Reviewerly/exHarmony huggingface dataset:
Absolute Data Security Scheme: An Advanced Approach towards Data Security

With the exponential increase in the applications of digitalization, the risk of data breach and theft is also rising tremendously. The abrupt increase in the use of digital money, especially after demonetization and then during Corona pandemic, has developed dependencies on the online transactions via different digital payments and financial service providers. In this it has become challenging to keep the information including credentials, One Time Password etc. secure. In the course of time, number of blooming techniques are introduced which secure the information whether it is in the network or stored at secured architecture. But the soul of these techniques is the complex algorithms that keep the data confidential from the attackers/ unintended users. In this paper we are proposing a robust scheme named as Absolute Data Security Scheme which can be useful for securing applications commonly used in modern environment. A customized version of International Data Encryption Algorithm (IDEA) algorithm is used in combination with message digest algorithm to provide better security. It is a combination which preserves the confidentiality as well as integrity of the data. This scheme is a combination of encryption; decryption and message digest techniques which contribute in the secure transmission of the data.
A Practical Approach to detect Indoor and Outdoor Scene Recognition

In computer vision recognition of scenes is a long-time research problem. Scene can be defined as the real-time environment view which consists of a lot of views (like road, tree, building, parks, etc.) in a meaningful manner. The problem of scene recognition can be explained as assessment of labels such as “road”, “building”, “hall”, “bedroom” or in an extra simplified way “Indoor scene” and “Outdoor scenes is classified on an input image based on the object or environment of the image. A huge amount of data is created and available every second in this growing era of digital data. Scene recognition is still a rising area that did not attain much success as compared to image recognition due to the vast variability of features in the scenic environment. Because of this reason, there is not a lot of work being completed these days in this area. This project focuses on the evaluation of problem statements using all the literature surveys completed lately and offering solutions for that problem statement. For resolving the proposed problem declaration ResNet and VGG variants are used ResNet18, 50 and 152 & VGG16, 19, and VGG19 with batch normalization are implemented. The entire scene recognition procedure is discussed in this report and the main motive is to form a foundation that can be further continued in proposing a new algorithm. Before deep learning the design and implementation of the scene recognition model depended on the low dimensional portrayal of the scene. The utilization of deep learning especially CNN for scene recognition has gotten extraordinary attention from the computer vision community.
An Analytical Approach on Artificial Intelligence Based Models for Anomaly Detection in Sensor Networks

Wireless sensor networks, also known as WSNs, have quickly transformed into one of the main important areas of research, significantly influencing the development of new technologies. Many resource-constrained sensor nodes function independently to collaborate and maintain wireless networks. Through these networks, essential source information is gathered and transferred to the end users or decision-makers. WSNs have been implemented in different critical applications, such as remote patient health monitoring systems, home automation systems, sales tracking systems, enemy target monitoring and tracking systems, and fire detection system implementations, where the trustworthiness of WSNs becomes very important. These kinds of applications desire to have both comprehensive and accurate data. WSNs may be prone to anomalies as a result of low-cost hardware and software that is unstable, as well as an adverse operating environment that may disrupt the network's communication. It is necessary to identify these anomalies because they can bring about malfunctions in the network and, as a result, impair the quality of the data that has been gathered. In this research, we investigate the anomalies present in WSN, discuss the desirable attributes of anomaly detection approaches, and examine the different anomaly detection strategies that can be applied to wireless sensor networks.
Comparative Study of different Machine Learning Algorithms to Analyze Sentiments with a Case Study of Two Person's Microblogs on Twitter

In recent years, social media has become a powerful tool for individuals to express their opinions and attitudes. Twitter, a microblogging platform, has emerged as a prominent source of such information. Sentiment analysis, the process of extracting attitudes and opinions from text data, has become increasingly popular in the field of machine learning. This study aims to perform a comparative analysis of different machine learning algorithms for sentiment analysis, with a case study of two individuals' microblogs on Twitter. A publicly available labeled dataset from Kaggle was used for the study. The pre-processing of the tweets was performed to make them manageable for common language management strategies. The study compared the performance of Naive Bayes, logistic regression, and support vector machine algorithms for sentiment analysis on the tweets. The results showed that all three algorithms performed well in classifying the sentiments, with support vector machines providing the highest accuracy. This study highlights the potential of machine learning algorithms in analyzing sentiments on Twitter and can serve as a reference for future research in this field.
Design of Artificial Intelligence Enabled Game Engine

