Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on Railway Connection Modes Behavior and Users Profiles Based on Passenger Clustering

Due to the higher requirements of the intelligent and personalized service level in the entire travel process, Maas has become a key research area for scholars. As an important part of the full trip chain, the connection stage plays an significant role in the satisfaction of railway passengers. To provide a service that better cater to individual passengers, this paper proposes an intelligent approach for identifying profiles of railway passengers’ connection preference. First, passengers were clustered into three segments by improved adaptive DBSCAN algorithm based on their personal attributes which captured in the questionnaire. Then, multivariate Logistic regression model is used to fit the parameter values of the passenger group’s selection behavior of connection mode. According to the parameter, user profiles of the three segments are identified and verified: (i) economy-preferred passengers; (ii) convenience-preferred passengers; (iii) time-preferred passengers. This method of profile portraying is able to formulate personalized and differentiated marketing strategies by matching more accurate and efficient connection travel plans for specific railway passengers groups.
An Efficient Technique of Predicting Toxicity on Music Lyrics Machine Learning

It is widely accepted that music is humanity's universal language since it can spread happiness and excitement throughout people's lives. Music is a form of art that is highly regarded worldwide. There are many ways that music lyrics affect our daily lives. In the music industry, it is crucial to prevent the reproduction of songs whose lyrics are toxic or unsuitable for children. Our mood might be impacted by listening to particularly toxic or non-toxic music. The listener's experience might be enhanced if the recommendation method eliminates toxicity. In this study, we use machine learning (ML) algorithms to classify lyrics from various musical genres and performers as toxic or non-toxic. Utilizing the Detoxify model, the toxicity score was generated and labelled the songs as toxic and non-toxic based on the scores. The study demonstrates that the configuration using the lyric data set along with TF-IDF vectorization and Ensemble of Logistic Regression, Support Vector Machine and Decision Tree as an algorithm surpasses all other designs with 94% accuracy. This classification will help the authority and policymakers of music industries to categorize the song based on the label and mention in the song description which is not appropriate for the children and set guidelines to prevent toxicity via songs.
Prediction of Road Accidents in the Different States of India using Machine Learning Algorithms

Analysis of road accidents acting a significant task in the road transport system. This article, predicting road accidents based on four attributes, that is. , collision type, road type, location, and weather. A machine learning model with a Random Forest Regressor and Decision Tree Regressor is developed and working to predict collisions based on collision records that have taken place in the different states of India. The hit and run, head-on collision, hit pedestrian, fog, cloudy, rainy, single lane, two-lane, four-lane, school, pedestrian crossing, market, and other parameters considered for analyzing and visualization of accidents in different states of India. The correlation between mortal rate and other features including different road conditions, conditions of weather, location, nature of collision and time of occurrence, kinds of motor vehicles involved in accidents were analyzed. The Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE) metrics are considered for predicting road accidents based on a greater number of parameters. The accuracy of the Random Forest Regressor model and Decision Tree Regressor model based on head-on collision is 94.2% and 84.8%, 92.8% and 96.9% for fog weather conditions, 80% and 81.5% based on single-lane accidents, and 90.3% and 86.1% based on pedestrian crossing attribute. The outcomes of the relative analysis proved that the Random Forest Regressor (RFR) model does better than the Decision Tree Regressor (DTR) model.
Prevalent Cyber Attacks and Defense

The world is increasingly adapting towards the use of internet which has made it easy for the mankind to digitally access the services provided by all the sectors such as retail, health, education etc. and thereby making man's life on this earth easier. But all good things also come with disadvantages, the main threat to these digital services is Cyber-attacks as the total value of the digital transactions in the year 21–22 is estimated to a value of 3,002 lakh crores which makes it a vulnerable region for the attackers to focus. The main type of cybercrimes which these attackers commit is phishing, malware, OTP frauds and Fake UPI links. The number of such crimes exceed 80,000 every month and value around 200 crores according to the government sources. With such increasing instances of Cyber-attacks, it is believed that the Cyberspace could be the epicenter of the future warfare.
Novel prediction system for stock market through Hybrid Cat Boost Correlation Algorithm (HCBC)

Due to Rapid growth in Technology financial market offers and excellent platform for investors and traders to connect with Global devices through internet. Over the year people are engaged with stock trading in online. People can inverse and grow the wealth from online applications available in Smartphone. Online trading has changed depends upon the individual purchase and pattern of selling stock. Technology such as artificial intelligence enable the growth of applications to watch advance, please advance options covered with new opportunities enable the system to predict the market and helpful for the inverters to make a move. Market prediction is one of the demanding requirements of research. The proposed approach is developed using hybrid cat boost correlation algorithm. The novel approach contains word to vector-based data allocation for feature extraction. The extracted features are formulated into vectors of feature maps used for training vector as well as testing vector. Based on the accuracy of the created model the predictive analysis system is improved. Accuracy of 95% is achieved by the static data analysis. Further the system the same proved by considering different parameters of stack prediction model to prediction show the upcoming race and fall of stock price for the individual consumer.
Self-Aware Fog Layer toward Scalable Resource Allocation and Dynamic Queuing

