Anup Kumar Barman

The growing use of computer resources in modern society makes it extremely vulnerable to several cyber-attacks, including unauthorized access to equipment and computer systems’ manipulation or utter breakdown. Malicious attacks in the form of malware cause significant harm to systems with limited resources. Hence, detecting these attacks and promptly implementing a computationally efficient technique is imperative. Utilizing a machine learning (ML) based model is a superior option for promptly identifying malware. This study develops fourteen machine learning models using a five-fold cross-validation technique on the dataset it obtained for research. We compute the execution time and memory used for each of the fourteen ML model developments, considering both all features and the reduced features after applying the data preprocessing methods. We utilized the Extra Tree classifier (ETC) to identify the top ten significant contributing features based on Gini impurity scores, which led to improved accuracy and reduced processing time. After that, we compared the experimental results and found that the Random Forest (RF) classification model on the reduced features set had a prediction accuracy of 99.39% and ROC-AUC values of 0.99. The ETC model prediction yields comparable results, confirming the viability of the suggested model. The proposed model is very resilient, exhibiting an extremely small standard deviation. It is also highly responsive, with reduced execution time and memory utilization.

In today’s data-driven world, the convergence of emerging technologies and innovative approaches has revolutionized the landscape of data analytics, paving the way for transformative solutions in decentralized data sharing, real-time communication, and demand forecasting. This paper explores the dynamic synergy of Distributed Ledger Technologies, particularly IOTA Tangle and Directed Acyclic Graph, with the Message Queuing Telemetry Transport protocol and advanced data analytics techniques for predictive insights. The application of IOTA Tangle and DAG in decentralized data sharing is the subject of our investigation. These technologies provide safe, scalable, and impenetrable platforms that support cross-industry collaboration and data analytics. IOTA Tangle and DAG additionally offer tamper-resistant transactions, preserving data credibility and integrity while promoting peer-to-peer data sharing that boosts effectiveness and ownership. The second area of our investigation focuses on applying the MQTT protocol for real-time communication in cross-industry collaborative supply chains. MQTT enables quick decision-making based on real-time data thanks to features like reduced latency, asynchronous communication, and bidirectional capability. Additionally, the strong security characteristics of MQTT improve data integrity and confidentiality, encouraging cooperation and supply chain effectiveness. Our study includes a comparison of different time series models, showing how Deep Matrix Factorization can significantly improve demand forecasting and prediction in collaborative supply chains across different industries. In this paper, we examine the intriguing interaction between IOTA Tangle, DAG, MQTT, and advanced data analytics techniques, ushering in a period of unmatched insights and efficacy in data analytics across various industries.

The Internet of Things (IoT) is vital as it offers extensive applicability in various fields, including healthcare. In the context of the risk level during pregnancy, to monitor and predict abnormalities, IoT devices provide a means to collect real-time health data, enabling continuous monitoring and analysis in the Internet of Medical Things (IoMT) environments. By integrating IoT devices into the system, crucial signs such as Heart Rate (HR), Systolic and Diastolic Blood Pressure (BP), Fetal Movements (FM), and Temperature (T) can be tracked remotely and non-invasively. This allows for the timely detection of abnormalities or potential risk factors during pregnancy, empowering healthcare professionals to intervene proactively and provide personalized care. This research focuses on developing a system for observing and predicting the maternal risk level in the IoT environment, mainly in remote areas. The goal is to improve maternal health and reduce maternal and child mortality rates, a significant decline according to United Nations targets for 2030. The research utilizes analytical tools and Machine Learning (ML) algorithms to analyze health data and risk factors associated with pregnancy. The acquired dataset contains various risk factors categorized and classified based on intensity. After comparing different ML models’ experimental results, Exploratory Data Analysis (EDA) approaches to determine the most effective risk factors. The fine-tuned Random Forest Classifier (RF) achieves the highest accuracy of 93.14%. An Android-based application has also been developed to deploy the prediction model to determine risk levels based on the different parameters.

