Miracle-dz/UniFAR-FTUs huggingface dataset:
Unmasking COVID-19 symptoms from Speech with Masked Autoencoders

In the aftermath of the COVID-19 pandemic, the need for swift, reliable, and scalable methods for COVID-19 like symptom detection has come into sharper focus. In response, we introduce Audio Masked Harmonic Autoencoder (AudioMHA), a novel self-supervised deep learning model designed to identify respiratory symptoms associated with COVID-19 and similar illnesses from audio signals, particularly cough sounds. Our model harnesses the strength of the transformer architecture, integrating an innovative Harmonic Attention-based decoder and a two-stage training procedure. Initially pre-trained on the rich and diverse AudioSet corpus, the model is then fine-tuned on the specific COUGHVID dataset, enabling the acquisition of robust, generalizable audio representations before honing in on detecting COVID-19-like cough signatures. Delivering an impressive F1 score of 86%, our model outperforms previous state-of-the-art baselines by a significant margin of 10% . This pioneering, non-invasive, and potentially scalable approach marks a significant stride towards enhancing preliminary symptom detection methods for respiratory illnesses. Our findings highlight the potential of sophisticated machine learning techniques in future healthcare applications, paving the way for prospective research in audio-based symptom detection.
Towards Automated Claim Detection In Fact Checking

The spread of fake news has an adverse effect on the audience as fake news can influence people to the extent that it may become a threat to their own life. Therefore, to verify a news article, we perform a claim detection task on the article to identify what it claims. In this work, we curate a dataset of 183 fact checked articles from India Today as well as make use of a benchmark dataset, FactDrill. We use both datasets to train model using conventional techniques namely Decision Tree(DTree), Naive Bayes(NB) and Support Vector Machine(SVM) with term frequency-inverse document frequency approach to extract features and out of which the Support Vector Machine(SVM) classifier outperforms with the F1-Score of 0.40 and 0.87 for Claim and Not Claim category on our dataset and F1-Score of 0.73 and 0.72 for Claim category and Not Claim category on FactDrill dataset, respectively. This is the first study to date that employs the FactDrill dataset for model training and testing using conventional methods.
EMPC-SA: Error Correction Scheme using Modified Product Code for Space Applications

The advancement of semiconductor technology has significantly increased data density, enabling the storage and processing of vast amounts of information. However, this higher data concentration makes the system more vulnerable to radiation-induced errors, especially in critical applications like space. Radiation can cause temporary errors in multiple bits, referred to Multiple-Bit Upset (MBU), that traditional codes like Hamming cannot correct, thus emerging more powerful codes like the Two-Dimensional Error Correction Codes (2D-ECCs). This paper proposes a 2D-ECC named Error correction scheme using a Modified Product Code for Space Applications (EMPC-SA), a product of two codes with fewer bits than traditional product codes. Each row and column of EMPC-SA is associated with a linear code consisting of three parity bits. The code was implemented using 16 data bits. The results of this work demonstrate that EMPC-SA achieves 100% error correction when 36 frequent error patterns from one to four bitflips are confined within the data matrix.
Flexible and Wearable EBG loaded SIW Band Pass Filter (BPF)

A substrate integrated waveguide (SIW) band pass filter (BPF) which is flexible in nature is discussed in this paper. Simple longitudinal ladder shaped electromagnetic band gap structure (EBG) is explored to obtain attenuation at the higher frequencies of the waveguide. Ordinary jeans fabric with thickness of 2mm, dielectric constant of 2.72 and loss tangent of 0.002 is used to prepare the filter for wearable applications. Wide passband frequencies ≈ 5 GHz to ≈ 13 GHz is observed by this technique. Important slot parameters which are affecting the transmission bandwidth are studied and presented with optimized results. The band pass filter which is proposed in this paper is having an insertion loss <1dB. The filter proposed in this paper is suitable for body wearable communication modules for emergency and rescue workers.
Investigation on Conventional LoRaWAN Communication System

