Reviewerly/exHarmony huggingface dataset:
A Machine Learning Method for Predictive Detection of Depression in Men with Schizophrenia

The first symptoms of schizophrenia appear in adolescence, but can also occur in people over the age of 40. Only a specialist can make a diagnosis, but everyone can see the symptoms of the disease. If a person's schizophrenia is genetic or chronic, it can progress. The first symptoms are the appearance of delusional ideas, intrusive thoughts. The patient begins to try to protect himself from the person by opening shutters, doors, choosing food, checking for poison. Often, men have the impression that someone is leading them, sending those ciphers. In this paper, a machine learning method has proposed for predictive detection of depression in men with schizophrenia. There are such signs that he often starts writing complaints to any authorities. Another symptom of schizophrenia in men is a disorder in which he begins to rise in mood, or on the contrary, he goes into depression. Symptoms of affective disorders include feelings of depression, hopelessness, fatigue, later anxiety, and lethargy.
Cancer Detection in Highly Dense Breasts using Coherently Focused Time versa Microwave Imaging and Using Warm-Boot Random Forest Classifier

Breast cancer is one of the most common diseases among women all over the world. Many research can predict critical indicators, but these analyzes are also crucial using basic statistical methods. A prognostic model framework is used under the current system to call and predict MP4Ei for invasive disease-free survival breast cancer patients (IDFS). We need to use more effective, accurate, and scientific methods to guide the treatment method. But on the other hand, medical and omics with bed facilities will be introduced to improve the data model. In the proposed work, the speculation model was constructed with a Warm Boot - Random Forest to determine the significant prognostic factors for breast cancer incidence. The next set of data is based on the implementation of Warner Bros-Random Forest Breast Cancer Patients' Receptor Level Clusters Modeling Advanced Immunoassay Identification. Finally, the results were verified using tree construction and survival analysis. The proposed result obtained from high accuracy = 79.8% in low WB-random forest accuracy = 82.7% in high. This method's essential factors are cancer status, tumor size, discharge armpit lymph nodes, and positive lymph node as first stage treatment and diagnostic approach.
Improving The Hiding Capacity of Image Steganography with Stego-Analysis

Communicating online without fearing third-party interventions is becoming a challenge in the modern world. Especially the sectors like the military, and government organizations or private companies sharing sensitive information. They invest a lot of effort and cost into obtaining the advancement of safe communication techniques. Image processing encryption techniques using various algorithms promote security over communication channels and using different analysis methods make the tool stand out in providing security to the information. In today's world, there are various steganographic mechanisms that convert the secret message into stego medium and send it across various communication channels. Using algorithms like Blind Hide promotes the security of the message along with using multiple analysis methods that will further improve the tool in giving out information of encoded accuracy, size of stego of the secret message. The aim is to generate a tool that will give out a benchmark value of how precisely the message is stored in the cover file. Using Stego and bulk analysis the information about the presence of the stego medium in the message can be known to the user. All these analysis methods make the tool more enhanced and secure.
Automatic Identification of Epileptic Seizure Using Kelm Optimized By Grey Wolf Algorithm

Epilepsy is the common neurological disorder where nerve cell activity is disturbed which results in causing seizures. Automatic epileptic seizure detection is an essential requirement for clinical diagnosis of epilepsy. Classifying facts is the usual task in machine learning. Epileptic seizure identification using Support Vector Machine (SVM) have few limitations like its outcomes lacks transparency. Extreme Learning machine is computationally uncomplicated and yields definite results also because of its quick learning pace, (ELM) is utilised to pick input data. The motivation of this research is to boost the training rate and correctness of the ELM. Kernel Extreme Learning Machine (KELM) is chosen for refining generalization capacity and to improve the classification accuracy nature inspired swarm intelligence Grey Wolf Algorithm (GWO) is adapted. The grey wolf algorithm optimizes the KELM parameters. The GWO-KELM classifier is laid on Epileptic Seizure Data Set from UCI and the experimental results such as learning accuracy, learning error and classification accuracy are compared with traditional classifiers. By performing analysis with the proposed GWO- KELM classifier faster learning speed, accuracy with high precision and low error rate is achieved and proposed classifier outperforms other classifiers in identification of epileptic seizures.
An Innovation Development of Document Management and Security Model for Commercial Database Handling Systems

