Reviewerly/exHarmony huggingface dataset:
Diabetes Prediction using Extreme Learning Machine: Application of Health Systems

Recently, the health sector is widely adopting artificial intelligence models such as machine learning (ML), deep learning for data analysis, disease prediction, and disease classification. However, the conventional models failed to analyze the data. Therefore, this work is focused on analysis of diabetes prediction using extreme learning machine (DP-ELM) model. Initially, Pima Indian diabetes is considered, which is pre-processed for missing data symbols identification. Then, the statistical features from pre-processed dataset are extracted using principal component analysis (PCA). Then, ELM model is trained with the PCA features and forms the trained feature dataset. Then, a random test combination is applied for ELM testing, which classifies the positive and negative status of diabetes. The simulations proved that; the proposed DP-ELM outperformed in terms of accuracy as compared to existing methods.
Named Entity Recognition for Protecting Sensitive Data using Hybrid CNN

Named entity recognition is a natural language processing technique that effectively recognizes and categorizes named entities in a document. The named entity recognition helps to bring out dynamic information about a document or gather critical data to store in a database. Deep learning helps to develop over time, while NLP examines the structure and standards of language and produces an automated system that can discern meaning from text. Mining the essential entities in a text helps identify related data, which is vital when functioning with enormous datasets. The proposed system has a feature that can retrieve and identify sensitive data such as PAN numbers, bank account numbers, and Aadhar numbers from unstructured text data.. The proposed model is designed using Hybrid CNN and it attains 95% F1-Score, 97.5% precision, and 98.3% recall.
Acute Lymboplastic Leukemia Detection Challenges and Systematic Review

-Leukemia is considered as the type of cancer that occurs on the blood cells. It leads to enlargement of the cells and become immature cell, and then it affects the other cells also. In recent times, the testing laboratories have consumed more time to detect as well as to diagnosis the Acute Lymboplastic Leukemia Detection (ALL), which is tedious to detect by humans. Moreover, the manual models and the existing models are also costly and time-consuming too. To overcome from these issues, various Computer Aided Diagnosis (CAD) techniques for analyzing of blood smear microscopic images have been designed by utilizing deep learning and machine learning techniques. Thus, this paper intends to give the literature review for detecting the acute lymboplastic Leukemia with the help of various development approaches. It is also illustrating the wide range of existing studies regarding acute lymboplastic Leukemia detection using machine or deep learning, performance analysis, and its challenges. Since the former methodologies exist with some downside factors, the review paper is helpful for future research work Consequently, the literature review is followed by fundamental prerequisite of acute lymboplastic Leukaemia detection, techniques for forecasting the disease, distinct data sources utilization, analyzing with diverse validating metrics, implementation tools, and also exhibits the information in terms of challenges and motivation towards the future direction.
Machine Learning Framework for Women Safety Prediction using Decision Tree

In every city, harassment and violence becomes one of the major problems for women. Further, women’s personal life is suffered by the bullying and abusive content presented in Online Social Networking (OSN). Therefore, it is necessary to identify the women safety in OSN environment. When it came to predicting the maximum safety analysis, however, traditional methodologies came up short. This study, then, employs a decision tree (WSP-DT) classifier to make predictions about women’s safety. After considering the Twitter dataset for system implementation, it is pre-processed to get rid of the blanks and the unknowns. The tweets were then processed by a natural language toolkit (NLTK) that handled tasks including tokenization, case-conversion, stop-word detection, stemming, and lemmatization. Next, we create a text blob protocol to determine the positive, negative, and neutral polarity of pre-processed tweets. To further extract the data characteristics based on word and character frequency, term frequency-inverse document frequency (TF-IDF) is used. At last, a decision tree classifier was used, based on several rounds of training, to determine if a tweet was phoney or real. Testing on the Twitter dataset demonstrates that the proposed WSP-DT classifier outperforms the competition in simulations.
Alleviating the Naive Bayes Assumption using Filter Approaches