Game engines are primarily designed for the development of video games but can also be used as rendering engines for 2D and 3D graphics. They include all relevant libraries and supported programs. Developers can use them to construct games for game consoles and other types of computer softwares. They also aid in the porting of games to multiple platforms. Developers look for platforms which are easily available and provide facilities to create impressive, interesting and animated games. But these software are difficult to run on everyday machines, especially laptops, limiting their use to professionals with dedicated systems. This paper discusses design of new game engine with all the standard features of a rendering engine. The artificial intelligence (AI) aspect of the engine requires rendering of visual data at a lower resolution to improve performance and then using trained Deep Networks to upscale the image to the native resolution of the user's monitor. The AI will ensure that upscaling will be adding detail rather than averaging the nearby pixels.
Towards Reliable Medical Transactions for Blockchain based Healthcare Systems using Hybrid Consensus Method

Blockchain technology opens the door to securing and protecting massive amounts of IoT data storage, improving decentralized storage applications, getting rid of centralized trust servers, and facilitating data accountability and traceability. A hybrid blockchain can be used to build a network where sensors communicate with a device that performs smart contracts and records all events on the blockchain. Actual clinical observation and treatment choices would be provided by alerting patients and medical professionals and keeping a safe history of who started these acts. The proposed work aggrandize blockchain consensus algorithm that combines the benefits of the PoW and PoA algorithms. These PoW-PoA hybrid consensus methods are simulated with Merkle tree creation focusing conditional contrast large item set tree. The PoW mining mechanism is effectively utilized in proposed system to control the block creation time. With PoA consensus technique, the user generates the real blocks. The validating nodes, which are arbitrary chosen as reliable entities, protect PoA blockchain. The Proof of Authority concept is a highly scalable system since it only requires a small number of block validators. Participants who have been pre-approved serve as the system's moderators and confirm blocks and transactions. According to results, the proposed method performs better than conventional consensus algorithm in terms of scalability, network throughput, and the quantity of blocks generated.
Improving Multi-Channel Wireless Network Security Performance Using Trust-Enhanced Blockchain Deployment

Wireless networks are constantly under security threats due to a large number of inherent vulnerabilities. Most of these vulnerabilities are due to improper deployment architectures, wherein rules for packet access, node access and router access are not strictly defined. These primary vulnerabilities expose the network to other secondary attacks, which further degrade network performance. These secondary attacks include but are not limited to, malicious packet injection due to improper trust levels, checksum invalidation due to packet tampering. In order to reduce the probability of these attacks, this text proposes a novel trust-based blockchain powered multi-channel protocol. This protocol utilizes a distributed blockchain based on proof of work (PoW) consensus for safeguarding the network against security threats. It is observed that the proposed protocol improves security performance by 8% when compared to other state-of-the art methods, and improves the QoS performance by 6% when compared to other blockchain & trust-establishment methods.
A Benchmarking Model based on Research Performance Indicators of Engineering Institutions: A Principal Component Analysis

The study presents the benchmarking model of rule-based decision tree for higher educational engineering institutions in India based on research performance indicators. The proposed model is built to train the engineering institutions for identifying homogeneous group of institutions in form in clusters. The top 100 engineering institutions ranked in five ranking exercises have been considered for the proposed model. A total of ten featured data variables have been considered as high-dimensional space which are considered as academic and research performance indicators. The principal component analysis is applied to transform the high-dimensional featured data space to principal components with maximum variations explained by featured data variables. Two decision trees are obtained from the proposed model based on the publications indexed in Scopus and citations received against publications. Decision trees are achieved in form of rule-based classifier for grouping cluster of homogeneous engineering institutions based on research performance indicators i.e., publications and citations.
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Aiming at preserving the structural features of images from degradations contaminated by impulse noise, this article proposes a novel strategy that inherits the characteristics of ℓ0-norm data fidelity and non-convex total generalized variation regularizer. The ℓ0-norm fidelity is more conducive to removing impulse noise, while the non-convex total generalized variation penalty term is both good at eliminating the staircase artifacts and maintaining neat contours. In addition, a modified alternating minimization algorithm is constructed to optimize the solution of the developed model, which merges the popular iteratively reweighted ℓ1 algorithm and primal-dual approach. Meanwhile, the convergence property of the designed algorithm is briefly presented. Finally, in the experimental part, exhaustive simulation experiments are carried out to demonstrate the effectiveness of the introduced scheme for removing impulse noise. As observed in the numerical results, our scheme possesses obvious superiorities in both visual effects and quantitative comparisons, in contrast to some existing popular image restoration methods.
Automatic Analysis of Gait Cycle Using MediaPipe

