Miracle-dz/UniFAR-FTUs huggingface dataset:
Hybrid Classification Model for Network Traffic Classification using SMOTE and PCA

Network Traffic classification (NTC) involves identifying diverse kinds of applications or traffic data for which the received data packets are analyzed. It is an important technique in communication networks, in recent times. The process to classify the network traffic classification is executed such as to take the data for input, pre-process the data, extract the attributes, classification, and performance analysis. Classifying the network traffic is a crucial process to manage the network and rapid advances in machine learning have driven the utility of learning methods to categorize traffic across a network. This work introduces a new framework to classify the traffic in network so that the efficacy is enhanced. The proposed model is the combination of SMOTE which solve problem of class unbalancing, PCA for the feature reduction and for the classification three classifiers are merged together which are SVM, KNN and random forest. The performance of proposed model is tested in Python is executed using Anaconda to test the efficacy of the introduced framework.
Research on Intelligent Energy Management of Fuel Cell Vehicle

With the development of fuel cell technology, how to effectively reduce hydrogen consumption of fuel cell vehicles has become a technical focus and difficulty. This paper proposed an adaptive energy management algorithm which was suitable for fuel cell vehicles. First, the ECMS algorithm considering the durability of the fuel cell was established. In order to ensure the good performance of the ECMS algorithm, an equivalent factor optimization model was established, which adjusted the equivalent factor according to the changes of battery SOC and future velocity. Considering that in the actual driving environment, it was impossible to accurately obtain the upcoming driving velocity in the future. Therefore, it was hard to quickly adjust and select the equivalent factor. This article established a neural network velocity prediction model, and the predicted velocity were provided to the equivalent factor optimization model, and the adaptive energy management algorithm was accomplished. Simulation results showed that the adaptive algorithm reduced hydrogen consumption by 2.6% compared to the basic ECMS algorithm, while battery SOC was in a suitable wording window.
Application of Fuzzy Clustering Algorithm in Predictive Control of Resource Optimal Allocation

In order to solve the low utilization efficiency of aviation logistics resources, the problem of not giving full play to air transport capacity, the author proposes an algorithm for optimal allocation of aviation logistics resources based on fuzzy clustering. This algorithm classifies the aviation logistics task requirements through the fuzzy clustering PCM algorithm to improve the retrieval efficiency of logistics resource services. Match the logistics task demand with the logistics resources in the logistics service platform through feature matching, then we can get the resources and services that best meet the needs of logistics tasks, and realize the optimal allocation of aviation logistics resources. The experimental results show that: The algorithm can optimize the allocation of aviation logistics resources with high efficiency (single object processing takes 24.68 ms) and 95.27% accuracy. Conclusion: The experimental results fully verify the effectiveness and feasibility of the proposed algorithm.
Alzheimer’s disease Classification using various machine learning approaches: A Review

Alzheimer's disease (AD) is a neurodegenerative illness in which brain cells deteriorate and die. It is a chronic illness. It is an incurable condition that causes memory loss as it proceeds. This research focuses on a detailed investigation of Convolution Neural Network-based computer-aided diagnosis of Alzheimer's disease (CNN). An accurate Alzheimer's disease (AD) diagnosis is critical for patient’s treatment, especially in the early stages of the disease. Prior knowledge allows patients to take preventive treatments before irreversible brain damage starts to occur.The main objective of this study is to assess more effective and coherent machine learning strategies for Alzheimer's disease classification. The main machine learning approaches examined in this study are Support Vector Machine (SVM), Artificial Neural Networks (ANN), and Deep Learning (DL). From the different kinds of literature, it is observed that the Convolutional Neural Network surpasses other approaches to diagnosing neurological illnesses.
Compartmental Modeling and Second-Moment Analysis of State Space Systems

Compartmental models involve nonnegative state variables that exchange mass, energy, or other quantities in accordance with conservation laws. Such models are widespread in biology and economics. In this paper a connection is made between arbitrary (not necessarily nonnegative) state space systems and compartmental models. Specifically, for an arbitrary state space model with additive white noise, the nonnegative-definite second-moment matrix is characterized by a Lyapunov differential equation. Kronecker and Hadamard (Schur) matrix algebra is then used to derive an equation that characterizes the dynamics of the diagonal elements of the second-moment matrix. Since these diagonal elements are nonnegative, they can be viewed, in certain cases, as the state variables of a compartmental model. This paper examines weak coupling conditions under which the steady-state values of the diagonal elements actually satisfy a steady-state compartmental model.
Drone Automation: An Overview of Recent Progress and Research