The LoRaWAN (Long Range Wide Area Network) technology enables communications over extended distances in a license-free radio spectrum. LoRa is significant because it enables low-data-rate, long-range communication for Internet of Things (IoT) applications. In order to suit LoRa for higher data rate applications, certain modifications to the present state of the art need to be executed. The objective of this research is to address the above requirement for LoRa. LoRa employs Chirp Spread Spectrum Modulation (CSSM) for signal modulation. In this research, the conventional LoRa system, comprising a LoRa Transmitter and Receiver along with an AWGN (Additive White Gaussian Noise) channel, is simulated in MATLAB R2018. This modulation technique involves the generation of a chirp signal with the frequency increasing or decreasing over a time period. Data is modulated using the chirp signal and encoded using Hamming encoders. It is spread over a wider frequency range and transmitted over the AWGN channel. The envisioned system efficacy is measured in terms of Bit Error Rate (BER) vs. Signal-To-Noise Ratio (SNR), with spreading factors ranging from 7 to 12. From the results obtained, it is found that a higher spreading factor results in a lower data rate, and vice versa. Hence, a lower spreading factor is recommended to achieve an improved data rate.
Vader protocol based sentiment analysis technique using LSTM for Stock Trend prediction

Stock prediction has always been a difficult task, and numerous strategies for predicting stock market behavior have been offered. Researchers have recently begun to investigate the use of sentimental analysis and deep learning models such as Long Short-Term Memory (LSTM) to predict stock prices. We offer a stock prediction model that integrates sentimental analysis and LSTM in this research. The model considers the mood of stock market news items and use LSTM to forecast future stock prices. The results of our suggested model's evaluation on the S& amp; P 500 dataset reveal that it outperforms traditional stock prediction approaches.
Resilient Fog-Based Adaptive Traffic Control System

Traffic congestion is a growing problem in India, largely caused by the increasing number of vehicles. To address this, creating an adaptive traffic control system has become essential. To present a solution, this paper aims to create a resilient, adaptive traffic control system that dynamically adjusts signal timings based on the current traffic density. The paper proposes introducing a Fog layer at each traffic junction to capture real-time video feeds and process them locally, with the final decision about dynamic signal control being taken in the Cloud. The validity of the solution is tested through simulations in SUMO. A Machine Learning model is trained in the Cloud to predict expected vehicle numbers to set adaptive signal times for contingency situations. The paper aims to demonstrate the resilience and performance of the system with the adaptive traffic signal control algorithm, potentially offering a solution to traffic congestion in India.
I-SDFNet: Interpolation-Based Implicit Neural Representation for Learning Watertight Surfaces

As machine learning methodologies continue to revolutionize computer graphics, the need for effective surface representation methods has become paramount. This paper presents I-SDFNet, an innovative deep learning methodology developed for acquiring an Interpolation-Based Signed Distance Field of a watertight surface by leveraging its inherent raw geometric information, such as point cloud or triangle soup. The zero-level surface of this acquired Distance Field resembles the continuous surface corresponding to the input point cloud (or triangle soup). I-SDFNet harnesses the capabilities of implicit neural representations and interpolation techniques to construct high-precision, watertight surfaces, essential for numerous applications, including rendering, 3D printing, and virtual reality. I-SDFNet introduces a new interpolation-based Signed Distance Field to capture intricate, high-fidelity details, thereby heralding a new era of surface representation in the computer graphics landscape.
Mel-Frequency Cepstral Coefficients-Based Emotion Identification Using Artificial Neural Network From Speech And Songs