The safeguarding of digitized data against unwanted access and modification has become an issue of utmost importance as a direct result of the rapid development of network technology and internet applications. In response to this challenge, numerous secret image sharing (SIS) schemes have been developed. SIS is a method for protecting sensitive digital images from unauthorized access and alteration. The secret image is fragmented into a large number of arbitrary shares, each of which is designed to prevent the disclosure of any information to the trespassers. In this paper, we present a comprehensive survey on SIS schemes along with their pros and cons. We review various existing verifiable secret image sharing (VSIS) schemes that are immune to different types of cheating. We have identified various aspects of developing secure and efficient SIS schemes. In addition to that, a comparison and contrast of several SIS methodologies based on various properties is included in this survey work.We also highlight some of the applications based on SIS. Finally, we present open challenges and future directions in the field of SIS.

Parkinson's disease (PD) is a neurological disorder which is progressive in nature. Although there is no cure to this disease, symptomatic treatments are available. These treatments can slow the progressive development of the symptoms. Medications can treat some of the symptoms of the PD up to a great extent that in turn may help the patients to live a normal life. Besides these medications, the patients can also be provided with various therapies based on the types of their symptoms. But for providing any treatment, detection of its symptoms at an early stage is very important. This can reduce its future complexities considerably. Early diagnosis along with proper medications may treat the symptoms of PD significantly. This motivates to propose a new and effective methodology for detection and analysis of PD. In this work, an approach has been proposed for identification of PD patients by using Electroencephalogram (EEG) signals. Here, the EEG signals of normal persons and PD patients are processed in three stages. First, the raw EEG signals are pre-processed for removal of noises and artefacts present. Out of various techniques, Wavelet transform is used for this purpose. In the MATLAB environment, de-noising can be executed by using the in-built functions. Performances of the de-noising techniques are examined with the performance parameters namely Root Mean Square Error (RMSE) as well as Signal to Noise Ratio (SNR). In the second stage, statistical features are extracted from the pre-processed EEG signals. In this work, five statistical features are considered for performing the classification. In the final stage, the extracted features are classified using Artificial Neural Network (ANN) and Support Vector Machine (SVM) techniques. ANN is an efficient classifier that predicts the human brain's working manners. SVM on the other hand has been proven as one of the most prevailing classification algorithms that gives highly accurate and robust results. This is a novel approach of analyzing the performances of the classification techniques by evaluating the best performing feature. In both the classifiers accuracy, precision, sensitivity and specificity are calculated from the confusion matrix evaluated from the values of the statistical features. In ANN, results using six different training algorithms at different hidden layers are calculated and compared. This proves the training algorithm Levenberg-Marquardt back-propagation with hidden layer 20 as the best combination for performing the classification. From the results it is seen that both ANN and SVM classifiers provide significant classification accuracies of 94.7% and 96.5% respectively. Amongst the five considered features, Mean performs the best in terms of classification accuracy.

Diabetic Retinopathy (DR) is a prevalent eye condition that occurs as a frequent complication among individuals with diabetes, particularly those who have been living with the disease for an extended period of time. This study uses fundus images to diagnose DR at five stages from early to late with No DR, Mild, Moderate, Severe, and Proliferative DR, commonly known as Stage 0 to Stage 4, respectively. This will aid in the timely treatment of diabetic patients preventing them from developing DR as early as possible. We used two most popular open-source datasets, the DR Detection database, namely APTOS 2019 and EyePACS, and combined them to create a larger dataset to trade off the data-centric obstacle and shortfall for any Deep Learning-based prediction models. Data augmentation and preprocessing techniques are applied to the images before feeding them to the proposed model to get a more accurate and efficient one. In the modern age oriented to Artificial Intelligence (AI), it is necessary to thoroughly analyze the identification of DR based on the existing Deep Learning (DL) models. After learning about the limitations of existing models, we have fine-tuned the ResNet50, DenseNet201 and InceptionV3 to enhance the model performance of the detection and categorization of DR. We have since proposed three Deep Convolutional Neural Networks (DCNN) models with better outcome based on accuracy than the existing state-of-the-art (SOTA) models. The fine-tuned DenseNet201 model, among the other two, performed significantly better with a validation accuracy of 90.04% and a negligible amount of loss, irrespective of each class, under the best configurable test conditions.