Reviewerly/exHarmony huggingface dataset:
Altruistic: An Assistive Mobile Application Featuring Cluster Based University Admission and University Suggestion for the Bangladeshi Students

To resolve the time and cost burden of admission aspirant students, public university authorities of Bangladesh have decided to enroll students via a cluster-based university admission process. At present, the cluster-based admission process mainly relies on an offline-based process and thus does not offer satisfactory user assistance results. There is an absence of a suitable online-based central university admission application process for students with admission result checking, eligibility checking, notice board, review, economic and housing help, complaint and authority contact feature. There is a lack of a proper university suggestion process based on the users study subject, location, cost, and user rating preference. To demolish the existing problems, this paper provides an assistive mobile application featuring cluster-based university admission application and preference-based university suggestions for Bangladeshi students with admission result checking, contact authority, economic/housing help, compliant, notice board, and add review feature. The reviewer’s recommendation results revealed that more than 55% reviewers submitted propitious remarks regarding the application features suitability and 60% reviewers submitted highly recommend remarks regarding the time/cost benefits of the proposed application.
ROI Encryption for the CABAC in VVC Codec

In coded video sequences, the Versatile Video Coding (VVC) standard allows the frame to be divided into non-square coding blocks that are executed independently. This work introduction the solution for encrypting coding blocks containing Regions Of Interest (ROI), which can be used to protect the privacy of video content in VVC frames. The proposed method encrypts all CABAC syntax elements in ROI blocks at the bitstream level, and encrypts ROI blocks while maintaining the same bitrate and format requirements. To prevent the encryption from leaking outside the ROI boundaries due to mutual prediction, motion vectors in non-ROI regions are restricted to the unencrypted blocks in the reference frame. Simulation results show that the ROI containing faces can be well performed by proposed encryption algorithm, while the background regions remain clear. Thus, the proposed solution provides secure and adaptive encryption for ROIs in VVC videos.
JPEG-XL based Compression of DICOM Images for Reduced Storage and Transmission Costs

Medical images are largely available in the form of DICOM. These files occupy large disk space and take much time to be transferred for diagnoses purposes. Compression algorithms help in reducing the file size and the data transfer rate. Some common DICOM compression algorithms are: Joint Photographic Experts Group (JPEG), a lossy compression algorithm, JPEG 2000 and Run-length encoding (RLE). With the recent emergence of JPEG XL, the algorithm’s performance outperforms the existing algorithms and is aimed to replace them. JPEG XL can compress in both lossless as well as lossy. This paper provides a comparative analysis of Image Quality Metrics like RMSE, PSNR, SSIM within the lossy and lossless modes of JPEG XL algorithms as well as a comparison between the compression ratios of JPEG XL and RLE algorithms. Hence, this paper suggests an emerging Lossless compression algorithm for a universal replacement for medical file size reduction.
Face Mask Detection Using CNN via Active Learning

The COVID 19 not only has changed the way we live but it also changed how hospitals operate. Security in hospitals now-a-days is given utmost priority. As one small mistake can easily change the life-saving hospital into a Coronavirus Hot spot. Face masks are by far the most important protection measure during the global pandemic COVID19 began to spread. The most effective preventative precaution against the corona virus COVID19 pandemic, according to the World Health Organization, is using a face mask in public settings. Face mask detection is performed by utilizing a deep learning model. In terms of image recognition models, Convolutional Neural Networks (CNNs) have established themselves as the dominating class. In this work we apply Active Learning with CNN using the five query strategies i.e. Largest Margin, Smallest Margin, Entropy Reduction, Least Confident and Random Sampling. Our experiments achieved an accuracy in the range of 62% to 72% and the gain achieved via active learning is in the range of 73% to 79%.
Privacy-Preserving of Edge Intelligence using Homomorphic Encryption

Edge intelligence has paved the way for a stronger and more secure system by combining security and artificial intelligence. A lot of research have been undertaken, revealing remarkable development and a variety of viewpoints on this novel phenomenon. We suggested a way enabling edge intelligence to move via homomorphic encryption-enabled gadgets and automobiles. This encrypted data will be delivered to an edge node for the building of a machine learning model in order to gather any potential information hidden in the data as well as any potential obstacles that may need to be addressed before any event happens. Several machine learning models, such as KNN, K-means, SVM, and others, are used to gain the best possible data analysis. Once secure, decisions will be taken, and the outcome will be visible. All machine learning training will be done in encrypted data, which will be encrypted to ensure that no one’s privacy is abused. This may be set up for a variety of machine learning modules.
Student Writing Segmentation Using Various Classification Techniques

