Miracle-dz/UniFAR-FTUs huggingface dataset:
Clustering Technique for Crime Rate Prediction and Warning to Users in Big Data Environment

Crime analysis is a systematic strategy for detecting and evaluating recurring criminal tendencies. Expensed our society significantly in a variety of ways. Many of us have to travel often for work or other reasons, and we constantly encounter a wide variety of threats to our safety, including hijacking, abduction, and harassment. As a whole, we can see that whenever we need to travel somewhere new, the first thing we do is pull up Google Maps; when presented with many routes to our destination, we almost always go for the shortest one, despite our inability to properly assess the situation. In this role, we use a variety of data mining clustering techniques to examine Bangladesh’s crime rate and a K-nearest neighbour (KNN) method to train our dataset, all of which leaves us wondering whether or not we are safe. The two types of information (primary and secondary) are both essential to our work. Through statistical examination, we are able to estimate the forecast rate of various crimes at various locations, and then use this rate to the algorithm’s output to get the predictive accuracy of the route. Last but not least, we use the predicted rate to choose our most secure path. As a result of this work, locals will have a better idea of the area’s safety and can find their way there more efficiently.
Smart Authentication using Human Ear

Biometric identification is the study of physiological and behavioral attributes of an individual's to identify the identity of the persons. The human ear, a recent development in biometrics, has several advantages over face, fingerprint, and iris biometrics. In the modern era, biometric technology is becoming increasingly popular for use in security and other applications. Theft and identity fraud are now major issues all over the world. To prevent these thefts and identity fraud, an ear recognition system must be installed. This paper's goal is to develop an ear-based security access control application. To achieve high accuracy and effectiveness, the training and identification processes are carried out using Python and the Raspberry Pi. According to the analysis, it has better efficiency and it can be used for a variety of applications, including ear detection.
A Text Independent Speech Emotion Recognition Based on Convolutional Neural Network

With the advancement of deep learning approaches, the performance of speech emotion recognition (SER) has shown significant improvements. However, system performance degrades substantially when number of emotional states increased. Therefore, this study proposes a text independent SER system that can classify eight emotional states. The proposed system uses joint Mel frequency cepstral coefficient (MFCC) and Log-Mel spectrogram (LMS) to represent the speech signals and a convolutional neural network (CNN) to classify these features in to different emotional states. Results show that the proposed system can achieve an average accuracy of 93%. Two widely used datasets RAVDSESS and TESS have been used in this work to test the model performance. Experimental results present that the proposed framework can achieve significant improvement using a joint feature of MFCC and LMS. Furthermore, the proposed network outperforms state-of-art networks in terms of classification accuracy. This network could be reliably applied to recognize emotion from speech in naturalistic environment.
Using Cuckoo Filters to Improve Performance in Object Store-based Very Large Databases

Today, there is a growing trend to leverage a cheaper data lake such as AWS S3 for storage of tables in very large databases deviating from traditional databases. Here, data is organized as objects (collection of tuples) before placing them in a bucket/container on the external object storage. To allow multiple readers and writers accessing the same objects in parallel, objects are multi-versioned using multiversioned concurrency control (MVCC). Typically, write operations on objects are not done in-place. Instead, newer versions of the modified objects are created. In the context of locking mechanism used today, only table level locks are being placed, which prevents writers on disjoint object sets of the same logical table executing concurrently. In some databases, optimistic concurrency control is used wherein no locks are placed upfront and concurrent writes can proceed. However, these are prone to additional rollbacks placing significant overhead on the system when modifications are a significant percentage of the overall load. In this paper, we focus on pessimistic concurrency control, managing object level locking efficiently using cuckoo filters. To enable transactions to lock only relevant objects of a table, we maintain object-level metadata including information such as minimum, maximum, unique values, number of non-null values, etc., for selected attributes in the table. With such a rich set of information available, objects needed by a transaction for processing could be identified with a greater precision. In summary, we propose an object-level locking mechanism leveraging cuckoo filters to improve the overall throughput of concurrent write transactions with a pessimistic multiversion concurrency control mechanism. Through experimental results and probabilistic analysis, we show the significant improvements (up to 60% in elapsed time and 171% in throughput) using the proposed scheme.
Fashion Forecasting using Machine Learning Techniques