A document-object can contain multiple representations (forms) of the document, and the system automatically synchronizes the contents of all representations when one of them changes. With this feature, the computer allows the client to view and annotate PDF documents in Adobe Acrobat Reader and Acrobat Exchange (the Acrobat Exchange document is included with EDMS). A document EDMS operates according to a three-tier client-application server-document database model. The Doc Base document base can store the contents of documents in both a network file system and a DBMS. Oracle, Microsoft SQL Server, Sybase, or Informix: Doc base stores document attributes under the control of one of the base SQL servers. It should be noted that document cards are optional in Documentum EDMS, but are generally used for convenience. Also, depending on the user's rights, a single document may contain multiple cards that show only the attributes required and allowed by the user.
Combining K-Means and Gaussian Mixture Model for better accuracy in prediction of Ductal Carcinoma in Situ (DCIS)- Breast Cancer

The most frequent malignancy in women now a days is breast cancer particularly in India. To predict DCIS( Ductal Carcinoma in Situ) various trained model of supervised learning algorithms like k-Nearest neighbor, Decision Tree classifiers, Random forest classifiers are used by various researchers. In unsupervised learning model K-means algorithm is also used to classify the clinical characteristics, mammographic findings, and images features. In this paper K-means (unsupervised learning) idea has been extended to combine the Gaussian Mixture model (GMM) for better accuracy. The EM (Expectation Maximization) based GMM is combined with k-means for flexibility in cluster shape by density estimator to overcome the problems of k-means algorithm. The simulated results show the competence of the proposed model to predict for the early diagnosis of DCIS (the early stage of the breast cancer). Scikit-learn, Pandas, Matplotlib and Numpy modules of Python have been used for breast cancer datasets, simulation and results.
The Task based Human Resource Control and Management Model using AI based Deep Learning Model

Human resource, material and money together make a business. Human resource plays a huge role in making a business rise or fall. Good people make good companies. Large companies have large portfolios for monitoring and improving human resources. Today, HR officers hold the most important responsibilities in their respective organizations. The importance of human resources in today's world of intellectual work is increasing day by day. In this paper, a task based human resource control and management model has proposed using AI based deep learning model. Human resource management is a task that is required in all organizations that can perform all kinds of tasks. There are jobs for HR department in all the organizations irrespective of whether they are government organizations or private organizations. Therefore, it is very important for you, the youth, to acquire the education to work in the HR sector and develop the skills for that. That is, when every employee in the organization develops, the entire organization moves towards massive development. Although every organization wants to achieve this level, this human resource development has to face some challenges.
Classification of Breast Tumor Using Radon Cumulative Distribution Transform Nearest Subspace Classifier

Early diagnosis of breast cancer can significantly increase the chances of appropriate treatment and survival rate. The methods doctors use to detect cancer are tedious, expensive, time-consuming, and above all, depend on the expertise of the physicians. Moreover, analysis of the image sometimes leads to false-negative reports and disagreement among the doctors. In order to improve diagnostic accuracy and act as a source of a second opinion, computer-aided diagnosis systems have shown tremendous potential. But, most of them are based on a combination of different machine learning and deep learning methods, which require not only a large number of training data but also a substantial amount of time to train. In this study, we employed a recently introduced image classification method known as Radon Cumulative Distribution Transform (R-CDT) Nearest Subspace (NS) classifier to classify breast tumors from histopathology and ultrasound images. It is a supervised learning method that is non-iterative and simple to implement in classification problems. Three different datasets are used to evaluate the performance of this classifier. The accuracies for the histopathology datasets are 93.5% and 90.9%, and for the ultrasound dataset, the accuracy is 90%. We have also used two pre-trained deep neural networks called AlexNet and GoogLeNet to classify the same datasets and compare their performance with that of our method. The comparison between these neural networks and our method shows that the R-CDT NS classifier is more computationally efficient and data-efficient for the classification of breast cancer because it does not require additional GPU and can achieve competitive test accuracy even for a small set of labeled data.
Comparative Performance Analysis of Feature Selection for Mortality Prediction in ICU with Explainable Artificial Intelligence

The mortality prediction model in the Intensive Care Unit (ICU) can be a great tool for assisting physicians in decision-making for the optimal allocation of ICU according to the patient's health conditions. Traditional scoring-based systems for mortality prediction don't provide good predictive performance in the case of a large dataset. Moreover, machine learning models can also provide poor performance for the lack of proper feature selection. A comparison of the performance of machine learning models with and without feature selection was explored in this study. Principal Component Analysis (PCA) was used to choose features for this investigation. For the classification job, the most widely used and diversified classifiers from the literature were used, including Logistic Regression(LR), Decision Tree (DT), K Nearest Neighbours (KNN), and Support Vector Machine (SVM). The Medical Information Mart for Intensive Care III (MIMIC-III) dataset was used to collect data on heart failure patients. On the MIMIC-III dataset, it was discovered that feature selection significantly improved the performance of the described machine learning models. Without feature selection, the accuracy of LR, DT, KNN, and SVM models was 86.66%, 80.12%, 85.13%, and 86.49%, respectively, however with PCA, the accuracy was improved to 88.0%, 80.46%, 86.83%, and 87.34%, respectively with only 5 principal components. Finally, the model's decision-making process was analyzed with explainable artificial intelligence using Local Interpretable Model-agnostic Explanations (LIME). This analysis can help to understand the feature's contribution to the model's prediction process. It was also observed that the features involved in the prediction process were mostly common with the first 15 features found in feature importance hierarchy.
A Blockchain-based Technique to Prevent Grade Tampering: A University Perspective

