Reviewerly/exHarmony huggingface dataset:
Predicting Breast Cancer An Evaluation of Machine Learning Approaches

A disease in which the cells of the breast have uncontrollable growth or cancer cell growth in the breast is known as breast-related cancer. There are various types of uncontrollable growth. In women, it is most common cancer which accounts for around 25 percent of all cancers diagnosed in women. According to the 3rd of February 2023, departmental news of WHO, every year, more than 2.3 million new instances of breast cancer are identified. Breast cancer is one of the major causes of female deaths in 95% of countries. Early diagnosis of BC can decrease the death rate drastically as it is a curable disease when diagnosed in early stages. The classification of diagnosed breast cancer into malignant or benign tumor is a major field of research at present. Use of ML in diagnosis and classification of breast cancer is widely recognized due to its powerful feature in detection from BC datasets. This paper will evaluate various ML techniques such as regression, support vector machines (SVMs), DTs naive Bayes and random forest in the classification and diagnosis of breast cancer. We have used Wisconsin breast cancer diagnosis (WBCD) dataset for paper. A health care system model is also presented along with this paper.
Wireless Sensor Networks Data Anomaly Detection: A Smart Approach

Wireless Sensor Networks (WSNs) are at the core of many critical civil and military applications where they are very amenable for deployment in some harsh/unsafe environments. The WSNs sensed data integrity and validity are very important in all applications, yet vital in some civil and military applications, where any faulty data might result in very serious consequences such as loss of human lives and precious commodities. In WSNs, sensors might report anomalous readings due to many factors related to their environment of deployment, energy level, computing capabilities, hardware failure, wireless media vulnerabilities, etc., which is a serious challenge. Hence, the need arises for immediate detection of such anomalies at the WSNs sink node, to guarantee the processing of accurately sensed data. In this paper, we devise a smart model to address a solution to the aforementioned problematic challenge. In our approach, we utilize different Machine Learning (ML) models in the process of anomaly detection in sensed data. The performances of the utilized ML models are compared, and the top performance model is incorporated into our smart anomaly detection model (SADM). In the design process of our new SADM, we experimented with over 2.2 million sensed data records from the Intel-MIT lab dataset to train and test many different ML models. The preliminary results of our SADM detection accuracy are very promising, which is very encouraging to proceed in this research direction, with different encoding data sets, to generalize the utilization of the new SADM model over a wider set of WSNs applications.
Real-time XSS Vulnerability Detection

In today’s world, web application attacks have become a major concern as they can lead to unauthorized access to important information and even disruption of online services. In the context of web application attacks, one example is XSS (Cross-site Scripting) attack. These attacks are dangerous because they can steal important information like passwords and credit card details or take users to harmful websites without their knowledge. Therefore, in this paper, a plugin has been designed to classify the websites as vulnerable and alert users on not giving data to the respective page to prevent any data loss or potential cyber-attack. This method produces a true negativity of 74.49%, a true positivity of 8.16%, false negativity of 2.04%, and false positivity of 15.31%.
Securing IoT in Quantum World

All data sent to and from IOT devices, (as well as any data held), must be encrypted in order to enhance the security of IOT devices. At the moment, elliptic curve cryptosystems (ECCs) are used as fundamental building blocks to secure communication in the Internet of Things. Yet, forthcoming quantum computers can quickly compromise public key systems like ECC. We should take action right away to get the IOT ready for the quantum future due to recent developments in quantum computing. This paper at first will give a brief overview on Crystal - Kyber, SIKE and BIKE algorithm which are among the NIST’s third round candidate. Then the comparison of these three on the basis of key size, computational power and memory requirement. Finally a recommendation on the most suitable algorithm for making IoT secure in quantum world.
Invasive Ductal Carcinoma Classification Using ResNet18 Model on Transfer Learning Concepts

Breast cancer that begins in the milk ducts and spreads to nearby tissue is known as invasive ductal carcinoma (IDC). Machine learning methods may be used to classify IDC, and transfer learning—which uses pre-trained neural networks like ResNet18—can be a potent strategy for this purpose. IDC, which makes up between 70 and 80 percent. It starts in the milk ducts of the breast and can "metastasize" (spread) to neighbouring tissues or other body regions. IDC frequently manifests as a breast lump or tumour that feels hard, irregular, or otherwise dissimilar from the surrounding breast tissue. Changes in the skin over the breast, nipple alterations (such as nipple inversion or discharge), and breast soreness are other potential indicators and symptoms of IDC. To confirm the diagnosis, further diagnostic procedures like mammography, ultrasound, or biopsy are required because not all breast lumps or alterations are suggestive of IDC. In order to treat invasive ductal carcinoma health disparities in its early stages, the problem must first be diagnosed. Our ResNet18 model was shown to have high classification performance for invasive ductal carcinoma classification with participatory research, with an accuracy rate of more than 80%.
Machine Learning Methods in Natural Language Information Extraction