The recent techno-trend is frequently including drones one way or the other. These have attracted increasing attention due to usage in communications, photography, agriculture, surveillance and numerous public services and many more vast ranges of applications. But still, there had been a minimal effort of including these automated drones in commercial or general usage. These applications range from greater yet simpler applications such as grocery shopping, pet care taking, and babysitting to sophisticated applications such as surveillance and security.This paper is a direct implication to answering these commercial and general requirements with the help of automated drones. We aim to propose a collaboration between a drone and machine learning so that the drones are automated to move on their own. The IoT device in the drone is loaded with a machine learning algorithm that can identify and distinguish between faces and track them and drone mechanics to make drones move accordingly. Built-in with the power of object recognition and automatic navigation, the drone can follow a distinguishable object, either as a part of surveillance or assistance. With photography and video graphy becoming an integral part of everyone’s lives, this drone can help film sports videos and professional cinematography.
Channel-Aware Self-Supervised Learning for EEG-based BCI

Electroencephalography (EEG) can record brain activity in a non-invasive way, but it is expensive and has high inter-and intra-subject variability. Recent research on EEG analysis using deep learning has shown great performance in dealing with this problem, but there is still the issue of heterogeneity. In this work, we propose a novel self-supervised learning method that can extract robust signal representations of EEG to solve the mentioned problem. To model the paradigm in which different channels exist, we devise a channel-aware encoder. In the downstream task, fine-tuning was performed by adding spatial convolution to consider channel information in the feature obtained by a single channel encoder. For the validity of our proposed framework, we conduct experiments using two public datasets with different paradigms, i.e., sleep staging classification and seizure detection. Further, we compare the proposed method to other comparable methods.
Recognition of Group Brawl Behavior in Buses Based on Deep Learning

The environment in the bus is complex, the people are dense and the obstruction is more serious, and the risk is greater when brawl occurs, which threatens seriously the safety of public transport vehicles, therefore, it is particularly important to identify and warn the brawl behavior in time. In this paper, the object detection of people inside the carriage is mainly obtained by yolov5 object detection algorithm, and then the overlapping frame part of the video is fed into the inflated three-dimensional convolutional network for video classification. Finally, the location information of the brawler in the video was identified. Taking the inside of the bus compartment as the experimental scene, 1122 abnormal behavior video samples and 911 normal behavior video samples were collected in the field, and the accuracy rate of brag identification was 91.7%, the false alarm rate was 4.9%, and the missed alarm rate was 3.3%. After testing, it is shown that this method is more suitable for the recognition of brawl behavior in the scene inside the bus.
Systematic Literature Review and Comparative Studies on Human Centred Software Development

Software development techniques has been understood and give systematical model used to plan, design, test, implement, verification, validation and control the designing processes for developing an information-based system and satisfied the end-user’s requirements and needs. Human-centred software development, is a design methodology that provides a solution-based approach to solving user’s-oriented problems and fulfill the human-centredneeds and provides framework to provides results according to end-user’s requirements. In this paper we analyze the systematic literature reviews (Ss) and mapping studies and covering numerous primary research studies on different aspects of human centred software development(HCSD) exists. Software development has been turned focus from developed the application phase to user’s-oriented application and switched all the development scenario toward the human centred development. In this paper we review the different literature views papers on software development approaches in past and provide the comparative studies between those approaches and in final we provide the objective and proposed research overview.
Research on time series based on improved LSTM

With the development of neural networks, the application of LSTM model has become the focus of relevant scholars. This paper first introduces the model of LSTM, CNN-LSTM, LSTM-ATtetion, and then carries on the practical application. Taking the CSI500 and CSI300 index from 2007 to 2022 as the research object, this paper uses the long short-term memory model (LSTM), and combines it with Attention mechanism and convolutional neural network (CNN) respectively to build a composite model for time series prediction.It is concluded that LSTM and its composite model have excellent predictive ability.
Modification to Waveguide Approach to Study HF Radio Paths

Modifications were made to the method of simulating HF signal characteristics based on the waveguide approach - method of normal modes. Within the waveguide approach, the electromagnetic field of radiation inside the Earth-ionosphere waveguide is represented as decomposition by eigenfunctions of the radial operator. The scheme of solving the radial problem and constructing the radial operator spectrum with respect the low-frequency part of the decameter range was modified. We developed the algorithms to calculate signal distance-frequency, frequency-angular and amplitude characteristics on HF radio paths based on the analysis and numerical summation a series of normal modes.
Optimal Sign language recognition employing multi-layer CNN