This paper compares the performance of three popular machine learning algorithms for speech emotion recognition - Multi-Layer Perceptron (MLP), Decision Tree, and Convolutional Neural Network (CNN). The MLP model achieved competitive accuracy of 0.83 while being computationally efficient and easy to train. The Decision Tree algorithm, which is a popular technique for categorization tasks, achieved an accuracy score of 0.65. The CNN model exhibited superior performance compared to the MLP and Decision Tree models, achieving an accuracy score of 0.86 and optimal performance across all emotion classes. The study also found that certain emotions are more difficult to discern than others. While the research highlights the potential of CNN models in speech and audio recognition, there is still scope for improvement, particularly in recognizing challenging emotions. Overall, this paper provides significant insights into the efficiency of several algorithms in speech emotion recognition and recommends future research directions.
Design of SICL Fed Wideband Dipole Antenna for Endfire Applications at 5G Millimeter Wave Frequencies

In this paper, design of a Substrate Integrated Coaxial Line (SICL) fed wideband printed dipole antenna using simple technique is presented for endfire applications in millimeter wave frequencies. Having the inherent advantage of side via-wall shielding and broadband unimodal operation, SICL technology is chosen to design an efficient feeding network for the proposed dipole antenna. The design starts with a conventional half wavelength (λg/2) dipole that gives a bandwidth of 5 GHz, which is further modified by using slant ± 45° tilted dipole arms that helps in achieving wideband performance. The proposed design achieves a peak gain of 5.6 dBi over a bandwidth of 11 GHz. The presented antenna design is planar, simple and compact that does not require additional balun to feed the arms of the dipole antenna. This antenna is a suitable candidate for endfire 5G mmWave applications.
Phoenix: IoT Trust Evaluation Using Game Theory with Second Chance Protocol

Internet of Things (IoT) devices are a big part of our daily lives for their use in smart environments and open fields. However, IoT devices, characterized by limited computational capabilities and energy constraints, are prone to malfunctions and cyberattacks. This vulnerability raises substantial trust issues, particularly concerning the reliability of the data they provide. This paper proposes a hierarchical trust evaluation methodology by incorporating game theory and Bayesian inference designed for IoT devices to address the trust evaluation issue. We also explored the impact of historical behavior, such as nodes’ age and trust ratio improving our trust evaluation results. Furthermore, we proposed a technique named "second chance protocol" to identify a transitory malfunctioning node and introduce it back to the network once it regains trustworthiness, resulting in resource optimization and enhancing average trust in the model. We generated synthetic data to evaluate the proposed approaches with randomly distributed noise levels simulating failures or compromised IoT devices. The results of our experimental analysis demonstrate that our approach improves the overall trust ratio of the system concerning traditional approaches. The proposed second chance protocol further enhances the trust average and device utilization by accurately identifying malfunctioning nodes. Our result with different noise levels achieves five to ten times better than an alternative study. Finally, our model achieved a detection rate of 97.99%, 89%, and 79% for 10%, 20%, and 30% noise levels.
Popularity-Driven Optimized Caching Scheme for Content-Centric Networking

Content-Centric Networking (CCN) emerged as one of the most promising Internet architecture. It offers data-centric communication instead of the host-based infrastructure, which has been used in the most of the existing solutions. In contrast to host based architecture, For effective content distribution and to raise the Quality of Service (QoS) for the end user, CCN offers in-network caching capabilities. However, in-network routers can cache a limited number of contents as their cache size is generally trivial in contrast to the content catalogue at the server. Thus, for improved utilization of the available network resources, an efficient caching approach has been suggested in this work. The approach implements a dynamic sized popularity table on every router. This table is invoked and searched during content caching to determine the content access frequency. The scheme proposed the empirical heuristics for optimal caching decisions based on a realistic network topology. Comprehensive simulation executions show effectiveness of the suggested caching technique as traditional caching schemes do not offer optimal network performance under the realistic cache size, catalogue size and request rate parameters. According to the simulation results, the suggested approach significantly improves the in-network cache hit-ratio when compared to majority of existing solutions while reducing network traffic and delay during content retrieval.
A Comparison of Two Transformers in the Study of Plant Disease Classification