The Internet is overwhelmed with innovative IoT -assisted devices. It is predicted that the number of online-connected devices will be more than 50 billion in 2030. Such IoT devices would need support from enabling technologies to consume less memory and lower the computation cost. The cloud-based services might further increase point-to-point latency. The unprecedentedly high volumes of real-time data generated by IoT devices may suffer from this delay issue. This work proposes a novel cognitive Fog computing-based data processing approach that manages the data influx caused by the sensor devices at the edge router. The proposed cognitive Fog based architecture has empowered edge devices, with the features such as Location awareness, low latency, portability, proximity to end users, diversity, and real-time response. A scalable resource allocation with a dynamic queuing technique was proposed. The simulation results have shown that the proposed architecture boosts the performance of the IoT Fog-based applications more than the existing approaches.
Bayesian Network based Optimal Load Balancing in Software Defined Networks

Due to the exponential increase in data volume, network complexity necessitated the requirement of software defined networks (SDN). But with SDN, network and service expansion significantly affect resource management. An efficient resource allocation method is mandatory to account for the random nature of network traffic and the load management across several controllers. However, due to the imprecise and dynamic relationships between resources, reinforcement learning has not been well-served in the context of real-time load in SDN. This paper presents deep reinforcement learning (DRL) technique to a Bayesian network to provide a smart optimization framework for SDN resource management. The Bayesian network use a reinforcement learning method, self-adjusting parameter weight, and automatic parameter weight adjustment to regulate the controller load congestion and anticipate the level of load congestion necessary to achieve load balance. By utilizing the prediction results from reinforcement learning, this algorithm selects the best possible next step. Theoretical analysis of the proper load balancing strategy for SDN is supported by a concurrent examination of existing datasets.
Development of A Smart Non-Invasive Glucose Monitoring System With SpO2 and BPM for Diabetic Patient

In this paper, a compact healthcare system has been developed that measures the glucose, SpO2, and heartbeat of the patient in a non-invasive method, and the result is provided within a minute on a Liquid-crystal display (LCD). In this system, a fingertip is placed on a 940nm-wavelength Near-infrared (NIR) light-emitting diode (LED) and a Saturation of Peripheral Oxygen (SpO2) sensor to get glucose level, SpO2, and heartbeat measurements. Light of a specific wavelength interacts with the blood, and the optical sensor detects the signals that carry the information of glucose, SpO2, and heartbeat level. Upon receiving the signal, the optical sensor's output is filtered, amplified, and routed to the ESP32, which calculates and displays glucose, oxygen saturation (SpO2), and beats per minute (BPM) on a liquid-crystal display (LCD) screen.
MediSearch: Advanced Medical Web Search Engine

Generic web search engines like Google and Bing are helpful for daily tasks and finding answers to most general questions. However, they could perform better for domain-specific queries. For the medical field, MayoClinic provides easy to use interface for common diseases, FindZebra is excellent for rare diseases search, and MediCrawl performs well for both rare and common diseases. In this paper, we propose MediSearch to extend the capabilities of the MediCrawl web search engine for diseases with image classification and textual queries. We propose to use machine learning algorithms on skin pictures taken from digital cameras or bones and tissue pictures taken from radiology devices, along with the free text search capabilities.
Trace Image Detection Technology Based on Deep Learning

The application of digital image processing based on depth learning in trace detection technology is a technology that allows users to analyze various types of images and data. It can be used to detect defects in materials, detect contamination, identify defects or anomalies, and inspect components. This software has been widely used by manufacturers and inspectors because it has many advantages over traditional methods. The main benefit users get from this software is that they can provide high-quality results in a short time. In addition, it can help reduce costs while increasing productivity. This paper mainly studies digital trace detection. Firstly, the digitization of the trace inspection acquisition process is introduced, and the technical change process in the trace acquisition process is introduced from two aspects of image acquisition and text acquisition; Then the specific process of trace inspection is divided into two parts: pretreatment and inspection. This paper introduces the process of image geometric processing, image arithmetic processing, image enhancement, image restoration and image compression coding in the early stage of trace detection, provides the detection basis for the subsequent trace detection process, and summarizes the application of digital technology in trace detection; Finally, it describes the digitalization process of the establishment and management of trace archives.
Review of Ransomware Attacks and a Data Recovery Framework using Autopsy Digital Forensics Platform