Writing is a crucial aspect of learning. It encourages you to clarify and sort out concepts and analyse textual evidence, and practice conclusions, which improves understanding and aids the cognitive development. This current study proposes an automated framework model that can assess and offer feedback on a student’s argumentative writing skills by segmenting the texts and classifying them. As a result, it will contribute to giving individuals more opportunities to develop their writing. Most of the 15601 text files (144294 sentences) used in the evaluation were written by students in grades 6 to 12. Deep learning models were developed based on a random collection of 80% of the phrases, and then evaluated on the other 20%, in order to assess the automated analysis of evaluation in writing. To compare the outcomes, we have employed classification machine learning methods like Passive Aggressive Classifier and Random Forest with deep learning techniques including Recurrent Neural Network i.e., RNN and Long short-term memory i.e., LSTM. Using these methods, student’s essays were segregated based on lead, position, claim, counterclaim, rebuttal, and conclusion. The accuracy of our suggested BiLSTM based model is 74.38% and 81.23% ,LSTM-based model is 76.2% and 88.49% based on different hyperparameters, 62.4% for Random Forest-based models and 61.5% accuracy for Passive Aggressive Classifier (PAC). We can infer from the accuracy that LSTM performs better than BiLSTM, RF and PAC in terms of performance. As a result, this study aids in essay segmentation and generates a solution for text segmentation that is fairly accurate.
Privacy-Preserving sensitive data on Medical diagnosis using Federated Learning and Homomorphic Re-encryption

Federated learning is an emerging technique that allows multiple participants to train a shared model without exchanging their private data. This approach is particularly useful for IoT applications, where data is often collected locally and stored in distributed devices like edge nodes. Privacy concerns arise when using IoT devices to record medical data for distributed learning. Techniques such as differential privacy and federated learning can help ensure data security while preserving privacy. Encryption and secure multi-party computation can also be used to securely share and compute data. By training a model on a combination of data from these devices. In the medical research, federated learning can be used to train models on data from multiple healthcare devices such as wearables, smart medical sensors, and electronic health records. By training models on local data, healthcare providers can improve the accuracy of diagnosis and treatment while protecting patient privacy. Security analysis can involve evaluating the potential vulnerabilities and risks of the system and identifying measures to protect against them. Experimental results can involve testing the performance of the system in terms of accuracy, convergence speed, and other metrics, and comparing it to other methods.
An Automated Attendance System Using Face Recognition Technique based on Haar Cascade with LBPH

In educational institutions, attendance used to be manually recorded on attendance sheets. This method doesn't work since substitute pupils can easily use them. This study introduces a face recognition-based automated attendance system that uses an image of the entire classroom to determine students' presence by matching their faces to a trained set. Everyone's faces are captured by a camera positioned in the classroom and sent to this system, which analyses them to find and recognize individual faces as well as log attendance in the main database. By capturing features on images, the Haar Cascade classifier was used to detect faces, and the Local Binary Pattern Histogram (LBPH) was used to recognize faces. This system is capable of recognizing students even if they are wearing glasses or have a beard.
Securing Medical Images using Compression techniques with encryption and Image Steganography

Medical imaging is an essential component of the healthcare sector, enabling the transfer of medical images between widely separated locations to improve healthcare business practices. However, the transmission of medical images across public networks can result in several security concerns, including those related to authentication, integrity, and confidentiality. Therefore, the major goal of this research is to ensure the security and accuracy of medical images by maintaining their confidentiality and integrity. This proposed method aims to enhance the security of medical images during their transmission across public networks. The method involves three main steps: compression, encryption, and embedding To achieve the goal of maintaining the integrity and confidentiality of medical images, this research utilizes a multi-step process. First, the medical image undergoes compression using the Singular Value Decomposition (SVD) technique to reduce its size and eliminate any noise. Following this, the compressed image is encrypted using the Advanced Encryption Standard (AES) algorithm to ensure confidentiality. Finally, the encrypted image is securely enclosed within a cover image using the Least Significant Bit (LSB) steganographic technique to create a stego image. This technique ensures that the encrypted image is seamlessly incorporated within the cover image, without any visible or perceptible changes. This method ensures that the medical image remains confidential and maintains its integrity throughout the transmission process. Furthermore, this study developed the aforementioned technique, and the experimental outcomes were compared using MATLAB to hide medical images without encryption using LSB steganography as an additional layer of security. This study analyzed multiple medical image formats, including DICOM, TIFF, BMP, and JPEG. The findings indicate that combining encryption and steganography techniques provides improved results in terms of PSNR and MSE values, as contrasted to utilizing steganography alone.
Augmented Reality in Education: An Insight into Current Trends and Limitations