Adopting current fashion trends for both apparel and accessories has now become a necessary component of day-to-day life. It creates confidence and helps make someone likeable. People are following new trends and placing a greater demand for fashion goods as they become more concerned with their appearance. Many people are entering the fashion and textile industries as demand for fashion items rises. But this trip also involves changes in fashion. Based on how long a fashion trend lasts, there are many sorts of fashion lifecycles. The lifecycle of a given fashion is defined as “normal” by its average survival time. Therefore, fashions and trends become popular rapidly but cannot last for a long time. These fashion lifecycles are referred to as “fads. ” “Classic” fashion is that which endures for a longer amount of time. It is easier to predict impending changes in fashion if a businessperson has analytical knowledge on hand. In this way, it will be advantageous for customers as well. Appropriate business decisions will be made in order to prevent excessive loss, and it will also give the right fashion advice to the client so that they invest their money in the right direction. Fashion picture analysis is helpful for a variety of fashion-related applications, including fashion retrieval and fashion recommendations. There are numerous websites that predict next fashion trends, like Heuritech, Wgsn, and Trendzoom, among others. The motive of this study is also the same, but we will make some more advanced changes in this regard. The effectiveness of the fashion forecasting analyzer website should continue to increase in the years to come as a result of the increased research spurred by this advancement in fashion forecasting.
Joint Estimation of the Distance and Relative Velocity of Obstacles via Smartphone Active Sound Sensing for Pedestrian Safety

In this study, we proposed a new method for pedestrian safety named ObsSense that estimates the distance and relative velocity of both static and mobile roadside obstacles using probing signals emitted by an off-the-shelf smartphone carried by a pedestrian. Sound-based estimation of the movement properties of an obstacle has been actively studied in pervasive computing, and generally provides the distance from a smartphone user to an obstacle and the relative velocity of the obstacle. However, most prior studies have focused on estimating the properties of either static or mobile obstacles. Furthermore, they estimated either the distance to obstacles or their relative velocity. Nonetheless, both the distance and relative velocity of the oncoming obstacles are necessary to derive the timing of a possible collision. To address this issue, we proposed a new probing signal composed of sine waves and sine sweeps, designed to acquire information about both the distance and relative velocity of static and mobile obstacles. In addition, we proposed a neural network model that can jointly predict distance and relative velocity from reflected sounds of the probing signal captured by a smartphone. The proposed neural network was designed to consider the mobility status of obstacles, i.e., whether they are mobile or static, in order to select the most appropriate method to estimate their distance and relative velocity. In addition, our method uses the relationship between distance and velocity to increase the precision of its estimations. For example, the distance can be estimated by integrating the velocity-time curve. The performance of ObsSense was evaluated using real-world data, and the experimental results demonstrated the effectiveness of our proposed probing signal and neural network architecture.
On Certain Subclass of p-Valent Functions with Negative Coe¢ cients De…ned by Convolution

In this paper we introduce and study new class of analytic p-valent functions with negative coe¢ cients de…ned by convolution. We obtain coe¢ cients inequalities, distortion theorems, extreme points and radii of close to convexity, starlikeness and convexity for the class T � p (f;g;A;B;m;q). Also we investigate several application involving an integral operator. Finally, we obtain integral means for functions belonging to this class.
Research on the Methods of Data Mining based on the Edge Computing for the IoT

With the advent of the internet of everything, the number of network edge devices has increased rapidly, causing the data generated by such devices to increase rapidly. In order to enable the internet of things to provide users with information services and reliable decision-making more intelligently, valuable knowledge can be fully mined from these massive data. At present, the use of cloud computing models for centralized processing of big data generated by the internet of things has begun to show many drawbacks. Compared with cloud computing, edge computing migrates data computing or storage to the “edge” of the network close to users, reducing the delay brought by network transmission. In addition, edge computing mode can better protect users' privacy data and reduce the risk of sensitive data privacy leakage. Edge computing has become a new paradigm to solve the needs of Internet of things and localized computing. The thesis studies the method of image data classification processing of the Internet, the edge agent is established by deploying the shallow part of the neural network on the edge device, and the cloud agent is established by deploying the deep part of the neural network on the cloud server device. The multi-agent is constructed by the way of edge cloud cooperation. The reasoning execution of convolutional neural network can complete the classification and exit on the local terminal for fast local inference; when additional processing is needed, the data can be transmitted to the cloud server for further processing by using the deep neural network in the cloud to improve the performance accuracy of the system.
Road Curb Detection Based on a Deep Learning Framework

Detecting curbs efficiently and at a low cost is essential for autonomous driving. Most of the existing methods rely on light detection and ranging, which is a complicated and costly solution. Therefore, this work propose an efficient method for detecting curbs in a single road image. This method utilizes the convolutional neural network (CNN) framework to localize the curb parts in the captured image. We built a feature-extraction CNN that can classify curb and no-curb regions. The relu, pooling, fully connected, softmax, and classification layers in the pretrained ResNet18 are replaced with ten new layers, which are relu, convolution2d, batchnormalization, dropout, fully connected, relu, maxpooling2d, fully connected, softmax, and classification layers. The comparison of several evaluation metrics to prove the effectiveness of the customized network structure for curb classification. The customized network structure is assembled in the Faster R-CNN detector to improve the performance of feature extraction both for classification and detection. In the detection stage, although the layer we used for feature extraction was the previous layer of the added layers, the detection experimental results demonstrate that our custom CNN network structure can improve the classification performance of the feature extraction layer.
Machine Learning Framework for Analyzing Disaster-Tweets

