novel methods.
Improved Task Scheduling Strategy Using Reinforcement Learning in Cloud Environment

Scheduling has recently been incredibly helpful in cloud computing due to the shift in resource consumption. Cloud provides many services (based on dynamism, elasticity, and uncertainty) to end-users based on their requirements which the users can access at any time, irrespective of the location, by paying for that service. The load on the cloud environment increases as the demand for applications in cloud services rises. The degradation in services (overuse of services) or the wastage of resources (underutilization) results from improper scheduling. The resources can be distributed appropriately to the different natured tasks through scheduling. The problem of resource imbalance can be avoided, and the optimization of the main execution factors, like availability, makespan time, utilization of resources, energy consumption, reliability, response time, etc., can also be optimized. Various algorithms like Heuristic, Meta-heuristic, and hybrid are proposed to justify the scheduling mentioned above. The proposed VTO-QABC is implemented and compared on the parameter throughput with different strategies. A significant improvement is found as compared to Max-Min(84.51%), MOPSO (37.82%), HABC_LJF (19.85%), Q-Learning (7.72%), VTO-QABC_FCFS(3.89%), VTOABC_LJF (3.89%) less time than MOCS.
Cloud Computing Using IoT

We can store data, manage it and process it on remote internet servers rather than on local computers or machines. Now coming to IoT, it can be any device equipment, or object which connects us with the cloud using the internet or with another device that is connected. It has sensors, processing ability, software, and many technologies which can be used to share and fetch data or information with other devices and servers over the internet [1]. Nowadays big companies are using cloud services for storing their data because it is easy to manage their data easily without any additional costs. Cloud computing has taken its place all over the IT industries. It is an on-demand internet-based computing service that provides the maximum result with minimum resources cloud computing provides a service that does not require any physical close to the computer hardware.Cloud Computing is a product of grid, distributed, parallel, and ubiquitous computing. This paper introduces the concepts, history pros, and cons of cloud computing [2].
LOCOMETER: an A.I. based system for capturing the location of underground pipeline

At the working site of tracker while laying pipelines there are various functions of tracker such as capturing the location of pipe using tracking machine and sending them to the administrator. The observation obtained by a tracker manually are often error prone. Many of the trackers slack off from work and takes more time in doing the work then it is required and hence ask for more wages. This problem can be solved by our application, the accuracy in the capturing readings can be increased as it will be automated, and the planner can monitor the work of the tracker so they can’t slack off. Therefore, to eliminate this problem, we use camera to scan the trackers screen and automatically capture the data from the screen using AI and send it to database. This paper provides all those services, which he needs while managing the site. The software provides a convenient way to the tracker and helps him to enter information correctly, without difficulty and save it to database. The main idea of this project is to design a system that will run automatically and fetch the details from the screen and send error free readings to the administrator. It will also help the tracker to check his working location and the administrator to plan the path of tracker and track his activities.
IWT based Reversible Data Hiding in Encrypted Domain using location map

The idea of reversible data hiding in encrypted images allows lossless data obscuring and data security. The exact retrieval of both the hidden message and the host image is crucial in this approach. In transform domain, there is some loss of information, not acceptable in most of the sensitive areas. Affine wavelet transformations that delineate integers to integers have significant role in such applications of reversible data hiding. This article presents a new reversible data hiding technique in the encrypted domain utilizing the integer wavelet transform (IWT) for grayscale images. The proposed work first encrypts the images using random permutation method and then perform embedding by utilizing IWT. For data embedding, encrypted images are decomposed into four sub-bands by applying IWT. Various experimentation has been done using standard grayscale images. Results and comparison with recent works show the efficacy of proposed work.
Detecting the drone by the combination of optical flow with various techniques

