Reviewerly/exHarmony huggingface dataset:
Analyzing Lung Cancer Data for Machine Learning

Data preparation is a critical step for any machine learning experiment. We have analyzed a dataset derived from images of human male lung cancer tumors. These tumors had been analyzed with genetic markers to identify Y-chromosome loss, which was the case in about half of the samples. Whole slide images (WSI) had been collected and H&amp;E stained by collaborators. We had processed the images with the CellProfiler software to extract numeric features. In this study, we analyzed the data in preparation for training a convolutional neural network to predict Y-chromosome loss from the extracted features, thereby recapitulating the genetic marker analysis. Using Excel and Python, we identified uninformative features and missing data. We predict that data cleaning, informed by these results, will improve the chances of successful machine learning.
External effects on the characteristics of handwriting analysis and the analysis of forgery types.

This project studies the effects of time and hand dominance on different characteristics of handwriting analysis. The handwriting characteristics that were tested are line quality, slope of writing, pen lifts, letter spacing, and word spacing. These characteristics were tested in a subject’s dominant and non-dominant handwriting. Twenty-five adult human subjects participated in this experiment. The research was split into two parts. Part one analyzed the characteristics of a subject’s dominant and non-handwriting during the morning, afternoon, and evening of (preferably) the same day. In the part one phase, participants were asked to write: "The quick brown fox jumps over the lazy dog." Part two analyzed the authenticity of a freehand, simulation, and tracing forgery. A ruler was used to quantify the handwriting characteristics. The line quality of the dominant and non-dominant hands does not change during the different sessions. The slope of the writing changes more with the non-dominant hand. The non-dominant hand has greater letter and word spacing. The dominant hand typically changed the pen lifts number more than the non-dominant hand. “Quick” was the word that had the most changes in pen lifts for both the dominant and non-dominant hand. The freehand forgery is easy to classify as a forged attempt. The simulation forgery can be distinguished from the genuine signature, but it can still be identified as a forgery. The traced forgery could pass off as a genuine signature.
Facial Image Generation with Limited Training Data

Deep learning models have a wide number of applications including generating realistic-looking images. These models typically require lots of data, but we wanted to explore how much quality is sacrificed by using smaller amounts of data. We built several models and trained them at different dataset sizes, then we assessed the quality of the generated images with the widely used FID measure. As expected, we measured an inverse correlation of -0.7 between image quality and training set size. However, we observed that the small-training-set results had problems not detectable by this experiment. We therefore present an experimental design for a follow-up study that would further explore the lower limits of training set size. These experiments are important for bringing us closer to understanding how much data is needed to train a successful generative model.
Media Platform Moodle Assisted Project-Based Learning Model for Short Story Text Learning in Class IX of Junior High School

The purpose of this study was to explain the design of a project-based learning model and the results of its implementation with the help of the Moodle platform in learning short story texts in class IX.The method used is descriptive qualitative method.This study uses a qualitative approach with the ADDIE model development method.Data collection was in the form of teacher and student activities in every aspect of learning model design, short story text assessment instruments, student worksheets and short story text product presentations.The form of data in this study is in the form of scores for short story text assessment sheets.From the results of the research and discussion, it can be concluded that the ability to write short stories in class IX at SMP NU Darul Ma'arif Kaplongan, with a total of 28 students, obtained an average total score of 90.3 out of the highest score of 100.Meanwhile, the field trial phase was carried out at SMPN 1 Karangampel Indramayu which totaling 31 students with an average total score of 80.5 out of the highest score of 100.And SMP Muhammadiyah Karangampel with a total of 31 students obtained an average total score of 80 out of the highest score of 100.Overall implementation results both small group trials and field trials received an average -the average total score is 86.6, it is included in the "Very Eligible" category.That is, the design of a project-based learning model assisted by the Moodle platform media is able to improve students' ability to compose short story texts.Which illustrates an increase in effectiveness and value scores after using the project-based learning model.The design of the Moodle Platform-Assisted Project-Based Learning Model in Learning Short story text is a combination of PjBL and the Moodle Media Platform, so the design of this model is very much needed by teachers in learning Short Story texts.From the implementation of the use of the PjBL model assisted by the Moodle Media Platform, it can be concluded that the developed Project-Based Learning Model Assisted by the Moodle Media Platform in Learning Short Story Texts in SMP/MTS is very feasible for teachers and students to use in learning.
Effect of Mnemonic-Aided Instruction on Academic Performance of Maritime Students in Calculus