The proliferation of digital technologies and the ubiquitous nature of data connectivity has dramatically increased the landscape of cyberattacks over the past decade. Ransomware attacks have become a global incidence and the most destructive cyber menace. As a popular example of cryptovirology, ransomware attacks typically encrypt files on a target computer and threaten to publish or permanently prevent access to the victim's data unless a ransom is paid. In general, ransom demands are often made in cryptocurrency to obscure transactions and maintain anonymity. Nonetheless, paying the ransom does not guarantee data recovery; and therefore, there is a strong need to develop alternative data recovery strategies. To build and implement proper data recovery procedures, it is necessary to analyze ransomware and identify its characteristics. In this paper, we first provide a review of ransomware types and common data recovery methods. Then, we propose a novel ransomware detection and data recovery framework to effectively retrieve data from infected files. Specifically, we investigate the notorious WannaCry malware and analyze its execution on a Windows virtual machine. We conduct digital forensics using the Autopsy tool to recover WannaCry-infected data and demonstrate the practicality of the proposed framework. Our framework can be applied to develop effective data recovery methods for WannaCry and other ransomware variants with similar behavior.
Deep Learning-Based Speech Enhancement Algorithm Using Charlier Transform

Machine learning, a part of artificial intelligence, is recently used in speech enhancement algorithms (SE). The primary focus of SE is finding the original speech signal from the distorted one. Specifically, deep learning is used in SE because it handles nonlinear mapping problems for complicated features. In this paper, Charlier polynomials-based discrete transform, simply discrete Charlier transform (DCHT), has been used to get the spectra of the noisy signal using a fully connected neural network. Deep learning effectively acquires the context information of speech signal and gets enhanced speech with good quality and intelligibility properties. The proposed algorithm is tested experimentally through self-comparison to obtain the best speech enhancement models corresponding to the DCHT parameter. The experiment is performed with different values of the DCHT parameter. In addition, the well-known TIMIT database is used for evaluation purposes. Different speech measures are used in the experiment. The realized results show the ability of the trained model based on DCHT to enhance the speech signal and provide good results on specific conditions.
Optimization Techniques for Deep Learning Based House Price Prediction

With the persisting increment in the rates of houses, the cost of homes is escalating every year. So it is necessary to design a system that can predict future housing prices. This intelligent system undertakes the task to play a crucial role between learning and optimization. But because these systems are dynamic, complex, and multidisciplinary, optimizing them is particularly challenging. The House Price Index (HPI) is one of the most useful indicators for calculating frequent changes in home prices. The data processing of the current home pricing involves several approaches and takes into account several variables, including housing design, layouts, climate control conditions, etc. To incorporate the benefits of external factors and utilize the increased home price, this approach makes use of a multiple regression model and optimization approaches. Using enhanced housing price composition and multiple regression prediction models, such as particle swarm optimization, genetic, firefly, and mayfly algorithms, etc., it is possible to include the benefits of outside influences. The purpose of this work is to provide an overview of the latest and most popular deep learning optimization techniques which predicts the more precise and accurate prices of houses.
Optimization strategies for Micro-grid energy management and scheduling systems by Sine cosine Algorithm

Energy is an essential factor for power generation, where a community microgrid seeks to integrate renewable energy sources such as solar, wind, tidal, hydropower, and bioenergy into a distribution network along with an energy storage system. This is not only for the security of national growth, but also to minimize electricity costs and availability. The source of renewable energy has no predicted schedule for synchronization of power generation, supporting battery as energy storage for emergency supply or backup. In this paper, predictive scheduling of battery energy considering the cost of degradation due to charging and discharging cycles is proposed. According to the economy, day ahead planning has required a technique for solving the cost efficiency. In this paper, planned has developed by sine-cosine algorithm, which belongs to mathematical trigonometric base populations sector meta-heuristic technique. The sine-cosine algorithm works on the principle of trigonometric mathematical solution to search a target location within an area evaluation.
An Improved Eco-friendly Practices in Modern Agricultural Department using Agro Deep Learning Model

In general, various small and large hand tools are used for electrical work and if they are used in the best way, our work will be completed efficiently and quickly, and the tools will be used for a long time without repair. It is an important tool for electrical workers. It is used for cutting wires, twisting and winding them. Its handles are covered with rubber so it can be used to work even on live wires. We also use it to remove and replace screws. Used to loosen and fit screws at short intervals. It is mostly used to repair electronic devices like radio box amplifiers. It is used to remove the insulation over electrical conductors. It is also used to clean the wires from which the fuse has been removed. It should have a wooden or plastic handle. It should be 4” or 5” long enough to fold it up and put it in a shirt pocket.
A Tri-Classes Method for Studying the Impact of Nodes and Sinks Number on Received Packets Ratio of MANETs Routing Protocols