Education plays a significant role in the advancement of a country. The quality of education that a university provides is crucial to this progress. Almost every university stores the assessed grades on the local server, making them vulnerable to different security attacks e.g. DDoS, SQL injections, cross-site scripting, etc. Any alteration to the assessed grades highly affects the students' personal, academic, and professional lives. This study proposes a blockchain-based secure approach for storing, managing and recovering students' grades by employing both a local server and a blockchain server. The immutability feature of blockchain ensures grade integrity and facilitates the implementation of a grade recovery system. Grades are encrypted before storing on the local and blockchain server to protect privacy. The blockchain server returns the index number of the corresponding block to the user for further use. Finally, the experimental analyses demonstrate the system's effectiveness over state-of-the-art works.
Efficient Hardware and Software Co-design for EEG Signal Classification based on Extreme Learning Machine

ElectroEncephaloGram (EEG) is associated with multiple functions, including communications with neurons, organic monitoring, and interactions with external stimuli. By decoding EEG signals, certain human activities such as sleeping, brain diseases, motor imagery, movement of limbs, and others can be observed and controlled through the brain-computer interface (BCI). Therefore, it is vital to efficiently process EEG signals with a robust and accurate system to build BCI systems with powerful applications. However, as a weak biosignal, EEG demands a fast-reaction system signal processing with high accuracy and sensitivity. In this work, a hardware/software co-design network based on Extreme Learning Machine (ELM) is introduced for the classification of certain actions, motor imagery of the human brain. This system is based on an optimized Hierarchical Extreme Learning Machine (H-ELM) on the software layer. The proposed method has advantages over previous designs with an accuracy of 90.3%. It also improves the training speed by around 25X compared to conventional methods. The software model is also translated into efficient FPGA hardware design to maintain high computation efficiency and reduce power consumption for biomedical applications.
Estimation of Transmission Bandwidth for VoIP Signals over IP Packet Transmission Network using Capacity Computing Method

The traditional telephone network is the required transmission bandwidth of 64kbit/s. The so-called VoIP is an IP packet transmission network as a transmission platform, simulated voice signal compression, packaging and continuous special processing, which can use the connectionless UDP protocol for transmission. Sending voice signals over an IP network requires several components and functions. The simplest form of a network consists of two or more devices with VoIP capabilities connected through an IP network. In this paper, a smart estimation of transmission bandwidth for VoIP signals over IP packet transmission network using capacity computing method. A speech network establishes a physical connection between communication endpoints (a line) and transmits encoded signals between the endpoints. Unlike circuit switching networks, IP networks do not create connections. It requires data to be placed into variable long data reports or packets, and then address and control information must be sent for each datagram and sent over the network to the destination.
Sentiment Polarity Detection Using Machine Learning and Deep Learning

As e-commerce has grown in recent years, so online shopping has increased with the number of product reviews posted online. The consumer's recommendations or complaints influence significantly customers and their decision to purchase. Sentiment polarity analysis is the interpretation and classification of text-based data. The main goal of our work is to categorize each customer's review into a class that represents its quality (positive or negative). Our sentiment polarity detection consists of the following steps: preprocessing, feature extraction, training, classification and generalization. First, the reviews were transformed into vector representation using different techniques of Tf-Idf and Tokenizer. Then, we trained with a machine learning model of SVM Linear, RBF, Sigmoid kernel and a deep learning model LSTM. After that, we evaluated the models using accuracy, f1-score, precision, recall. Our LSTM model predicts an accuracy of 86% for Amazon-based customer reviews and an accuracy of 85% for Yelp customer reviews.
Design of Analysis Platform for College Students' Physical Learning Effect Based on Data Mining Algorithm

The design of College Students' physics learning effect analysis platform based on data mining algorithm is the process of designing a computer program, which will automatically analyze the results from large databases to find patterns. The purpose of this study is to develop an analytical framework for assessing and improving the effectiveness of sports events, called dapt. Dapt aims to develop an analytical framework to provide information about students' learning effects by analyzing their performance in physical education classes. This framework should have three components: (1) data mining algorithm, (2) evaluation method, and (3) improvement. In this paper, the design of the platform for analyzing the effect of College Students' Physical Education Learning Based on data mining algorithm and the results obtained by the data mining algorithm are of positive help to teachers in diagnosing teaching problems and constructing characteristic teaching curriculum system.
Detection of Colon Cancer Using Inception V3 and Ensembled CNN Model

