Miracle-dz/UniFAR-FTUs huggingface dataset:
Motion Prediction for Autonomous Vehicles Using Deep Learning Techniques

Autonomous vehicles require motion prediction of nearby traffic agents to guarantee secure navigation. By anticipating the movements of surrounding objects such as other vehicles, pedestrians, and bicycles, the autonomous vehicle can make informed decisions to prevent collisions, adjust speed and direction, and operate effectively in changing conditions. Motion prediction is an essential aspect of autonomous vehicle systems, contributing to increased safety, dependability, and efficiency. This work generates an effective mechanism to predict the movement direction of nearby traffic entities around our ego vehicle (the vehicle that we originally reference) using deep learning techniques and the inclusion of a LIDAR-based dataset.
Incentive-based Energy-efficient Federated Learning Aggregation for Intrusion Detection in IoT Sensor Network

At present, the wide range of critical applications encompassing healthcare and financial management are inhabited by wireless sensor networks (WSN). The foremost challenge associated with it is the resource constraint. Also, the WSN being itself deployed for critical applications, the adverse wireless security threats lead to head-on effect on the safety and operational efficiency. Hence in WSN, the intrusion detection implementation should corroborate the energy-efficient implementation for seamless operation. The inherent distributed characteristic of WSN mandates the federated learning to avoid the huge communication cost. This in turn preserves the security of critical sensor data used for training. However, the sensor nodes suffer from energy depletion due to local training and communication for federated model aggregation. Thus, in the proposed Incentive-based Energy-efficient Federated Learning Aggregation (IEE-FedAgg), the global model incremental training performance is considered for optimally scheduling the sensor nodes for learning. The aggregation number is further tuned based on the real-time performance exhibited. The simple multi-layer perceptron (MLP) and Long Short-Term Memory (LSTM) were considered for local training. Both the communication and computation energy consumption of the sensor nodes are considered for better analysis. The proposed IEE-FedAgg-LSTM model demonstrates the multi-class prediction accuracy of 99% and 98% for iid and non-iid data distribution respectively. Further, the proposed IEE-FedAgg mechanism attains the energy savings of 50% and 24% respectively for instrusion detection in iid and non-iid data distribution.
Thermal Robust Design Considerations for a Forced Convection Immersion Tank

Forced immersion cooling is an obvious next step from currently available passive immersion datacenter cooling. However, forced convection applications with parallel branches are known to be susceptible to interaction effects where a change in one flow branch causes changed behavior in the other branches. In forced immersion datacenter cooling this can mean that removing a server from a tank will interrupt cooling for the remaining servers, while such removal is foreseeable for reasons of repair and maintenance. CFD simulations on a case study confirm the risk. In the simulated case, removal of one server enclosure leads to 90% less flow and 30% less cooling performance in the remaining ones. A fully passive countermeasure is designed, consisting of a locally perforated plate that acts as an additional large flow resistance. With this in place the loss of cooling performance is limited to 0.5% for removal of one server, and to 5% over all possible empty slot configurations, at the cost of a larger pump and a small increase in pumping power. The case demonstrates that robust design against the effect of flow bypass in a forced convection immersion tank is both necessary and feasible. Tank level CFD simulations are key to find the correct layout and porosity of the resistance plate.
Transfer Learning-Based Methods for Prediction of Liver & Heart Diseases: A Review

To analyze and comprehend free text, machine learning, a subset of "natural language processing," may be employed. It can be wielded in conjunction with medical data to enhance hospital triage processes, forecast patient outcomes, and flourish diagnostic algorithms that can spot early-stage chronic illnesses. As there is a greater volume of patient data to analyze in critical care and patient death prediction is widespread, these solicitations may be highly helpful. Transfer learning, a subset of "machine learning," was flourished in addition to deep learning to address the issue of large datasets. Many different medicinal contexts have wielded it. This study aims to contribute a comprehensive analysis of the development and solicitation of transfer learning in "Natural Language Processing" (NLP) methodologies, with a focus on the analysis and forecasting of diseases, particularly heart and liver problems, using clinical pictures and free-text notes. It also examines how challenging it is for NLP techniques to comprehend clinical narratives and recognize features in medical images. The search parameters for "PRISMA, or Preferred Reporting Items for Systematic Reviews and Meta-Analyses", were implemented, and 31 papers were evaluated.
Zero Knowledge based Authentication for Internet of Medical Things