This paper describes the effect of mnemonic aided instruction on the academic performance of maritime students in Calculus.Specifically, it aimed to determine the level of academic performance of students before and after the intervention when taken as a whole and in terms of the topic areas.Furthermore, it aimed to determine the significant difference between the level of academic performance before and after the intervention when taken as a whole and in terms of the topic areas (Derivative and Integral).A One-Group Pre-test Post-test Quasi-Experimental research design was used to assess the effect of mnemonic aided instruction on the academic performance of maritime students in calculus during the school year 2021-2022.The data were gathered using a researcher-made instrument in order to answer the study's objectives, as reflected in the table of specifications prepared by the researcher, which had undergone validity and reliability testing.Thirty-four (34) maritime students participated in the study, considering they have complete attendance from the administration of test before intervention up to the administration of test after the intervention.The results showed that maritime students got low academic performance in calculus before the intervention and average level of academic performance in calculus after the intervention.This implied a favorable increase in students' level of academic performance when taken as a whole and in terms of the topic areas after they are infused with mnemonic-aided instruction.Furthermore, the Wilcoxon Signed-Rank test results revealed a significant difference between the level of academic performance before and after the intervention when taken as a whole and in terms of the topic areas.From the results, this study emphasizes the need to consider the infusion of mnemonic-aided instruction to improve students' learning through memory retention in calculus, specifically in the topic areas of derivative and integral.
Convolutional Neural Networks using KERAS for Face Detection and Emotion Recognition.

In the past few years, facial expression recognition has been a popular topic in the field of computer vision. The purpose of this research paper is to analyze the use of Convolutional Neural Networks (CNNs) with Keras for facial expression recognition. This paper will discuss the main architecture of CNNs and the advantages of using Keras for facial expression recognition. It will also discuss the challenges associated with using CNNs for facial expression recognition and the potential solutions. Additionally, a detailed description of the datasets used for the research and the evaluation metrics used to measure the performance of the model will be provided. Furthermore, the paper will provide a comprehensive discussion of the results obtained from the experiments and its implications.
Developing Virtual Reality Apps to Enhance Tourism

In the tourism sector, virtual reality has been used to develop a one-of-a-kind, immersive experiences that give visitors a sense of being there in person. The user is immersed in a three-dimensional experience thanks to virtual reality. Users engage with 3D worlds instead of just seeing a screen in front of them. Virtual reality can be utilized in the travel business to capture tourist places in a distinctive and immersive way. These virtual travel experiences try to replicate the genuine destination as closely as they can. The use of virtual reality (VR) in tourism is currently widespread. People enjoy travelling and learning about new locations, and thanks to virtual reality (VR), they can now learn about and experience a place without actually being there. VR is a fantastic tool with many dimensions and applications. This paper focuses on the implementation of Virtual Reality in the real world through the use of various devices , sensors.
Enhanced Symmetric Convergent Encryption for Secured Data Deduplication in Cloud

Cloud storage is a virtual setting that includes an amazing amount of processing power. The resources are in a state where they can serve users. Cloud data centres are the locations where all of this computing is maintained. In order to produce high-performance computing, the data centre houses a sizable number of powerful computers and servers connected to one another. The physical computer resources housed in the cloud data centre are accessible to the user via virtualization. To provide a convergent encryption method that will enhance data security while reducing deduplication's impact on security, encryption, and decryption times. Convergent encryption is used to prevent duplicating data, but the key used to encrypt data for the first time is kept up to date and distributed to all users who have same or closely related content of data to upload to the cloud storage. In an open cloud environment, it's crucial to distribute the key to all users. When using cloud storage services, users encountered numerous issues.
Advanced End-to-End Image Encryption and Compression