Colon cancer is one of the most prevalent types of cancer. Early diagnosis of colon cancer can lead to an increased chance of successful treatment with less cost. To speed up this process deep learning can provide very useful and effective approaches. In this thesis work, two types of models were developed to classify colon cells from image data - one is the transfer learning model where a deep network Inception V3 is used as the pre-trained model and the other one is an Ensembled model which combines predictions of three simple sequential CNN models. To develop these models, 10k images were used from the LC25000 dataset and a very small Warwick-QU dataset having only 165 images was used to provide new data for retraining and testing purposes. Both models achieved a high result for the first dataset with 99.4% and 99.95% accuracy respectively, where Inception V3 showed 94.545% accuracy on new data from Warwick-QU after retraining and Ensembled model showed 78.182% accuracy. This approach can be used in research in the field of early and effective detection of colon cancer with a larger amount of varying images and more preprocessing methods to reduce overfitting and to make the model perform well in various types of images.
Sexual Harassment Detection using Machine Learning and Deep Learning Techniques for Bangla Text

Harassment is a kind of act that annoys or upsets someone. Harassment can be classified into different categories. Sexual harassment is one of them. Sexual harassment is a type of harassment that involves the use of implicit or explicit sexual overtones, including the inappropriate and unwelcome promises of rewards in exchange for sexual favors. At present time, the technology has become more advance and spread all over the place. That gave the toxic people a huge opportunity to spread toxicity in online platforms. Because of the increasing amount Bangla text in different social media platforms, we also need to filter such kinds of offensive Bangla texts. The objective of this research is to detect sexual harassment from Bangla text and classify them by using machine learning and deep learning algorithms as well as prevents them. In the experiment, we combined TF-IDF with different machine learning algorithms like Naive Bayes, Decision Tree, Random Forest, AdaBoost, SGD, Logistic Regression, KNN, SVM and got accuracy of 74.9%, 75.6%, 70.0%, 70.1%, 75.2%, 75.7%, 65.2%, 76.5% respectively. Deep learning algorithms like CNN, LSTM, hybrid CNN-LSTM were also used and achieved accuracy of 89% for all of them which is comparatively better than machine learning techniques.
Securing E-Passport Management Using Private-Permissioned Blockchain and IPFS

To travel globally, a passport is the only official travel document that authenticates the holder's identity and citizenship which is provided by the government. Digital passports provide adamant verification through documentation that tangibly marks the passport owner using biometric techniques because it holds an electronic chip into the passport, also known as an e-passport. The biometric method uses a combination of RFID and fingerprint scanning technology. Notwithstanding, RFID chips have numerous security threats as the information confidentiality can easily be hacked and it is not entirely hassle-free. To get rid of these challenges as a potential remedy, in this paper we propose Hyperledger to secure e-passport management, which in contrast to the public blockchain, is an open-source private blockchain with strong security. When compared to the standard blockchain development framework, the legitimacy of transactions in a private blockchain powered by Hyperledger is substantially distinct, which is enable more effective and quicker transactions while maintaining essential security features. The InterPlanetary File System (IPFS), which has diverse security measures like access control features, non-modifiable data, and immutable history records, and this private ledger have both been used to safeguard private passport information and biometric data.
The Smart Analysis of Hydropower Energy Measurement in Power Plants using Industrial Machine Learning Model

A small hole is made before fitting and to help identify it. A tool used for drilling holes in concrete walls, brick walls etc. It is named 8SWG 6SWG according to the thickness of the chisel. Its handle is made of iron and after plugging this nut into a small hole on one side of the tool, hit the other end with a hammer. This tool is used to drill holes in the wall to carry the wires from one side to the other. On one side the teeth are cut and sharp and saw-like. The other end should be hammered. While beating, the pipe should be rotated with the hands holding it. Wooden boxes, frames, round blocks, (Round Block) are used to arrange them, cut them as required. Can be used to cut pipes, metal sheets etc. Its output is made of iron. The handle is made of wood. The length of the beam can be increased or decreased to adjust the length of the iron sword. It is so called because the lye part is ball-like. Its handle is made of wood and the other part is made of iron. This hammer is used to drive nails and bend wire pipes. Its other end is flat. It is usually named by its weight. Used in various weights from 0.5 kg. It is a tool with a sharp tip. Its handle is made of wood.