Internet of Things (IoT) applications, such as e-healthcare departments have grown tremendously where devices gather patient data and instantly transmit it over a distance to servers. Despite its huge advantages, IoT in the healthcare sector has acquired little consideration, largely because of the threats of unauthorized access to private health data made possible by the weak wireless communication channel. The economic complexity of the current security protocols makes them inappropriate, hence new security protocols must be developed for resource-constrained and diverse IoT networks. This work suggests an authenticated key agreement protocol for the Internet of medical things (IoMT) that is based on zero-knowledge proofs (ZKP). The research will include the comparison between computational delays in traditional protocols and ZKP protocol. This paper provides a protocol to obtain privacy of data in IoMT environment on the basis of zero knowledge phenomenon and anonymous communication by ZKP.
Comparative Analysis of Traditional Machine Learning and Deep Learning Techniques for Facial Expression Recognition

Facial expression recognition (FER) serves as a rapidly developing and important research space in the domain of pattern identification. Nonverbal communication plays a big part in daily life, and it accounts for roughly 55% to 93% of all communication. Robotics, video footage, expression recognition, home automation, electronic games, patient monitoring, mood disorders and anxiety diagnosis, psychoanalysis, and paralinguistic communication are all effective applications of facial expression identification. A thorough analysis of FER is provided in this paper. The literature is compiled from a variety of credible studies that were released during the most recent ten years. In this review, a variety of deep learning techniques are employed in addition to traditional machine learning methods. In addition, a number of freely available FER datasets are analysed, and standard outcomes are contrasted with them. In order to point out the subsequent research needed in this field for future researchers, this study provides an in-depth analysis of FER using conventional machine learning and deep learning strategies. Finally, by providing an overall comprehension of the latest cutting-edge techniques, this research study serves as a manual that is extremely beneficial for both experienced researchers looking for fruitful directions to pursue in the future and academicians in the FER field.
Investigation of YOLO models in the detection and classification of multiple negative road anomalies

Negative-road anomalies like potholes, humps, cracks present on the road surface impart a serious discomfort to drivers and passengers. Moreover, in such a road condition if a vehicle or pedestrian suddenly cross the road it becomes a significant challenge for the driver to drive safely. Early and accurate detection of multiple negative anomalies present on the road surface is crucial to prevent road accidents and for the timely repair of roads. Conventional approaches are time consuming and lack in detecting multiple anomalies. Owing to the advantage of detection of multiple objects in an image or video, computer vision (CV) based object detection algorithms are best suited for timely detection of negative anomalies. Herein we present a comparative analysis of nine different versions of the state-of-the-art object detection models (ODMs): you only look once (YOLO) in detecting multiple negative anomalies. To evaluate the performance of the ODMs, metrics such as precision, recall, F1 score and mean average precision (mAP) are measured. The frameworks are experimented using 3391 road images including potholes, speed breakers, vehicles, and humans on different road surface conditions. Upon studying the performance of all the nine variants of ODMs i.e., YOLOv5s, YOLOv5m, YOLOv5l, YOLOv5x, YOLOv6s, YOLOv6m, YOLOv6l, YOLOv7 and YOLOv7x, we observed that YOLOv6s is outperforming others resulting in mAP: 71.2 %, precision: 74.9 %, recall: 66 %, and F1 score: 70.2 % for a reasonably small image data set containing multiple negative road anomalies. YOLOv6s took 2 hours 44 minutes for training the dataset and tested images in 0.9 ms.
Deep Learning based Spectrum predictions for Cognitive Healthcare system