Information extraction has become a research hotspot. According to the division of ACE (Automatic Content Extraction) conference evaluation tasks, the main research focuses on four areas: named entity recognition, entity relationship extraction, anaphora resolution, and event detection. Among them, entity recognition and relationship extraction are the most important parts of these tasks. Machine learning is a branch of computer science. Its focus is on developing algorithms that can be used to solve problems in the fields of pattern recognition, classification, prediction, and data analysis. Machine learning has been applied in NLP for several years. The most common machine learning technology in natural language processing (NLP) is called supervised learning. This technology involves training models using labeled data, where tags refer to the attributes or features of the training dataset. The goal is to predict which class or category should be assigned to a new unlabeled text sample based on prior knowledge of existing text attributes. This article focuses on machine learning and conducts research in the fields of natural language processing and transmission. In natural language processing, we first explore a general technology for generating word vectors, that is, integrating word embedding. By integrating the existing word embedding vector set and semantic knowledge base, we can generate a higher quality word embedding vector set.
Breast Cancer Detection and comparative analysis of Convolutional Neural Networks and VGG-16

Most common machine learning algorithms were designed to tackle particular problems in seclusion. Based on the business requirements, a machine learning algorithm was employed for a particular task and these algorithms were applied to train models separately on specific feature sets. Most of the machine learning models are trained on the assumption that the test and training sets have similar features while sharing the same underlying distributions. Convolutional Neural Networks (CNNs) have demonstrated remarkable potential for breast cancer detection using histopathology images. Moving forward with the latest research, we present VGG-16 architecture for breast cancer detection, which involves the use of pooling and convolutional layers to extract high-level accuracy from histopathology images. The proposed network consists of sixteen layers, each of which performs convolutional operations on the image to detect local features and pooling operations with the aim of simplifying the feature maps’ dimensionality. The test results illustrate that the VGG-16 architecture secures superior classification accuracy when compared with the previous cutting-edge models.
Action Recognition of Skateboarding Tricks – Ollie and Kickflip Using Neural Network

This research project aimed to develop a computer-based system using deep learning techniques to accurately detect and recognize skateboarding tricks, with a focus on ollies and kickflips. A deep learning architecture that combined RCNN, Mobile Net with Bi-directional LSTM, and CNN was proposed and implemented on a dataset of 222 skateboarding trick videos categorized into two subdirectories - Ollie and Kickflip. The proposed models were trained to precisely identify the different skateboarding motions, and the results of the experiment showed how well the recommended deep learning model classified ollies and kickflips. The trial results revealed that the algorithms were highly effective in guiding viewers through the process of scoring skateboarding films. Based on their accuracy, precision, recall, F1-Score, and AUC, the three deep learning models CNN, CRNN, and Mobile Net with Bidirectional LSTM were assessed. The results showed that CRNN had the highest accuracy and AUC of 79% and 86 respectively, while Mobile Net with Bidirectional LSTM had the highest recall and F1-Score.
Implementation of a Deepfake Detection System using Convolutional Neural Networks and Adversarial Training

Due to the threat of spreading erroneous information, the employment of deep convolutional neural network (CNN) techniques has resulted in an increase in the number of altered images. A trustworthy and effective mechanism for identifying false photographs is required to solve this problem. In this study, we used the V4D architecture to analyze manipulated videos and concentrated on identifying DeepFake videos in three different contexts: I identifying all types of manipulations, (ii) identifying single manipulations, and (iii) identifying cross manipulation detection, which was used to assess the videos' authenticity. To do this, we used a variety of manipulation techniques and created algorithms to classify undiscovered manipulation methods.
A Novel Women Safety Analyis and Monitoring Sysetm over Social Media using Machine Learning