Particularly compared to other machine learning algorithms, the Naive Bayes algorithm is notable for its widespread application in a wide range of classification problems despite its seeming simplicity. However, the conditional assumptions it makes about the dataset are largely invalidated by the real-time data. As a result of this, the performance of the prediction is less than optimal. To solve the problem, a multitude of researchers came up with a variety of methodologies to alleviate the NB assumption. 1. Structure Learning, 2. Feature Selection, 3. Data Expansion, and 4. Attribute Weighting is the headings for the different strategies that have been proposed to mitigate the effects of the NB assumption. In comparison to the other methods, feature selection has garnered significantly more attention in recent years. Since the intention of feature selection is to find the relevant subset of variables from the complete dataset in order to improve the NB prediction and fulfill the NB presumption, it is crucial that this is accomplished without diminishing the amount of information known about the dataset. The use of feature selection in conjunction with NB is implemented in this work in order to alleviate its conditional assumption. There are several sorts of feature engineering approaches seen in nature, and employ the filter FS technique in the proposed research. Because this filter FS works quickly and effectively in comparison to other FS methodologies. In this work, two distinct Filter FS are applied, and the features that are recorded are then modeled using the NB algorithm. The experimental technique is carried out from two distinct points of view: the first perspective uses Filter FS with NB, while the second perspective uses NB only. The outcomes obtained from the two methods are evaluated, compared, and forecasted with the help of a variety of validity scores. According to the conclusion of the research, Filter-NB performs much better than NB. Also, the time complexity of filter-NB is more effective than that of NB.
A Bitcoin Transaction Network using Cache based Pattern Matching Rules

Crypto currencies usage increasing every year around the world. The Bitcoin is the one of the famous cryptocurrencies, which is an unofficial usable currency in various nations. The bitcoin transactions are increasing, which needs to be monitored carefull y. However, the conventional methods are failed to analyze the bitcoin transaction effectively. Therefore, this work focused on development of bitcoin transaction network (BTN) using pattern matching rules (PMR). Initially, the dataset preprocessing is carried out to identify the missed symbols, unknown characters from forensic blockchain dataset. Then, Petri-Net model applied on preprocessed dataset, which identifies the time stamp, transaction id, work tera hash, and work error properties. The Petri-Net model mainly used to parse and build the BTN model. Then, PMR conditions are developed to extract the transaction addresses extracted with time stamp details. So, PMR detects the illegal payment addresses by matching the known data with illegal (spam) addresses. Further, cache based PMR (CPMR) is also applied to detect the fraud transaction, which store all previous detected illegal payment addresses. So, for every new transaction, CPMR will ignore all those previously stored (detected) illegal payment addresses. This phenomenon causes reduction of fraud transaction detection time and processing becomes faster. The simulations shows that the proposed method resulted in reduced transaction processing time (TPT), fraud transaction detection time (FTDT), and improved fault transaction detection accuracy (FTDA) as compared to conventional methods.
Comparative Model of Offline Signature Verification System

Among other types of biometric recognition systems like fingerprint, voice, iris, etc., signature verification is the most frequently used biometric type. Authenticating a handwritten signature can be made easier by using the proposed Signature Verification System (SVS), which minimizes human error. The proposed system helps in determining how closely the user's new signature resembles the previous signature stored in the database. Biometric verification technologies include voice recognition, iris scanning, face recognition, and finger print scanning, among others. However, not all systems are portable, and some have very high deployment costs. This study investigates the signature verification system that can be used to achieve beneficial results in order to increase efficacy, efficiency, and portability. This is accomplished by comparing the Structural Similarity Index Measure (SSIM) and the Convolutional Neural Network (CNN).
Protecting Bitcoins Frequency Count Against Double-Spend Attacks

Bitcoin is a distributed cryptocurrency that verifies transactions using the Proof-of-Work system. It is now being accepted as payment by an increasing number of establishments, particularly quick-service restaurants and vending that must quickly satisfy orders. Unfortunately, given the correct circumstances, such as quick payments, a Bitcoin double expenditure scam may occur. Finding scalable and simple to-install defenses against such assaults is essential since consumers shouldn't be responsible for paying the price for cyberattacks. In an earlier study, some potential countermeasures were published without enough thought. In this study, they use the Shadow framework to predict the outcomes of deploying these security measures in a sizable Bitcoin P2P network. Our model shows that if an upgraded observer combines these countermeasures, a vendor may be informed of a double-spend assault in much less than 28 milliseconds on average.
Improved Multiphase PSO using Greedy Approach for Effective Population Size