Encryption-then-Compression (EtC) systems are used to securely transmit images over an untrusted channel provider, and a novel grayscale-based block scrambling image encryption method is developed to improve the security of EtC systems. This approach is intended to make encryption-then-compression (EtC) systems more secure. In comparison to the new encryption technology, the suggested scheme allows for smaller block sizes and a greater number of blocks.Despite the fact that the original image has three colour channels, photos encrypted using the suggested method contain less colour information due to the usage of grayscale images to encrypt the data. These features boost security against threats like jigsaw puzzle solvers and brute-force attacks, among other things. Apart from that, despite the fact that the encrypted photos do not include any colour information, it enables for colour sub-sampling, which can improve the compression speed of the images. In a test, encrypted photographs were posted to and later downloaded from social networking sites, and the findings demonstrated that using advanced compression algorithms, the suggested strategy is successful for ETC systems while still keeping excellent compression performance.
Element Based Searchable Encode in Cloud Computing Using Spheroid Arch Alphanumeric Monogram Algorithm (SAAMA)

To obtain statistics from two sources, we need minutiae positions from one source and the orientation from one source, and the reference points from both sources. A combined minutiae template is created with the use of obtained statistics. In the field of remote data management services, cloud package has emerged as a major sector. It raises safe keeping issues because encode is currently the greatest method for avoiding data leakage. A promising method among these is public key encode with keyword rifle (PKEKR), which allows users to quickly rifle through encoded data files and PKEKR come across cloud. The problem is Cloud Server discover the privacy statistics. In our proposed system propose a forward secure Element based Spheroid Arch Alphanumeric Monogram searchable encode scheme. This method is more effective.
Fuzzy Authentication System for Online Transaction Fraud Detection Using Geo Location

In the current days, there is an extensive use of online transaction of information from one place to another place and daily, it is incrementing as an exponential growth. Simultaneously, online activities are also performed by the hackers and thus incrementing the online fraud. Therefore, the present research work is an attempt to minimize online fraudulent activities especially for the financial sectors by implementing the different authentication and authorization schemes at the different levels. In the present work authentication scheme has been proposed by implementing the secure One Time Password (OTP) transmission and Personal Identification Number (PIN) which are based on Global Positioning System (GPS) where transaction is performed then GPS has collected the location details from where the transaction has performed and the location of phone in which OTP is received. The concept of distance has calculated with the both GPS locations and used as a authentication factor. Client Financial Control Application (CFCA) is also responsible for authentication for the online transaction which ables to allow or deny transaction based on account status and fuzzy rule based expert system is used to authenticate the distance, transaction time and other parameters while transaction is performed. The main objective of this paper is to provide security for online transactions and to keep track for online fraudulent transaction.
Incorporating the New PRNG in Advance Encryption Standard

This manuscript explores the fusion of two well-known encryption algorithms, the Advanced Encryption Standard (AES) and the Rivest Cipher (RC4), resulting in a novel encryption algorithm called AES-RC4. AES-RC4 is a block cipher that combines the strengths of AES with a variant of RC4 called RC4-GF, developed by ourselves. RC4-GF incorporates additional steps compared to AES, resulting in a more intricate ciphertext generation process.&#x0D; The introduction of AES-RC4 brings forth a highly secure encryption technique with increased complexity in the ciphertext compared to traditional AES. By leveraging the combined capabilities of AES and the modified RC4-GF, AES-RC4 offers enhanced encryption strength and improved resistance against cryptographic attacks.
Big Data Enabled Realtime Crowd Surveillance and Threat Detection Using Artificial Intelligence and Deep Learning

In recent years, security precautions at public events have become increasingly important as a direct response to the growth in the number of disruptive actions. There are several various varieties of closed-circuit televisions that are employed to perform 24-hour surveillance of public areas and the people that live such areas. Every person in a socialised society with a population of 1.6 billion is subjected to imprinted pictures an average of 30 times each day. Since the entire participation of the community is required to safeguard public areas from the most unexpected and lethal of events, it is difficult to discern whether an incident is an exceptional or casual occurrence because continual monitoring of human data makes it difficult to distinguish the difference. Within the scope of this study, we propose a method for identifying potentially threatening actions within footage obtained from closed-circuit television systems. To accomplish this objective, we must first extract individual still frames from the video and then examine the actions of the people seen in those still frames. We have placed a significant amount of reliance on both machine learning and deep learning algorithms to make this a reality. To automate this process, we must first develop a training model that makes use of many photos and a "Convolution Neural Network" that makes use of the Tensor Flow Python package. This model must be created before we can move on to automating the process. Every frame from every video that is provided will be used to train an algorithm that will analyse the film and evaluate whether it contains suspicious content or merely everyday activities. If we conclude that the activity was suspicious, the next phase of the study will concentrate on locating any weapons that may have been concealed on the corpse.
An Exhaustive Wrapper Method for Feature Selection in Large Dimensional Datasets (WFS)