One goal of gait analyses in gait rehabilitation is to determine treatment effects. However, gait analysis, which is mainly based on observation, is limited in determining treatment effects because it is only a qualitative evaluation. Besides, quantitative gait analysis requires special facilities and equipment, including the knowledge and skills to handle them. Therefore, this study constructed a quantitative and simple gait analysis system using MediaPipe, which can automatically analyze gait cycles from gait videos captured by smartphones and other devices. Then, I created a classification model using neural networks to discriminate gait states based on the changes in knee and ankle angles estimated from the gait videos. Investigations revealed that although the model could discriminate 94.2% of the gait state changes in the right leg and 93.5% in the left leg, 68.1% of misclassifications occurred near the change in the gait state. This alteration is hypothesized to be because of ambiguities in the teacher data used to train the classification model. Hence, future work should further improve the discrimination rate and verify the limping and shuffling gait in nonhealthy subjects.
Blockly earthquake transformer: A deep learning platform for custom phase picking

Deep-learning (DL) algorithms are increasingly used for routine seismic data processing tasks, including seismic event detection and phase arrival picking. Despite many examples of the remarkable performance of existing (i.e., pre-trained) deep-learning detector/picker models, there are still some cases where the direct applications of such models do not generalize well. In such cases, substantial effort is required to improve the performance by either developing a new model or fine-tuning an existing one. To address this challenge, we present Blockly Earthquake Transformer(BET), a deep-learning platform for efficient customization of deep-learning phase pickers. BET implements Earthquake Transformer as its baseline model, and offers transfer learning and fine-tuning extensions. BET provides an interactive dashboard to customize a model based on a particular dataset. Once the parameters are specified, BET executes the corresponding phase-picking task without direct user interaction with the base code. Within the transfer-learning module, BET extends the application of a deep-learning P and S phase picker to more specific phases (e.g., Pn, Pg, Sn and Sg phases). In the fine-tuning module, the model performance is enhanced by customizing the model architecture. This no-code platform is designed to quickly deploy reusable workflows, build customized models, visualize training processes, and produce publishable figures in a lightweight, interactive, and open-source Python toolbox.
Timing-Attack-Resistant Acceleration of NTRU Round 3 Encryption on Resource-Constrained Embedded Systems

The advent of quantum computing with high processing capabilities will enable brute force attacks in short periods of time, threatening current secure communication channels. To mitigate this situation, post-quantum cryptography (PQC) algorithms have emerged. Among the algorithms evaluated by NIST in the third round of its PQC contest was the NTRU cryptosystem. The main drawback of this algorithm is the enormous amount of time required for the multiplication of polynomials in both the encryption and decryption processes. Therefore, the strategy of speeding up this algorithm using hardware/software co-design techniques where this operation is executed on specific hardware arises. Using these techniques, this work focuses on the acceleration of polynomial multiplication in the encryption process for resource-constrained devices. For this purpose, several hardware multiplications are analyzed following different strategies, taking into account the fact that there are no possible timing information leaks and that the available resources are optimized as much as possible. The designed multiplier is encapsulated as a fully reusable and parametrizable IP module with standard AXI4-Stream interconnection buses, which makes it easy to integrate into embedded systems implemented on programmable devices from different manufacturers. Depending on the resource constraints imposed, accelerations of up to 30–45 times with respect to the software-level multiplication runtime can be achieved using dedicated hardware, with a device occupancy of around 5%.
Analysis of the Next Release Problem to Tackle Requirement Selection

The Next Release Problem (NRP) is a component of the Search-Based Software Engineering (SBSE) domain. The SBSE area proposes to employ search-based optimization techniques to address issues in software engineering. It is an emerging area of research. Selecting the wrong requirement may result in unhappy stakeholders and potential financial loss. This research study examined several optimization techniques for solving the problem of the subsequent release. In this research work, a novel framework is introduced for solving NRP based on different scenarios. The risk associated with decision-making can be reduced when scenarios are built to analyze the needs.
A Secure and Decentralized Method of E-voting using Blockchain and Smart Contracts