Mobile Ad hoc Networks (MANETs) consist of a set of mobile nodes connected together without any wired or physical infrastructure. , which makes nodes independent and simple in deployment. The arbitrary movement of nodes within appropriate range area makes dynamic network and routing between there nodes is difficult. In MANETs the routes are performed by the source nodes (sinks) that establish the network without central access point. , so number of sinks is very important in MANETs. When the nodes moving at a variant speed thus making unpredicted network that have unspecific topology. That networks have many limitations such as low energy due to the battery powered of its nodes. Therefore, routing protocols must be used based on the mobility. , suitable nodes and sinks numbers, and reducing the energy consumption of the nodes. In this research we proposed a methods consist of three classes (Tri-Classes) to study the impact of changing numbers of nodes and sinks on Received Packets Ratio (RPR) for different MANETs routing protocols, the comparison includes the four major routing protocols. , AODV (Ad hoc On Demand Distance Vector), DSDV (Destination Sequenced Distance Vector), DSR(Dynamic Source Routing), and OLSR (Optimized Link State Routing) under various nodes size (50, 100, and 250 nodes) and variant numbers of sinks (5, 10, and 15). We observed that the RPR is completely affected by changing numbers of nodes and sinks. In general we obtain best RPR by increasing nodes and sinks numbers. The protocols were simulated using Network Simulator 3 (NS3).
SkinNet-8: An Efficient CNN Architecture for Classifying Skin Cancer on an Imbalanced Dataset

Skin cancer is a fatal disease that has become the leading cause of death worldwide in recent years, although it is curable if diagnosed early. Early skin cancer detection significantly improves patients' chances of survival and reduces mortality. In this research, we conduct experiments on a high imbalance dermoscopic ISIC 2020 dataset. The primary objective of this study is to develop a shallow CNN architecture to complete the classification task effectively, requiring fewer computational resources without compromising accuracy. We have used pre-processing techniques to remove image noise and truncation and augmentation techniques to balance the dataset before fitting it into the model. Multiple performance measurement metrics were utilized to establish the overall performance. Our proposed model yields a remarkable test accuracy of 98.81%. We compare our models' performance with different transfer learning (TL) models to assess the faster convergence rate. The proposed model demonstrated its robustness by outperforming the other TL models in terms of accuracy within a short processing time. It is reasonable to assume that our proposed system will reliably aid dermatologists in diagnosing skin cancer patients early and increasing survival rates.
juktoMala: A Handwritten Bengali Consonant Conjuncts Dataset for Optical Character Recognition Using BiT-based M-ResNet-101x3 Architecture

Bengali, the seventh most spoken language in the world by the number of speakers, doesn't have a well-established Optical Character Recognition (OCR) system for handwritten texts. One of the major reasons behind this lacking is contributed to having no complete conjuncts database. No dataset available today covers all the conjunct characters that are used by authors around the globe. In this research, we prepared a complete dataset consisting of 292 consonant conjunct characters, which is the biggest consonant conjunct character dataset to date by the number of classes available in the literature to our knowledge. We applied Big Transfer-based M-ResNet-101x3 Deep Convolutional Neural Network (DCNN) which achieves 91.32% accuracy that outperforms the baseline EfficientNetB7 approach which obtained 81.05% accuracy.
Non-Invasive Blood Glucose Measurement Device: Performance analysis of Diffused Reflectance method and Diffused Transmittance method using Near Infrared Light

Diabetes is a condition that develops when blood glucose, often known as blood sugar, is too high. A diabetic patient must constantly monitor his or her blood glucose level to keep it under control. In the commercial invasive approach, a patient must injure his body part to obtain a blood sample, which is uncomfortable for the patient and can increase the risk of infection. Blood glucose monitoring using a non-invasive technique can lessen discomfort. In this paper, we suggested a non-invasive blood glucose measuring technique that consists of a Near Infrared LED (940nm) and a photodetector to estimate blood glucose levels. In our work, we employed Near Infrared Light to assess blood glucose levels. Following the device's implementation, we compared the accuracies of both diffused reflectance method and diffused transmittance method to see which method is preferable. It was found that diffused transmittance method is the better one of the two. The results from both methods were also compared with a commercial invasive blood glucometer on the market. It is observed from Clarke Error Grid Analysis that, most of the test data from diffused transmittance method lies in Region A. We have also developed an app that can show the data from the devices on patients' smartphones.