In image processing, detecting and tracking air targets (especially drones) is attainable. The present study focuses on drone detection using the combination of optical flow and histogram and presents a new approach to the recognition of flying targets (drones). Drones are mostly occupying a small portion of the visual field and are often moving in front of complex backgrounds such as buildings, crowded scenes, and local motion of the trees and leaves. In our work, video images obtained from YOUTUBE were used for considering these motions. Videos were transformed into N-frame by free video-tojpg converter software. Then, the drone boundary was determined with MATLAB software in the images containing the drone. Eventually, these pictures and drone-free pictures were applied as a database. In this work, four approaches were introduced: 1) combining the histogram of oriented gradient (HOG) and support vector machine (SVM), 2) combining the optical flow (OPF) with segmentation, histogram of oriented gradient (HOG), and support vector machine (SVM) without background elimination, 3) combining the optical flow (OPF) method, image segmentation, histogram of oriented gradient (HOG), support vector machine (SVM), and background elimination by K-means procedure, 4) combining the optical flow (OPF) method, image segmentation, histogram of oriented gradient (HOG), support vector machine (SVM), convolutional neural network (CNN), and background elimination by K-means procedure. Simulation outcomes demonstrated that the proposed procedures have acceptable performance in detecting one or several drones on the scene.
Developing an Efficient Secure Query Processing Algorithm for Unstructured Data on Encrypted Databases

A few years ago, information size increased unexpectedly and a data explosion happened. In this world of growing information, a change in database generation may also be required. Historically, we used a structured query language that works best with structured data. Now, we want to work with unstructured data as well as with structured data. The solution is to use not only SQL (NoSQL) database, this means not only structured query language. Recently, NoSQL databases are widely used in many organizations. Moreover, the data is kept in external services like Database as a Service (DaaS), where server-side and client-side security concerns are created. Additionally, the database’s query processing by several clients using complicated techniques and a shared resource environment may lead to ineffective data processing and retrieval. An effective data processing technique among several customers can be used to retrieve data in a secure and effective manner. In this paper, we present an Efficient Secure Query Processing Algorithm for Unstructured Data (ESQPA_U) for efficient query processing by applying data compression techniques before transferring the encrypted results from the server to clients. We have solved security concerns by using CryptDB to encrypt a database on the server to protect the data. Encryption methods have recently been suggested to give customers secrecy in cloud storage. The queries can be processed using encrypted data using this technique without having to first decrypt it. In order to evaluate ESQPA_U performance, it is contrasted with CryptDB existing query processing method. According to results, storage space is more effective and can save up to 57% of its original space.
Proposed HCDNA for Enhancing the Security of Marine Environment IoT Systems

Recently, the shipping industry and marine operations have been intensively reliable on the internet of things (IoT).IoT devices are used for the automation of shipping operations cargo tracking and security monitoring of the ships. In this paper, we focus on maintaining efficient security levels for marine communications. We propose new cryptographic methods based on the hyperchaotic sequence and the DNA sequence for image encryption. The proposed algorithm is applied to a smart buoy to enhance the security of the transmitted image through IoT systems in shipping industry monitoring. In addition, we evaluate and analyze the performance of three existing encryption algorithms for IoT (Blowfish, Advanced Encryption Standard (AES), and International Data Encryption Algorithm (IDEA)). Furthermore, a comparison was conducted between the three existing algorithms and the proposed one in terms of encryption time, correlation coefficient, and entropy before and after encryption. The simulation experiments show that the proposed cryptographic method outperforms the three existing algorithms in terms of quality, security, and robustness against noise as well as overall performance.
Cybersecurity Risks Mitigation in the Internet of Things

Internet of Things is a dynamic platform and involves data which in turn attracts cybersecurity threats. Numerous organisations are increasingly placing its focus towards understanding cyber risks in a dynamic and complex environment while also trying to quantify their exposure. This article focuses on addressing the assessment of cybersecurity threats in IoT and its vectors through a risk-based approach using machine learning algorithms while highlighting and discussing the traditional cyber risk assessment techniques at length. Built on the statistics analysis, it can be decided that the Bayesian algorithm performs in estimating the potential risks at 82% and in comparison, the CAQ model using J48 classifies at 94%. The Bayes algorithm also helps in quantifying the complex risks originating from different sources and helps in understanding how the risk factors emerge and connect in an IoT environment and what controls are required for mitigating them.
A Privacy-Preserving Model for Big Data