During natural disasters and catastrophes, Twitter is becoming a more popular source of information exchange. It is primarily used to share the status of disaster recovery efforts initiated by humanitarian and disaster relief organizations, to report and request or provide volunteer services, and to update on the scope of geographic phenomena. This paper supports the creation of future automated crisis management systems as well as the planning and preparation of effective disaster responses by teams working on disaster mitigation. This work focuses on developing a comprehensive framework for text processing and analysis on tweets posted in Twitter during natural catastrophes using natural language processing techniques. Disaster-related tweets are categorized into precautionary tweets, educational tweets, and recovery tweets. The algorithms which are used to develop the framework are Naïve Bayes based on Bayes theorem, Logistic Regression based on Sigmoid function, Random Forest based on decision trees, Extreme Gradient Boosting is based on bagging and boosting, Support Vector Machine is based on hyperplane. Five performance metrics, namely, accuracy, precision, recall, F1-score, and time, are calculated to assess how well the algorithms perform. The data set is split into training set and testing set as 75:25, 63:37, and 50:50. This comparison is to provide insights about the performance of algorithms in terms of efficiency with time bound actions and reactions.
A 2.7 W AC-Coupled Hybrid Supply Modulator Achieving 200 MHz Envelope-Tracking Bandwidth for 5G New Radio Power Amplifier

Envelope tracking is an effective way to improve the efficiency of radio frequency power amplifiers (PAs). This article presents a wideband hybrid envelope tracking supply modulator (ETSM) for 5G new radio PAs. To reach a wide envelope tracking bandwidth of 200 MHz while maintaining high power efficiency, this work utilized a linear amplifier with accurate frequency compensation and enhanced driving capability. The proposed low-pass sense and control circuit ensures that the switching power amplifier only tracks the low-frequency components of the envelope to reduce the switching power loss. The proposed dual-path duty modulator regulates the voltage across the ac-coupling capacitor. This supply modulator circuit is designed and fabricated using 0.18 μm BCD technology. When tracking 200 MHz bandwidth envelope signals, the prototype ETSM achieves 78.5% efficiency at the output power of 2.7 W when taking a realistic 4 Ω//600 pF load as the PA model.
An Involuntary Liver Cancer Recognition in Abdominal Liver Imageries with Soft Optimization Methods in Biomedical Application

A medical image recovery system can be used for a variety of useful purposes, one of which is the classification of medical images. When numerous different types of medical imaging evidence are accessible, it is essential to have accurate categorization models. Because of the CT paradigm in clinical diagnosis, radiologists are able to properly detect and monitor changes in the body's physiological state. The ability to differentiate between different types of tissues in a CT image based on the various grey rates along each line contributes significantly to the accuracy of a medical diagnosis. The standard practice for diagnosing cancer relies on scientific and histological data, both of which have the potential to lead to incorrect or erroneous findings. The liver, which may be found in the upper abdominal region of the external human form, is responsible for filtering the blood and getting rid of any toxins it discovers in the process. The distinction between the two types of cancer is taught through an artificial neural network in this method. A number of different parameters, including accuracy, area, correlation, entropy, homogeneity, contrast, and the similarity index, are employed to conduct an analysis of the impact. After that, researchers tried to enhance the early identification and treatment of liver cancer by segmenting and classifying liver tumors from CT images. This was done in order to gain a better understanding of the disease. The accuracy of the identification was raised to 99.63 percent thanks to the classifier and optimizer that was proposed.
Analysis of Key Policy-Attribute Based Encryption Scheme

Attribute-based encryption (ABE) is a Iproposed new cryptographic fundamental that might be used to solve the issues of secure and acceptable intelligence sharing and decentralised access control. Key-policy attribute-based encryption (KP-ABE), a popular kind of ABE, tags cipher texts with a set of characteristics and connects secret keys to access structures that limit which cipher texts a user can decrypt, Data sharing on unstable cloud storage has a broad range of applications for KP-ABE. The cipher text size, on the other hand, expands exponentially in relation to the number of characteristics in the cypher text in most recognized KP-ABE systems. A novel context-aware Cyber- Physical System that integrates embedded sensors, Internet of Things technologies, and cloud computing is thought to benefit the healthcare industry (CPS). As a result of the intellectualization of mobile terminal devices, mobile healthcare networks are automatically generated. These networks can offer basic health care services as well as real-time communication. However, the CPS has two challenges that need to be solved. The first challenge can be overcome by lowering a mobile terminal's processing and storage expenses. The second challenge can be addressed by providing healthcare data with cyber-physical privacy and security. Data privacy and secure sharing in an open environment can be guaranteed by key-policy attribute-based encryption (KP-ABE). Encryption, authentication, and authorization techniques are used to keep data safe. Some of the more complex applications and cryptographic approaches that the KP-ABE system can be used in include event log encryption, selective broadcast encryption, operational encryption, and software obfuscation. An access tree can be used during the key creation process to create fine-grained access control, and users' attribute sets can also be permitted to overlap. This work provides a detailed analysis of the Key Policy Attribute Based Encryption Scheme, allowing the investigator to concentrate on the flaws suchas collusion attacks and make improvements.
Patients' Health Analysis using Machine Learning