There has always been a lot to learn from and discover about nature, which has served as a major source of motivation for research. A prominent branch of artificial intelligence called swarm intelligence (S1) aims to mimic the collective properties of social swarming in nature. Unquestionably, the Particle Swarm optimization algorithm is one of the most well-known SI approaches (PSO). PSO has established itself as a leader in the optimization area over the past two decades by successful application and good results in a wide range of scientific and technological fields with a wider range of difficult optimization problems. However, more investigation has found a variety of problems with the approach, including issues with convergence, diversity, and stability. PSO has undergone a wide range of modifications, expansions, and variations in many aspects of the algorithm, and as a result, the linked research has now advanced to an astounding degree. The middle of the 1990s saw the initial development of PSO. This study organizes and summaries information on the PSO algorithm using a rigorous and methodical review that considers the coverage of the paradigm, theory, hybridization, parallelization, complex optimization, and the algorithm’s numerous applications. Additionally, it highlights current advances and trends in some of the most significant algorithm implementations. It will be easy for researchers to choose the PSO variant that is currently or will be generated for a specific optimization application or challenge. Inspiring academics and researchers to do additional research on the theory and use of the algorithm in the years to come, this current analysis also sheds light on the fascinating unsolved challenges and pressing open problems affecting PSO.
A Survey on Machine Learning to Detect Creation of Fake Identities by Human vs. Bots

Online social networks are more prevalent than ever and have become deeply ingrained in people’s social lives. Through online social networks, they chat with one another, share data, organize events, and even run their own online companies. In order to steal personal information, spread destructive activities, and publish fake information, attackers and imposters have been drawn to OSNs because of their explosive growth and the vast amount of personal data they collect from their users. On the contrary, researchers have started to look into reliable strategies for identifying fake accounts and questionable activities using account attributes. However, several of the employed account variables have no impact at all or have a negative effect on the results. Furthermore, employing independent categorization algorithms does not necessarily yield positive outcomes. In order to effectively detect phone Instagram accounts, a novel algorithm called SVM-NN is proposed in this research. There were utilized four feature evaluation and data reduction procedures. The support vector machine, neural network, and the most current technique, SVMNN, were used to determine whether the chosen accounts were actual or spam. SVM-NN outperforms SVM and NN, using fewer characteristics while still being able to accurately classify about 89% of the users in our classification dataset.
Fall Detection Technique for Older Individuals based on Deep Layered Neural Networks Embedded with Transfer Learning

The vital priority of the society is to render helping hand to most fragile category of people such as older individuals, neurogenerative disease affected patients, etc. to lead their normal routine social life being physically active without lowering their quality of life and not losing their autonomy. As ageing progress, the elderly adults experience falls and those consequences lead to lesser quality of life due to the injuries occurred L This further adds to the fear of falling and reduced physical activity. The first solution of protection against the negative effects of falling is to identify fragility and high fall risk at an early stage. The development of novel techniques to identify potential fallers before any fall occurs would be the second solution of protection. The Artificial lntelligence (AD technique is used to examine and prevent falls from the signal-hised image gathered from the gyroscope or the accelerometer sensor information. The several layers of convolutional neural network are used with different dilation rates for extracting different categories of feature selection. The performance evaluation hised on the experimental results on the signal image dataset proves higher accuracy of classification rate.
User Recognition for App Rating using Machine Learning

The constant presence of fake reviews online tells us that reviews are an important part of consumer decision making, and underscores how important reviews are to local Search Engine optimization (SEO). As always in SEO, if someone tries to cut or cheat on something, they usually assume that doing it right is worth the effort. The degree to which fake reviews penetrate this reputation management space generally threatens the credibility of reviews. This study will implement a verification process that includes face recognition. Second, it uses the user’s biometrics. In this project, before users can post ratings for your app, you need to make sure they’re human and not bots. It must pass the facial recognition and fingerprint biometrics previously set up for the user. You can only publish a rating if the user who passes verification. The algorithm used in the proposed system is Open-Source Computer Vision Library (Open CV), and the proposed system uses the You Only Look Once (YOLO) methodology for face recognition.
Attention based Image Caption Generation (ABICG) using Encoder-Decoder Architecture

The image captioning is utilized to develop the explanations of the sentences describing the series of scenes captured in the image or picture forms. The practice of using image captioning is vast although it is a tedious task for the machine to learn what a human is capable of. The model must be built in a way such that when it reads the scene, it recognizes and reproduce to-the-point captions or descriptions. The generated descriptions must be semantically and syntactically accurate. Hence, availability of Artificial Intelligence (AI) and Machine Learning algorithms viz. Natural Language Processing (NLP), Deep Learning (DL) etc. makes the task easier. Although majority of the existing machine-generated captions are valid, they do not focus on the crucial parts of the images, which results in lesser clarity of the captions. In the proposed paper, anew introduction to attention mechanism called Bahdanau’s along with EncoderDecoder architecture is being used so as to reflect the image captions that are more accurate and detailed. It uses a pretrained Convolutional Neural Network (CNN) called InceptionV3 architecture to gather the features of images and then a Recurrent Neural Network (RNN) called Gated Recurrent Unit (GRU) architecture in order to develop captions. This model is trained on Flickr8k dataset and the captions generated are 10% more accurate than the present state of art.
Various Methods in Texture Analysis and Classification Techniques used in Cervical Cancer Detection