In the current scenario, women community facing issues like gender discrimination, schooling, child marriage, sexual assault and harassment, and much more, not just from society but also from social media. Women are protected by organizations like the She Team, Disha Act, and many others in society, but these organizations are much less in social media. Social networking sites like Twitter, Instagram, Facebook, etc. cause problems for women. This paper focuses on the safety analysis and monitoring of women using various social media platforms in Indian cities. The posts on Facebook and Instagram, as well as tweets on Twitter, that abuse women are considered and show the percentage of threats that women face from social media, which aids in understanding by the youth of India who misuse the women’s safety and harass them in social medias via tweets, posts, and text should face strict action. People may grasp the threats to women with the help of this, and it demonstrates that women face challenges not only from society but also from social media platforms. The outcome is easily comprehended in the form of a graph and a pie chart. Algorithms such as Nave Bayes (NB) and XGBoost are used in the analysis of women’s safety on various social media sites. The goal is to use classification techniques to categorize or forecast the Type based on dataset properties. Using categorization algorithms, we can determine whether social media content is positive, negative, or neutral. It has been substantiated that Naive Bayes algorithm has proved better accuracy compared to random forest and decision tree algorithms.
Multi-Document Summarization Made Easy: An Abstractive Query-Focused System Using Web Scraping and Transformer Models

The paper proposes a web-based abstractive query-focused multi-document summarization system that aims to simplify the process of summarizing multiple documents on a given topic. The system leverages a range of technologies and techniques, including web scraping, natural language processing, and transformer models, to automate the summarization process and improve the accessibility of information for users. The system is designed to take user input in the form of a query, the number of words to be summarized, and the number of documents to be referred to. It then utilizes Google search engine API integration to retrieve the most relevant webpages based on their ranking, and performs web scraping of tags using beautiful soup (bs4) and selenium frameworks. The scraped data undergoes pre-processing, including stop word removal, tokenization using Auto tokenizer, and visualizing frequency matrix and word-cloud plots with seaborn and matplotlib. The system employs a transformer model ‘mt5-small Pretrained’ as the pipeline summarizer. The transformer model ranks the words based on frequency and generates a summary of the text that is coherent, concise, and relevant to the user’s query. The system delivers the output in the form of a well-structured summary that captures the essential information from multiple documents. The experimental results demonstrate the potential of integrating different technologies and techniques to automate the summarization process and provide users with high-quality summaries of multiple documents on a given query.
Robot Learning Method Based On Video

With the arrival of 5G era and the continuous development of computer technology and digital video technology, the amount of video data is increasing day by day. Therefore, there is a technical requirement in real life that can significantly shorten the video duration while retaining the main content of the original video. The emergence of video summarization method successfully meets this demand. It can reduce the time for people to browse videos and save a lot of storage space at the same time. Video summary is the process of automatically extracting key information from video and presenting it in the form of summary. It has been proved to be useful for both humans and machines because it can help them understand the main idea of video without watching the whole video. In this paper, we will focus on using deep learning technology for video summarization. Deep learning is an effective technology for many tasks, because it can learn complex functions by analyzing data samples. This also makes it an ideal summary technology, because video contains a lot of information needed. This paper studies various types of video, and proposes a robot learning method based on video summarization to improve the performance of video summarization algorithm and generate video summaries that interest users.
Analysis of CPU Utilization of a Cross-Platform Web Application for Facial Recognition based Remote User Tracking System

One of the most common and integral tasks in any institution is to track and maintain attendance records of its employees. Whilst there are multiple options available at present to accomplish this, they are either time consuming or often prone to errors. More importantly, they are incompatible with different kinds of mobile handsets that employees use nowadays. To tackle this challenge, we propose a simple user-friendly cross-platform mobile application using Flutter – a cross-platform application development framework that accommodates facial recognition and GPS tracking features. Besides these aspects, the CPU utilization of the proposed mobile application is also analyzed, ensuring it uses minimal computational resources. The proposed application is able to track a user’s location and record attendance precisely and remotely, minimizing the need for manual logging of attendance. This provision can play an integral role in these post-pandemic times, as it eliminates the requirement for users to use fingerprint-based or RFID-based attendance systems. Additionally, the GPS features make it feasible for employees working remotely from their homes, or on location such as a client visit, to clock-in their attendance. The proposed application can help reduce paperwork required in traditional attendance monitoring as well human errors, while also eliminating long queues prevalent in traditional attendance systems and other health and safety risks – making it a powerful real-world tool following the Covid-19 pandemic.
Anomaly detection using Federated Learning: A Performance Based Parameter Aggregation Approach