It is observed that the traditional and conventional teaching methodologies adopted in present educational system lead to low levels of student participation often due to lack of specialist preparation and teaching. There is a necessity of utilizing cutting-edge and successful teaching strategies in the existing educational system in order to guarantee successful learning results. One approach that is being investigated these days to address these issues is augmented reality (AR), since it may provide a fully comprehensive and collaborative atmosphere for learning. This review explores AR and its application in the technical field, ranging from its uses in training and teaching. The paper also mentions other studies that provide light on augmented reality and its likelihood to improve quality education. Various existing works have been reviewed and many advantages of using have been identified. Further, the current limitations which can serve as a future research area to improve the quality of education have also been presented.
Solar Power Prediction Based on Recurrent Neural Networks Using LSTM and Dense Layer With ReLU Activation Function

In the past decades, power production from renewable energy sources has been increasing at a tremendous rate. Such increased production had led to various benefits such as improvement of environmental conditions, production of energy independent of fossil fuels and reduction in the cost of energy production. To enjoy the benefits of renewable energy and its production in an optimum manner, it is important for us to accurately predict renewable energy production. In this paper, a model that uses deep neural network to predict solar power for two different horizons is proposed. The proposed method predicts solar power for five minutes and one hour ahead based on the observations made in the past two hours. The proposed model is executed in python software using the deep neural networks technique and is compared with an existing method in literature.
Swarm Based Optimized Key Generation for Preserving the Privacy in Cloud Environment

Cloud computing may be described as a collection of software applications and hardware components that are used in tandem to provide users with a variety of cloud services in response to their specific needs. Cloud computing ensures a significant position in the technological business by allowing users to access services from almost any location in the globe. Cloud computing's biggest concerns are data privacy and integrity. The cloud's distributed nature makes it challenging to build a unified communication system. Therefore, it is crucial to establish security measures that guarantee privacy during data transfers. The primary goal of this study is to provide an optimization method for addressing the cloud industry's privacy preservation issues. Sensitive information is protected from unapproved access by having it obfuscated via data sanitization. The work comprises of generating an optimal key for data encryption through Kronecker method. The key generation is done using proposed Enhanced Harmony Search Algorithm (EHSA) where the continuous values of Harmony search method will be converted into discrete model using roundoff method. The results are compared with the recent state-of-the-art algorithms to prove its significance.
The Construction of Intelligent Classroom for Blended Teaching of Cognitive Load and College English Spoken Language Based on Data Mining

Building an intelligent classroom that combines cognitive load and college English teaching is a key step in improving students' learning experience. Blended learning combines traditional face-to-face teaching with online learning, creating a more flexible and interactive learning environment. The use of technology in the classroom can help reduce cognitive load and enable students to focus on understanding complex concepts. Intelligent classrooms should be equipped with modern technologies such as interactive whiteboards, projectors, and audio-visual aids to facilitate the delivery of course content. In addition, the use of artificial intelligence can provide tailored feedback and suggestions based on the individual needs of each student, thereby helping them personalize their learning experience. Incorporating blended learning into college English courses can also provide opportunities for real language use through online communication tools, thereby improving language acquisition. This method can help students develop language skills in real life while reducing cognitive load. In short, building an intelligent classroom for blended learning is an important step in improving the quality of higher education. It provides an opportunity to integrate technology into traditional teaching methods, creating a more attractive and effective learning experience for students.
Fuzzy C-Means Clustering of Soil data Samples

This article is inspired from the remarkable results exhibited by AI (Artificial Intelligence) techniques in the field of data analytics. The soft computing approaches such as neural networks and fuzzy logic also belong to AI family. These techniques are very effective in making decisions in case of partial or vague information available. So they are very popular. Data clustering is also one of the very significant features of these techniques which is basically an unsupervised learning. In the present work, the clustering of soil related data has been accomplished based on its fertility using Fuzzy C- Means (FCM) clustering in the MATLAB. The accuracy of developed model is also evaluated.
Quantum Resistant Cryptographic Systems for Blockchain Network