In this paper, a novel algorithm for randomly selecting a small subset of features from a dataset is presented. Using different combinations of features across a number of trials, the algorithm discovers the best subsets of features. When these subsets of features are obtained, the classification accuracies produced by three classifiers (K-Nearest Neighbor, Support Vector Machines, and Random Forest) are considered to evaluate the performance criterion of the proposed wrapper-based method. Further, to improve the classification accuracy and reduce the cardinality of the selected feature sets, an exhaustive feature selection method (the wrapper method) is used. The proposed algorithm is simulated on eighteen datasets, and the results are compared with those reported using nine comparable algorithms using three classifiers to justify the performance of the proposed algorithm. The average classification accuracies of eighteen datasets achieved are 88.66% in K-NN, 89.88% in SVM, and 89.14% in RF classifier with at most 10 features. The proposed algorithm archives better CA compared to nine comparable algorithms and the results of the experiments prove the proposed algorithm's performance is better in selecting the most effective features compared to other algorithms.
Smart Suspect Tracker Using Deep Learning Model

Each human progress requires a problem free environment since it influences all components of monetary and social turn of events. Quite possibly the main work in keeping a serene local area is finding lawbreakers. In India, finger impression ID is the most part used to distinguish lawbreakers In most of the cases, The fact is that most Suspects these days are becoming more skilled at trying not to leave their thumbprint on the scene, this type of recognizable proof is restricted. CCTV cameras, have been placed in number of public spots and confidential spots to give reconnaissance activities. CCTV can be used to distinguish the denounced rapidly. we give a Profound Learning model to find the criminal location. It's a two-step process: In camera photos, countenances of individuals are first distinguished and afterward perceived. This method might be utilized in an assortment of surveillance cameras in different areas to persistently screen and look for hoodlums inconspicuous. The police headquarters will be cautioned of a suspect's where abouts after the person in question has been distinguished. The police headquarters will be cautioned of a suspect's when he/she has been found.
Progressive Analysis and Predictions of Leukemia (Cancer) Patients on a Machine Learning Model- The APPOLLO Hospitals Accredited

This abstract describes a machine learning model for predicting the prognosis of cancer patients with leukemia. The model is based on analyzing patient data such as age, gender, diagnosis, lab results, treatment history, and prior hospitalizations. The model employs supervised learning techniques such as random forests and gradient boosting to generate predictions of survival probabilities. Additionally, the model uses feature engineering to identify important features and reduce noise in the data. The model is evaluated using the area under the receiver operating characteristic curve (AUC) and other metrics. The results indicate that the model has good predictive accuracy and can be used to identify high-risk patients and guide clinical decisions. The goal of this project is to develop a machine learning model for the diagnosis and prognosis of leukemia in Indian patients. The model will use a progressive analysis of patient data to identify the characteristics associated with leukemia, including genetic markers, environmental exposures, lifestyle factors, and demographic information. Using this information, the model will be used to accurately predict the risk of developing leukemia in Indian patients. Additionally, the model will be used to identify the most effective treatments for those diagnosed with leukemia and to monitor the disease progression. Finally, the model will be evaluated for its accuracy and effectiveness in a clinical setting.
Interpretable Deep Learning Models: Enhancing Transparency and Trustworthiness in Explainable AI

Explainable AI (XAI) aims to address the opacity of deep learning models, which can limit their adoption in critical decision-making applications. This paper presents a novel framework that integrates interpretable components and visualization techniques to enhance the transparency and trustworthiness of deep learning models. We propose a hybrid explanation method combining saliency maps, feature attribution, and local interpretable model-agnostic explanations (LIME) to provide comprehensive insights into the model's decision-making process.&#x0D; Our experiments with convolutional neural networks (CNNs) and transformers demonstrate that our approach improves interpretability without compromising performance. User studies with domain experts indicate that our visualization dashboard facilitates better understanding and trust in AI systems. This research contributes to developing more transparent and trustworthy deep learning models, paving the way for broader adoption in sensitive applications where human users need to understand and trust AI decisions.