Anomaly detection is a significant challenge that exists in multiple industries, including healthcare, finance, and manufacturing. Machine Learning techniques are extensively being used to solve these problems. However, data privacy concerns and regulations are making acquiring the necessary data for model training increasingly challenging, particularly in sensitive sectors. Federated learning has emerged as a promising solution, allowing local models to be trained on edge devices using local datasets and model parameters to be shared with a central server for aggregation. This allows the models to reap the benefits of the distributed data without actually sharing any data. However, the current aggregation method used in federated learning is susceptible to models trained on bad data, which can impact the accuracy and reliability of the resulting model. To address this issue, the study proposes a new filtered aggregation algorithm that greatly mitigates the effect of models trained on bad data. The proposed algorithm accounts for models that cannot detect anomalous patterns and gives them much less representation in the final model, resulting in improved accuracy and reliability.
IsItPhish: Dynamic Phishing Website Detection

This Paper Discusses the development of a Chrome extension for detecting and preventing phishing attacks. The review gives an overview of the proposed methodology and their findings of the study, including the accuracy of the proposed approach. The paper also discusses the significance of research and its potential impact on improving online security. Overall, the review highlights the importance of the research and its contribution to the field of digital forensics and security. The paper also provides an overview of the current state of phishing attacks and their impact on the security of online users. The methodology and experimental results of the study are discussed in detail, including the dataset used and the performance metrics evaluated. The paper also identifies the drawbacks and difficulties of the suggested strategy and recommends new lines of inquiry for this area of study. Overall, this analysis offers insightful information about the usage of machine learning methods for identifying phishing websites and their potential utility in boosting online security.
Remote Health Monitoring System using Secure Communication Techniques

Heart related diseases causes elevation to the fatality rate across the world especially post-Covid. Furthermore, high security cryptographic algorithms are used to provide confidentiality and availability to sensitive data collected from imbedded medical devices. To escalate the rapid and prior diagnosis of cardiovascular diseases especially if a person has a history of heart related ailment, an effective solution is to bring Machine Learning Models and Data Analysis for operative decision making and precise cum accurate prediction. In the proposed work, encountering numerous challenges around the machine learning models along with a sound and lightweight encryption technique to provide added security to the sensitive medical data of the patient for careful analysis by both the model and prescriptive doctor-patient interface using an IoT based cloud environment running in the background has been used. The sensor data will be used in two fronts - on one end the data will be uploaded to the ThingSpeak IoT platform where it will provide the analytics of the patient health status (both past and present) and a likelihood of chance of experiencing any heart-health abnormality to the patient there and then along with on the other end it is stored on the Fire-Base Cloud Storage space which is stored with encryption protocols to enhance the security of the patient’s sensitive health data which can be reviewed by an expert doctor. Experimental outcomes show us that K-nearest neighbor algorithm achieves the best results among logistic regression, support vector machine, random forest tree and voting classifier with accuracy of 88.5% and 88%, 86%, 85.2% and 86.88%. Therefore, the suggested model is successful is achieving an effective accuracy by secure IoT data in cloud based IoT platforms.
Sarcasm Detection on News Headline Dataset Using Language Models

Mechanized detection of sarcasm in textual data is a challenging task in NLP, as it involves recognizing the opposite of what is actually meant. This has become important today since many artificial intelligence tasks require understanding the user and answering appropriately. In this paper, we investigate multiple deep learning models for sarcasm detection on a news headline dataset, including BiLSTM and BERT. For the first time, hybrid BERT models have been used in our study to identify sarcasm in non-social media writings. The results show that the BERT and GBDT strategy combined performs better than all the other strategies listed.
Anthropomorphism of AI based chatbots by users during communication

Advancements in artificial intelligence (AI) provide new means of communication to users for various purposes. It also provides digital assistance through live chat interfaces which provide real time services to users. In modern times, services of human agent are getting replaced by software agents like chatbots designed with the help of natural language generally based on artificial intelligence. Anthropomorphism is personification of non-human entities/objects. Drawing on social response theory, this paper intends to examine whether the users anthropomorphize chatbots as they are expected to show the factors of trust, expertise, predictability, human-likeness, ease of use, risk, reputation and propensity to trust technology. Our findings demonstrate that users anthropomorphize the chatbots during their interactions resulted from an explanatory investigation undertaken. This paper intends to extend the current knowledge base related to natural language interfaces applicable to digital interactive agents other than chatbots.