This paper presents a comprehensive examination of post-quantum cryptographic techniques, also known as quantum encryption, which offer protection against quantum attacks. It explores various methods such as hash-based cryptography and lattice-based cryptography that can safeguard cryptographic hashing and encryption against the potential vulnerabilities posed by quantum computers. The focus is specifically on the implications for blockchain technology, as it is susceptible to compromise during quantum attacks. The study provides an overview of quantum cryptographic encryption techniques that are poised to withstand quantum attacks, ensuring the resilience and quantum-proof nature of cryptographic measures. Blockchain is a peer to peer network that maintains distributed ledger. The peers in the network shares information in the network that is secured using typical cryptographic mechanisms. The proposed system aims at integrating quantum resistant cryptographic techniques in the blockchain network which ensures enhanced security to the information shared in the network. The proposed algorithm improves level of security to the typical blockchain network.
Research on the Application and Development of Intelligent Algorithm in the Field of New Media Communication

Mobile Internet, Internet of things, big data, cloud computing, intelligent human-computer interaction and artificial intelligence algorithms jointly promote the development of current media platforms and systems in the direction of intelligence. In the development process of media intelligence, social information communication system turns to online social information communication system.The application of intelligent algorithm in the field of new media communication is a human-computer interaction (HCI)which uses artificial intelligence (AI)and machine learning to improve user experience.It can be used in various applications, such as social network, search engine optimization, recommendation system and so on.The main advantage of using artificial intelligence in human-computer interaction is that it can help users obtain more relevant information with less effort. When you use AI tools instead of manual processes,the process becomes faster and easier.This can also reduce costs by reducing the labor hours required to achieve the desiredgoals
Website Classification Through Exploratory Data Analysis Using Naive Bayes, Random Forest, and Support Vector Machine Classifier

Website categorization is the process of grouping websites resources according to their content, function, and other characteristics. Website categorization is crucial for a number of reasons, including supporting users in finding pertinent material, allowing advertisers to target particular demographics and social policy, and assisting businesses in recognizing and keeping track of emerging online trends and security issues. This study uses Naive Bayes, Random Forest, and Support Vector Machine classifier's to categorize websites and forecast accuracy for these parameters. This study employs three different classifier types to pinpoint and address security flaws in websites. Penetration testing, vulnerability scanning, and code review are some of the social techniques that may be employed in this kind of study. This study concentrates on how a website's visual design might influence users' perceptions and actions since it predicts an accuracy of 88% using the Naive Bayes Classifier.an accuracy of 88% using the Naive Bayes Classifier.
Parameter Design Approaches based on AI Techniques for Transformer Neural Network Optimization

The Transformer neural network is a magnificent monster of the deep learning world, demanding attention with its capacity to digest data sequences with unmatched efficacy. It has a towering self-attention mechanism and powerful feedforward layers. People are in awe of its skills as a result of its skill in multimodal activities, language interpretation, and picture processing. Certainly, artificial intelligence (AI) continues to advance more quickly. Therefore, the preparation, procedure, preservation, and commercialization of the energy infrastructure are hotspots for research approaches based on data-driven technologies AI. In several areas of picture analysis and evaluating, including self-driving automobiles, it performs remarkably well. A brief overview of the transformer neural network and the specified input define are presented in this research. Next, a transformer neural network design accompanying model for tasks involving natural language processing is outlined. Following that, papers relating to distinct transformer neural network types together with distinct techniques and approaches have been addressed here through multiple points of view of different scenarios. This study additionally outlines many transformer neural network applications. Finally, various artificial intelligence-based strategies for transformer design optimising and prospective directions are highlighted.
Image Encryption using Chaotic maps: State of the art

Due to the swift growth of the internet and the increasing flow of data across public networks, data security is becoming more and more crucial. One potential resolution to this issue is encrypting the information which might take the form of text, images, audio, video, etc. Today, photographs are used in the majority of multimedia applications. AES, DES, RSA, and other early picture encryption algorithms have low levels of security and poor anti-attack capabilities. By adopting chaos-based cryptography, this issue was solved. The chaotic systems have the potential to be used in image encryption because they are sensitive to the starting conditions and control variables. Many research endeavors have been focused on investigating image encryption. An overview of the components and methods of the image encryption design is attempted in this work. We used statistical analyses, such as entropy and pixel correlation, as well as differential analyses, such as unified averaged altered intensity (UACI) and number of changing pixel rate (NPCR), in order to assess the security against various cryptographic assaults. Analyses of key sensitivity and key space are also carried out to thwart brute force assaults. The randomness of the proposed encryption system was further examined through entropy and NIST randomness suit tests.