The main aim of this study was to analyze patient health using Machine Learning (ML). To do this, we used the Extreme Gradient Boost (XGBoost) classifier and auto-ML-Pycaret techniques. The sequential procedure we followed for the XGBoost model is data analysis, feature engineering, and model building, which are discussed in this paper. For these tasks, we used data science tools such as the Jupyter notebook and Google Colab (GC). Subsequently, we discuss the auto-ML-Pycaret model, which is an excellent tool for ML tasks. Finally, a performance comparison is performed between the two models based on their accuracy levels. The accuracy rate for the first ML model was 87%, and for the auto ML Pycaret model, we achieved 88% accuracy. Based on the accuracy percentages and time factor, we observed that the auto-ML Pycaret model performed better than the XGBoost model.
Xception Model for Pneumothorax Classification using Chest X-ray Images

Pneumothorax is a disease that might threaten the patient’s life and must be recognized and treated as soon as feasible. An X-ray of the chest should be conducted as the first diagnostic test when a pneumothorax is suspected. Due to the advent of deep learning techniques, its diagnosis can be accurately performed. If it can be used successfully, it may be able to help us find solutions to a range of problems. The application of classification in the prognosis of pneumothorax greatly aids medical professionals in treating the illness. It took a long time to uncover the problem, which caused numerous pneumothorax patients’ deaths. This unique technology enables us to diagnose patients more swiftly and accurately. A pre-trained model called Xception was used in this work. The model performs admirably, and the time spent training decreases thanks to the pretrained models. The model is performed on a total of 2027 photos, with 1824 images used for training, 102 for testing, and 101 for validation. Precision, recall, accuracy, and F1-score are used to calculate the findings. When the model’s parameters are compared, the total accuracy is determined to be 85%.
A 5G Cloud Platform and Machine Learning-Based Mobile Automatic Recognition of Transportation Infrastructure Objects

Crack recognition is important in periodic pavement inspection and maintenance. The wide application of image recognition technology in daily inspection and maintenance makes the health monitoring of asphalt pavement defects more effective, both intelligently and sustainably. In this study, a mobile automatic system integrating fifth-generation wireless communication technology (5G), cloud computing, and artificial intelligence (AI) was proposed for transportation infrastructure object recognition. The original dataset contained 344 images of pavement defects, including longitudinal cracks, transverse cracks, alligator cracks, and broken road markings. Three lightweight algorithms for automatic pavement crack identification were used and compared, including MobileNetV2, ShuffleNetV2, and Res-Net50 networks, respectively. The results showed that the model based on ShuffieNetV2 achieved the best overall predictive accuracy (ACC = 95.52 percent). A mobile automatic monitoring system based on the cloud platform and Android framework was then established. With the help of 5G technology, the cloud-network-terminal’ interconnection can be achieved to provide fast and stable information transmission between transportation infrastructure and road users. The proposed system provides an engineering reference for the transportation infrastructure inspection and maintenance using the 5G communication technology.
Wavelet based Convolutional Autoencoder for Medical Image Compression

Deep learning approaches have seen tremendous progress lately, particularly in image processing. In image compression, the deep autoencoders are predominantly showing promising results. Autoencoders have demonstrated exceptional performance in image compression tasks when compared to conventional lossy algorithms used for compression, in both the quantitative and qualitative aspects, despite the fact that they are lossy image compression approaches. This paper, puts forth a wavelet-based autoencoder-style compression approach for medical image compression. The proposed architecture is composed of four modules namely Pre-processing, Encoder, Decoder and Enhancement module. In order to achieve remarkable compression performance on the medical images, this method utilizes the advantage of wavelet decomposition technique to extract finer details from the low and high frequency components of the medical image. The experimental findings suggest that our approach achieves notable image compression performance. It outperforms the established JPEG in terms of the objective and subjective metrics PSNR and SSIM, and it also yields results that are comparable to those of JPEG2000 and BPG.