We generally convey our ideas, thoughts, and facts through vocal communication. However, not everyone is gifted with the ability to express oneself. verbally. The deaf and mute populations have a difficult time expressing their thoughts and ideas to others; Sign Language (SL) is their most expressive mode of communication, but the majority of the general population is illiterate in SL; as a result, the mute and deaf have difficulty communicating with the rest of the world. A device that can reliably translate SL gestures to voice and vice versa in real-time is required to overcome this communication barrier. The current solutions are not real-time, have poor recognition accuracy, and need static environmental conditions. Some systems might need additional hardware components, such as expensive sensors, which boost the cost. The current methods allowing silent people to communicate their thoughts to others may be divided into two categories: systems based on computer vision and systems based on electrical sensors, each with its own set of benefits and drawbacks. In comparison to previous state-of-the-art approaches, we offer a Convolution neural network (CNN) based SLR system with many layers that gives great accuracy of 99.89%. Overall, we believe the study will serve as road map for future research in the domain of SLR.
A modified CNN model for Brain Tumor Detection

The brain is the most essential part of the human being. Any abnormal growth of brain tissue may lead to tumors and then cancer. In time detection of brain tumors can save the life of the person affected by a brain tumor. Various research works have been developed in this field for the automatic detection of tumors considering different preprocessing steps. In this work, we have designed a convolutional neural network model which is free from data overfitting and preprocessing steps. The proposed model was trained with the BRATS dataset containing 2065 brain magnetic resonance images (MRI) and provided 98.5% testing accuracy that is competitive with the state-of-the-art models.
Physically Unclonable Function (PUF) a Lightweight Security Primitive for IoT: Scope and Challenges

Internet of Things (IoT) devices is constrained in memory, energy, and computation power. The security of IoT is often overlooked to cope with extreme resource constraints. Also, the high-end cryptographic primitives are less feasible for low-cost IoT devices due to the prohibitive resource demand. Moreover, the conventional cryptographic primitives are insufficient to ensure the desired security in an unmonitored environment. In this context, physically unclonable function (PUF), low-cost security primitive, can be adapted to ensure security from the hardware root. The distinctive features of PUF like tamper evidence property, unclonable, and on-the-fly key generation make it promising security primitive for IoT. With the ever-growing domain of IoT, PUF is simultaneously being adapted in different emerging applications. The PUF-enabled IoT device can facilitate low-cost, unique authentication and prevent attacks like tampering, and cloning. However, increasing computation power is making PUFs susceptible to model-building attacks. Also, the unmonitored nature of IoT devices increases the possibility of attacks on PUF when adapted to IoT. Different techniques are introduced in the literature to resist attacks on PUF considering IoT. However, these techniques need to be lightweight to support the constraints of IoT as well as should be able to prevent the attacks specific to IoT. In this work, we have discussed different PUF designs, their applications, limitations, and scope in the context of IoT. Also, we have reviewed recent PUF-based security protocols, their limitations, and their challenges.
An Unsupervised Convolutional Adversarial Anomaly Detection Model for IoT Data Infrastructure

Anomaly detection is critical to ensure the IoT (Internet of Things) data infrastructures' Quality of Service. However, due to the complexity of incon-spicuous(indistinct) anomalies, high dynamicity, and lack of anomaly labels in the operational IoT systems and cloud infrastructures, multivariate time series anomaly detection becomes more difficult. Existing approaches are rarely effective in meeting these challenges. In this paper, We propose a novel convolutional adversarial model based on CNN adversarial training with POT (Peak Over Threshold) dynamic threshold selection (CAT-POT) for multivariate time series anomaly detection in the IoT data. We build on the original autoencoder, replacing the fundamental linear changes with a convolutional neural network and adding a convolutional decoder to make an adversarial training architecture. The ability of the model to identify “slight” anomalies is enhanced through adversarial training, combined with POT dynamically selected thresholds to detect anomalies. We conduct ex-periments on five subsets of various open datasets, and the overall performance of CAT-POT is better than that of the baseline method. The average F1 value of CAT-POT on all datasets is 0.889, which is 5.1% higher than the suboptimal model, with the best improvement reaching 21.5%.
Investigation of Stealing Passwords Stored in Human Brain Using Hemodynamic Responses Measured by fNIRS