Blockchain has already been used in developing many applications, including cryptocurrencies and NFTs. With the help of blockchain and smart contracts, a Decentralized E-Voting System can be developed. A Decentralized E-Voting System is used for activities like voting, verifying the user’s details, adding candidates, starting and ending the election, self-tallying the total number of votes, and giving the results of the elections. A system using blockchain is more secure, faster, transparent, and immutable. The existing systems for voting are less secure as they use EVMs and VVPATs, which can be tampered with. The existing systems are also slower than the proposed model in terms of the time taken to announce the results. There are other problems like Vote Rigging, Polling Booth Capture, and Voting Manipulation. With the increasing risk of cyberattacks, it is of utmost importance to have an online voting system capable of withstanding these attacks. A blockchain-based e-voting system will enable user confidentiality using encryption. The platform is created on the Ethereum network that makes use of smart contracts written in the Solidity programming language. Truffle, Ganache and Metamask are the tools used to create a Decentralized E-Voting System.
Student Data Analysis using Hadoop

The amount of data produced at educational institutions today is growing quickly. It is challenging to analyze the student performance from the vast amount of data using conventional data processing methods. One can quickly analyze the vast amount of data by using big data. Previously, different databases are used for students. So that it takes more time to analyze the performance of a student. By using this application, it takes less time to analyze the data, as it is just like a centralized database where the entire profile of a student is found. In this study, data are analyzed using map-reduce techniques, which is a programming model suitable for processing large amounts of data, and a Hadoop cluster for ease of processing. This helps educational institutions analyze the students’ data better and focus on their weak spots.
Literature Review : Super Resolution for Autonomous Vehicles using Generative Adversarial Networks

Autonomous vehicles have attracted a lot of attention worldwide in recent times due to their capability of operating without human support. With concepts of deep learning and computer vision achieving breakthroughs in numerous disciplines, it has now become possible to develop such vehicles. For the vehicles to perform accurately, cameras are used to extract information from the surroundings which is sent as input to various algorithms for object detection, path detection, road sign recognition etc. The images captured by these cameras must be of high quality, which may not always be possible. To enhance the quality of these images, the concept of super resolution using Generative Adversarial Networks has become prominent. This review study discusses about a generous number of existing research works that are concerned with the task of super resolution that GANs perform and how these can be used in object detection for autonomous vehicles. The research works considered can be categorized into two: super resolution in remote sensing imaging and weather distorted images, all of which can be applied in the development of autonomous vehicles. Innovation along with limitations of the proposed GAN based architecture in these research works have been analyzed with the objective of improving the performance and accuracy of the existing models in the field of autonomous navigation.
Diagnosis and Classification of Breast Cancer Using Multiple Machine Learning Algorithms

In recent times, Breast Cancer is one of the most commonly diagnosed diseases in women worldwide leading to increased mortality. Cancer is life-threatening and needs early and immediate diagnosis and treatment for the survival of the patient. Early detection will increase the survival rate while reducing the treatment cost of later-stage health complexity. In this paper, we show the use of multiple machine learning algorithms for the diagnosis and classification of breast cancer using the Kaggle Wisconsin Breast Cancer (Diagnosis) Dataset. From our results, we have seen that breast cancer diagnosis using machine learning algorithms such as SVM, and random forest, has achieved greater precision and sensitivity. The performance comparison of the results of various classifiers on the classification shows that the best performance on the accuracy, precision, and sensitivity is achieved with SVM 97% with a precision of about 97% for both benign and malignant cases and a sensitivity of 98% for the benign case and 95% for the malignant case. Consequently, we have seen that machine learning is a good way for an early and faster diagnosis of breast cancer.
Deep Learning based Web App for Malaria Parasite Detection in Granular Blood Samples

One of the major health problems that modern humans encounter is malaria, which affects people of all ages. Malaria is a fatal disease caused by parasites carried by the infected mosquitoes. One way for diagnosing malaria is to examine a sample of the person's blood underneath a microscope for the presence of parasites. The project involves the creation of a web app that employs deep learning to recognize malaria parasites in images from blood smears. This can be accomplished by collecting and labeling a dataset of blood smear images utilizing convolutional neural network (CNN) models such as ResNet50, VGG19, and Customized CNN to discover patterns and features in the images. A Convolutional Neural Network (CNN) model is customized by including convolutional layers, max-pooling layers, totally connected layers, and a SoftMax layer. This approach has the power to increase the detection speed, precision of parasite diagnosis and assist in lowering the disease's global health impact.