This paper, have implement two transformer models, namely Vision Transformer (ViT) and Shifted WIndow Transformer (Swin) for identifying paddy leaf diseases at an early stage, using thermal images. Thermal images provide detailed and precise information when compared to normal images, which helps in the early detection of leaf diseases, even before the spots appear on the leaves. The aim of this paper is to prove that both Vision as well as Swin transformer works well with vision (image) tasks, such as image classification. Here, in our paper we were able to identify and classify paddy leaves diseases using ViT and swin transformer with an accuracy of 94% and 98% respectively. Swin transformer works better when it comes to semantic segmentation, where one needs to classify each pixel to it’s respective category. It does so by using the concept of Windows, about which we will talk in detail in the coming sections.
Dealing with Soft Aperiodic Tasks in RUN

RUN is a scheduling policy designed for multiprocessor real-time systems, composed of periodic task sets with implicit deadlines, that guarantees up to 100% of processor utilization with reduced number of preemptions. In this paper we extend RUN to deal with task sets composed of both hard and soft tasks. Results are evaluated via simulation, which indicates that: RUNHS can deal with soft tasks; the average response time of aperiodic tasks is improved; hard deadlines are preserved while soft deadline miss ratio is kept low.
Predicting BPL Match Winners: An Empirical Study Using Machine Learning Approach

With the evolution of computer science, every company is implementing the newest technologies to survive in market with better decision-making capabilities, better communication and customer satisfaction. The only means of fulfilling all these criteria’s is to perform data analysis that is more accurate and pure. In cricket, where no one can guess which team will win until the last ball of the last over, machine learning can help by predicting the results of the games. Match outcome prediction models have a lot of financial incentive because cricket is a multi-billion-dollar industry. The goal of this study is to identify the most accurate machine learning model that can accurately predict the winner given the data from the Bangladesh Premier League. For this analysis five ML models XGBoost, Gradient Boosting, KNN, Decision Tree, Random Forest has been tested for the purpose of model building despite that our proposed model is XGBoost. To get access to BPL dataset web scrapping has been done, the dataset contains 15 columns and 3239 values and 8 team was available in each season from 2018 to 2023. We use cutting-edge machine learning techniques based on the use of numerous models, feature selection, and data separation techniques. Finally, by structuring every line of action, the forecast accuracy is attained.
A Comparison of Deep Learning and Machine Learning Models for Speech Emotion Recognition Using Multiple Features

This study presents a comparison between different deep learning and machine learning method for emotion recognition of speech data. Multiple features were extracted from the data and used for training the LSTM, MLP, Random Forest, and CNN models. Datasets used in the study are CREMA-D, RAVDESS, SAVEE, and TESS. The combined dataset are categorized into seven emotions such as surprise, neutral, disgust, fear, sad, happy, and angry. The features extracted from the dataset are zero crossing rate, MFCCs, root mean square, chroma stft, spectral contrast, spectral bandwidth, spectral centroid, spectral rolloff, spectral flatness, poly feature, mel spectrogram. LSTM model gives an accuracy of 57.5%. MLP model provided an accuracy of 59.6%. The accuracy of Random Forest model is 67.8% whereas the CNN model provided an accuracy of 75.8%. The results shows that CNN model out performs the LSTM, MLP, and Random Forest models when used for emotion recognition of speech.
Machine Learning Based Malicious URL, IP & File Classification

Machine learning algorithms to identify and categorize cyber hazards have garnered attention recently. Malicious URLs, IP addresses, and files provide the greatest cybersecurity risks. This abstract describes how machine learning-based classification of dangerous URLs, IP addresses, and files identifies and mitigates threats. The article begins with hazardous URL categorization, which uses machine learning to identify if a URL is risky depending on its attributes. URL classification is challenging since there are many legitimate URLs. Researchers have developed many machine learning algorithms that assess URL domain names, length, and keywords to identify fraudulent URLs. Second, machine learning-based IP address classification is discussed. Classifying IP addresses helps identify and combat DDoS and botnet attacks. To discover rogue IPs, machine learning techniques like clustering, classification, and regression examine IP reputation, geolocation, and traffic patterns. File categorization—using machine learning to identify and categorize potentially hazardous files—concludes the study. If users open harmful files, viruses, Trojans, and other malware may infect their systems and steal personal data. Using file type, size, and behavior, supervised and unsupervised machine learning systems may identify dangerous files. This study discusses machine learning methods for detecting and banning risky websites and files. Machine learning algorithms can detect and mitigate cyber risks through analysis and categorization. These algorithms have false positives and negatives, overlook dangers, and require a lot of training data. Thus, machine learning must be employed with intrusion detection systems and firewalls to guard against cyberattacks.
Stock Market Prediction Using Genetic Algorithm Assisted LSTM-CNN Hybrid Model