Cancer is one of the deadliest diseases which puts human life at risk. Among various types of cancer, the most common malignancy among women that results in mortality is cervical cancer. Early detection of Cervical cancer is hard to detect because it does not have any external manifestation of the disease. A Pap smear is one of the clinical procedures to find cervical cancer but it is not accurate always. Also, it is a very sensitive process and also consumes a lot of time. Several diagnostic techniques like MRI, CT, etc, can be used with various imaging methods to detect cervical cancer. This review paper aims at analyzing various images acquired by imaging tools for the detection of malignant tissues in the cervical region using image processing techniques and feature extraction techniques. The work also gives a comprehensive study on Texture analysis and methods used in the classification of textured images using AI, Machine learning, and deep learning processes.
Automatic Detection and Localization of Breast Cancer from Mammogram Imaging Modality using Modified Faster RCNN

The evolution of different imaging modalities leads to the study of the internal anatomy of the breast. It assists radiologist and physician in breast cancer detection. Breast cancer has claimed the lives of more women than any other type of cancer. With that in mind, this research proposes a strategy for detecting, classifying, and localizing breast cancer on mammogram type of imaging modality. Because Mammography of the breast muscles can detect breast cancer in its early stages and widely available. Therefore, this research work proposed a breast cancer detection framework based on Faster RCNN using mammogram images. It is help full to overcome the problem of miss classification and to save from unnecessary pain of biopsy. The proposed novel fully automatic CAD system can be used for helping the radiologist and doctor in detecting of the cancerous cells in the breast on mammography imaging modality. For being able to judge the proficiency of the suggested work. The CBIS-DDSM database for mammography is used. The result of our proposed method obtains accuracy of 94.12%, the true positive rate of 93.33%, and rate of true negatives is 94.74%, precision of 0.93, F-score of 0.93, BCR of 0.94 and Youden’s index of 0.88. The findings indicate that the proposed work provides a more effective technique for the earlier detection, classification, and localization of breast cancer. The result of qualitative and quantitative valuation show that proposed method is giving better results with respect to various performance evaluations metric such as accuracy, TPR, TNR, precision, F-score, BCR and Youden’s index.
Visual Impaired Assist Mobile App using CNN

The term “visually impaired” refers to the people who have lost their vision either partially or fully. These people have a hard time interacting with any environment and dealing with their surroundings. Hence this study develops an app and a camera module to interact with each other in order to provide information to visually impaired people about their surroundings in a way to make their life easier. The camera would be taking continuous images that would be sent to the app. A module has been developed for face recognition to notify the user about the person who is near them. It also adds it to the server when the user wants to remember the new person. The next module would help the user to identify various daily use objects for a better understanding of their current surroundings. This would help them to not waste time fidgeting with the things around them. The app would also integrate a map service that would help the user navigate to various locations. The location service helps to precisely track the current location of the user and send the data to pre-configured people during an emergency. This would help people to easily locate their location and not waste time trying to locate the user. The all the above mentioned modules would be easily controlled by the user using voice commands also.
The Role of AI in “Oral Cancer” Detection: A Systematic Review and Future Perspective

Low survival rates are a major characteristic of “Oral cancer”. Early diagnosis and precise prognosis prediction are critical for appropriate and efficient care of “Oral cancer”. A branch of AI Systems called machine learning has been praised as having the potential to revolutionize cancer care by increasing diagnostic accuracy and prognostication. Despite of this, it has thus far only had a limited impact on patient care or actual medical practice. The study’s objectives include presenting a thorough evaluation of machine learning’s diagnostic and prognostic applications in “Oral cancer” and highlighting some of the drawbacks and issues that physicians face when employing the ML and DL-based models in routine clinical practice. This work given here focuses mostly on the underlying ideas and fundamental vocabulary learning technology of DL and “Oral cancer” detection techniques. The effort then shifts to research based on ML and deep learning that is done on the diagnosis of “Oral cancer”. Finally, the work that has been given offers potential study directions for future deep learning-based research projects for “Oral cancer” diagnosis.