Passwords are essential to enhance security in the information system fields, but many vulnerabilities found that passwords can be stolen while being entered. Attackers are especially interested in bank account passwords and personal identification numbers (PINs). Thus, we aim to investigate the possibility of identifying the digit passwords stored in the brain from a neuroscientific perspective. To achieve this goal, we observed hemodynamic responses while viewing 6-digit random numbers (Task 1) and a preset virtual 6-digit password (Task 2), using functional near-infrared spectroscopy (fNIRS), the latest neuroimaging technique. As a result, we found the signal differences between Task 1 and Task 2, indicating that Task 2 has relatively low brain activity compared to Task 1. We found also a gender difference in brain activity, which requires gender-specific security measures. Therefore, our results suggest that attackers or third-party groups can identify the 6digit passwords stored within the brain. We provide the possibility of neuro-crimes using fNIRS for the first time.
A Comparative Statistical Analysis Between ML Algorithms & DNN Techniques Using MNIST Dataset

Handwritten digit recognition has been around since a very long time and it has always been a topic of attention among the students and researchers. This job of recognizing handwritten content has great importance as it helps in recognizing various type of important information which are filled by hand but are not clearly understood. While solving this problem, the most common challenge is that the digits written by hands are of different size, orientation or thickness. Inspite of all these challenges, various Machine Learning Algorithms & Deep Neural Network Techniques are trying harder to achieve higher accuracy. In this report, we have tried to compare the accuracies of most common and popular ML Algorithms which includes SVM (Support Vector Machine), Random Forest Classifier (RFC) & KNN (K-Nearest Neighbour) with DNN Techniques (Deep Neural Network Technique) also termed as CNN (Convolutional Neural Network) using Keras. We have applied these algorithms and techniques on MNIST dataset. With the help of the results obtained, we have plotted some graphs to show differences between ML (Machine Learning Algorithms) & DNN (Deep Neural Network Techniques). In this research work, we got the accuracy of approx 97.86% using SVM, 97.10% using KNN, 97.05% using RFC while on the other hand, by using CNN, we got 99.98% accuracy on validation image data. Also, we were able to achieve accuracy approx , 96.85% using RFC, 97.73% using SVM, 96.80% using KNN while on the other hand, by using CNN, we got 98.72% accuracy on test image data.
Monitoring of traffic using advanced Digital differential based Neural network

Traffic analysis true video monitoring is quite a complex job. The reason for the same include different appearances, light changes, and variation in speeds. This paper proposed an efficient traffic monitoring system, which can detect the vehicle flow on the roads in real time. This system has three made components including evaluating vehicle density that is based on background subtraction procedures. the second component evaluate the speed of the traffic flow using optical flow mechanism first up the third component includes evaluating the number of vehicles using improved differential approach. From the fundamental information extracted, our system can predict events such as high traffic zones, vehicles over speeding and many more. The simulation is conducted by using the traffic data set derived from Kaggle.
An Optimized Transaction Processing Scheme for Highly Contented E-commerce Workloads Optimized Scheme for Contended Workloads

High contention frequently explodes in E-commerce scenario when promotions are held. However, modern multi-core main-memory databases cannot achieve ideal performance under high contention. Transactions contending for the same resources must be executed serially in traditional architecture to guarantee correctness, which severely chokes database management systems. In this paper, we propose to optimize the transaction processing scheme for highly contended E-commerce workloads. First, we analyze the characteristics of these workloads in detail. Second, we design to filter ineffective operations at IO layer instead of sending them to executing layer, considering the limited number of items involved in the promotion. Third, we make out a homogeneous operation merging scheme to share database execution resources, e.g., locks, and improve parallelization. We implement a prototype, Filmer, to demonstrate our idea. Filmer launches filtering and merging for contended transactions to make full use of system resources and improve parallelization. Extensive experiments show that filtering and merging improve the throughput by up to 1.95x and 2.55x respectively.
Comparative Analysis of Compound TCP with Various End-to-End Congestion Control Mechanisms

Congestion control is a fundamental mechanism offered by the Transmission Control Protocol (TCP) to make sure stable data flow and efficient bandwidth allocations in the network. It mainly focuses on the adjustment of the incoming sending rate with respect to the receiving rate. The reliable TCP helps to manage the transmission rate by avoiding the state of network congestion. In recent years, different variants of TCP congestion control protocols have been proposed to enhance TCP reliability and performance under different network scenarios. Moreover, it is associated with several types of network states such as latency, throughput, and packet loss. In this paper, we have performed a comparative study on the performance analysis of various TCP congestion control protocols on NS2 simulator. The results show that CUBIC gives the highest average throughput, Vegas gives the lowest packet loss ratio, and Veno gives the lowest average end-to-end delay among other TCP congestion control techniques that are considered in this paper.