Stock price prediction has always been a tough task for all the stakeholders involved. This paper focusses on four different models, namely LSTM, CNN, LSTM-CNN, and Genetic Algorithm-Assisted LSTM-CNN (GA-LSTM-CNN) for stock price prediction. The day-to-day stock prices of a certain company for a period of more than 10 years make up the dataset utilized in this study. Root mean square error (RMSE) has been utilized as performance metric for each model. Outcomes of our experiment show that the GA-LSTM-CNN model performed better than other models with an RMSE value of 0.0064 upon training and an RMSE value of 0.03 upon testing. The GA-LSTM-CNN model combines the strengths of LSTM as well as CNN models with the help of a genetic algorithm, which helps in optimizing the hyperparameters. According to the results, the GA-LSTM-CNN model can be very helpful for the stakeholders in predicting future stock values.
New fractals as images of classic fractals under a complex polynomial transformation

This paper proposes a new way of generating fractal sets using classic fractal sets and complex polynomial transformations. This study maps a classic fractal using a complex polynomial transformation, giving rise to a new fractal set. It is important to note that from a classic fractal, you can obtain different images that have the behavior of a fractal set, depending on the transformation used. The box count dimension method shows that the new images can be located within the Fractal Geometry since they are dimensioned fractional. This proposal generates a new class of fractal sets and opens up the possibility of new applications, considering the applications offered by classic fractal sets.
High Gain Semicircular Slotted Printed Antenna Array for 5G and Beyond Applications

This paper portrays the design and characterization of a high-gain dual semicircular slotted planar antenna array at 60 GHz. A compact 4×1 array was designed and its bandwidth and gain were improved by adding a high permittivity superstrate above the array. The proposed 4×1 array antenna with superstrate provides a 6.43 GHz improved impedance bandwidth with 16.5 dB enhanced gain. A simulated efficiency of more than 95% was achieved for the designed array antenna. A parametric study has been performed to discover the effect of superstrate parameters on array performance. The array antenna exhibits almost similar simulated and fabricated results. The designed antenna array will be used in mm-wave fronthaul / backhaul applications for 5G and beyond applications.
Emotion Recognition from Electroencephalogram using Variational Mode Decomposition and optimized Fuzzy K-Nearest Neighbor Algorithm

Emotion recognition via Electroencephalogram (EEG) has gained considerable attention in the recent years due to its objective and naturalistic response to the emotional stimulus. However, the non-stationarity of the EEG signal makes it challenging to decipher the information hidden in the raw EEG using the conventional time or frequency domain methods. To obtain the pattern information from the EEG data useful for recognition, a framework using Variational Mode Decomposition (VMD) is proposed. This framework firstly uses VMD, an adaptive approach to decompose EEG into specific Intrinsic mode function representations (IMF’s). Thereafter, power in different rhythms and absolute value of wavelet coefficients is extracted from each IMF. Moreover, a BAT optimized Fuzzy K-Nearest Neighbor (FKNN) algorithm is presented to finally recognize the different emotional states. The BAT algorithm optimizes the FKNN hyper-parameters to boost the classification performance. The proposed framework is tested on EEG signals from DEAP dataset, in which the Arousal and the Valence conditions are classified as High or Low. The results indicate that the proposed framework outperforms the existing state-of-the-art works by attaining a classification accuracy of 65% for High/Low Arousal and 62% for High/Low Valence conditions.