In the past few years, the tremendous expansion in the usage of computer programs has resulted in a large amount of data, called “Big Data.” This leads to new challenges for storage, processing, and privacy-preserving. Meanwhile, organizations tend to collect more data about customers for subsequent processing to extract knowledge, resulting in potential threats to their confidentiality. This raises significant questions regarding the security and privacy of data in NoSQL databases. In this paper, a privacy-preserving model for big data protection is proposed, which could be applied to various types of NoSQL databases. This model addresses the challenges of securing NoSQL databases including the lack of predefined schema, the existence of different NoSQL database categories, and the potential threats due to data processing. It also guarantees properties of conditional roles, purposes, tasks, policies, and possible insider threats. Verification and validation for the model using a use case are introduced to demonstrate how it ensures privacy-preserving in NoSQL databases.
Optimizing GoogleNet using New Connections and Auxiliary Layers Information

GoogLeNet is a convolutional neural network developed by researchers at Google for image processing. In this study, we have optimized the GoogLeNet model. We divided inception blocks into two types of blocks and established new connections to improve the model accuracy. We created new links between the SoftMax layers to reduce information loss. These changes help us improve recognition accuracy. We compared the updated model performance with state-of-the-art architectures GoogLeNet, ResNet, and AlexNet on the CIFAR-10 dataset and achieved the best classification accuracy.
Considering User Interests to Provid an Event Base News Stream Framework

With the wide spread use of social networks, news, as the most important product of online media, has a significant effect in creating news streams and guiding public opinion. By creating a news stream, news agencies sensitize people’s minds to a specific issue. One of the most important components of creating a news stream is generating and recommending the appropriate text. In this article, we use the fine-tuned GPT-2 model based on a deep neural network to generate text related to the target topic. We also recommend the generated text to users using recommender systems. In this research, the performance of the system was evaluated using the dataset collected from a Persian question-and-answer (Q& A) social network. The test results indicate that the relevance of the generated text to the topic by the fine-tuned GPT-2 model, has increased by 4% compared to the basic model.
Survey Paper: Study of Natural Language Processing and its Recent Applications

Natural Language Processing (NLP) is a field of research related to developing computer systems that can process native language intelligently. Considering natural language such as English has always been one of the key issues of the practical feasibility of research, both because of the key language that works with human ingenuity and because of the richness of potential applications. The main functions of natural language processing are machine translation, speech analysis, automatic summarization, conference resolution, speech recognition, etc. NLP is used to make the virtual computer interface easier for humans. This paper gives you an overview of NLP from scratch and discusses some of the key applications.
Fusion BASED AGV Robot Navigation Solution Comparative Analysis and Vrep Simulation

Today, Automated Guided Vehicle (AGV) robots are integral to many factories. One of the basic problems of these robots is accurate navigation, which, in addition to creating security in performing tasks, it helps the robot to manage the battery power and energy and move on a predetermined path. Over the various method, the visions are based on good performance, recently been widely used. In this research, a simple but effective fusion method as the combination of vision(camera) and infrared (IR) sensors with the minimum number of sensors is proposed and implemented. The proposed method has been simulated and evaluated using the Vrep simulator with real dimensions of our previous design AGV system named Hongma and the Python API. In the simulation, the proposed method was carried out. In the experiment, five paths named Circle, Elliptical, Spiral,8 shapes, and Special path, different paths with different complexity were tested, and the experiment aimed to find the maximum speed at which the proposed algorithm and the vision sensor (camera sensors) can track the path with a 100% success rate. The results obtained in the experiment show the fusion method’s effectiveness over the five mentioned scenarios in tracking the planned path compared to the routine vision method.
Implementing SOLID principles for IoT Arduino sensor code

Performance, energy efficiency, and other run-time attributes are usually prioritized by stakeholders in the embedded software industry as evaluation metrics, which often results in a trade-off of maintainability, reusability and other design-time qualities. But the need for shorter maintenance cycles is becoming more and more important, as more IoT devices and systems get adopted. Object-oriented programming (OOP) SOLID principles are well adopted practices in software development projects in achieving design-time qualities including maintainability and reusability. Despite the fact that maintainability and reusability are the design-time qualities that drive embedded software refactoring, the absence of a standard embedded software framework results in redundant and wasted development effort. In this study, we apply OOP SOLID principles to implement selected sensors to create an open-source library. This work can help beginners to quickly get started with sensors, and also help embedded software developers to build highly scalable and maintainable systems.