The prevailing pandemic scenario and the constant explosion of wireless devices lead to demand for a large spectrum. There is a crucial need for regular supervision of patient's health in these dire situations of a pandemic. This demands a smart solution for healthcare based on the latest technologies. This research work presents a detailed review of the latest advancements in the context of smart healthcare and medical imaging. This research work also proposes a novel framework for Cognitive healthcare based on Deep Learning. A unique spectrum prediction model has been proposed that combines the Grey model and Long Short Term Memory functionalities and allocates spectrum to the patients according to their risk classification (high, medium, and low). This research work describes the distinct novel applications of Cognitive radio, Deep learning, and the Internet of things in the Smart healthcare and Medical Imaging context. The proposed LSTM-Grey model has also been compared with existing spectrum sensing methodologies based on LSTM and CNN. All simulation results based on detection probability, false alarm probability and Accuracy of prediction indicate that LSTM-Grey model outperforms the existing solutions for healthcare system. The unique solution proposed based on the Grey model and LSTM for spectrum prediction enables transmission of medical data on free spectrum slots for real-time supervision of patient's health. The Deep Learning-based Cognitive Healthcare system is a promising candidate for Smart Healthcare supporting swift diagnosis, tracking, and patient health supervision.
Regularized Fusion of CNN with Transfer Learning for Facial Emotion Recognition

Emotional states of a person are hard to predict accurately by machines. Facial emotion recognition (FER) has become one of the prominent areas of study, because of its various applications. The objective of this work is to enhance the accuracy of facial emotion recognition by employing regularization and fusion techniques when the profile view images, in addition to frontal view images, are considered. The fusion model (Fnet), wherein the network utilizes pre-trained models; VGG-19 and Densenet-121 has been proposed. The network we proposed is not just the concatenation of the aforementioned networks, but the fusion of all the outputs obtained; output from the VGG-19, the Densenet-121 and the Sumnet (model formed by concatenating VGG-19 and Densenet-121). The significance of this proposed network, Fnet lies in the fusion loss (FL) we developed, which aids in optimizing the model during training. The network achieves the highest test accuracy of 98.98% within 30 epochs of training. The research utilizes the KDEF dataset, consisting of 4900 images across 7 emotion classes. The study demonstrates the effectiveness of regularization and fusion techniques in enhancing facial emotion recognition accuracy, thereby improving human capabilities in perceiving and responding to emotional signals.
Using Deep Neural Nets for Flower Classification

With the growing influence of Machine Learning, Image classification is a topic of interest for many. Flower Classification remains to be a hurdle which is yet to be crossed in this topic. The nature of flower image datasets, with indifferentiable backgrounds, high inter-class commonalities and intra-class dissimilarities makes the process complex and demanding. Our research employs the transfer learning approach by training a pre-trained ImageNet image classifier, VGG-16 using TPU. The TPU, due to its cores, speeds up the process of training and is a part of various large-scale classifiers. The epoch size and learning rate have been chosen to favour the process. The results achieved were gratifying with an accuracy rate of 94.1%. This work holds a scope in the future which include automation using an application. Our study represents a significant step forward in flower classification using machine learning, with implications for fields such as agriculture, botany, and ecology.
Machine Learning Techniques for Fake News Detection in Low-Resource Hindi Language: A Comparative Study

The dissemination of fake news is a key concern as it can lead to serious negative consequences. It has the potential to sway the minds of the public which passively consumes their information without verifying its content. This happens due to an abundance of unverified sources available online, which accelerates the spread of fake news. It occurs primarily through regional languages, where minimal fact-checking is done. There is a dire need to identify these news articles and stop the propagation of misinformation through these platforms. Due to a scarcity of resources for detecting false news in Hindi, the creation of a comprehensive, high-quality dataset was necessary. This paper presents a benchmark dataset of 15355 fake news articles and 18849 real news articles, as well as a Supervised Machine Learning technique for detecting Hindi false news using grammatical features. As Support Vector Machine(SVM) is one of the most sophisticated yet simple-to-implement algorithms, different variants of SVM were compared. Ensemble learning techniques such as Adaboost were also explored. With an accuracy of 87.71%, linear SVM fared significantly better than all the other variants. Adaboost had the best performance with an accuracy of 90.1%.
Long-Range Stealth Mode Wi-Fi Surveillance and Interception System

The ubiquitous presence of the Internet in all aspects is a necessary component of modern-day living. It is very easy to access the Internet through free public Wi-Fi, which can be found in various places such as malls, restaurants, cafes, and hotels. Public Wi-Fi hotspots play a dominant role in many illegal activities on the Internet. Monitoring and eavesdropping systems are required to sure veil users on the network and sniff their data traffic. A Wi-Fi surveillance system is used to intercept traffic between a user and the Wi-Fi access point (AP). Such systems are used by law enforcement agencies to spy on the data of criminal suspects while they are connected to Wi-Fi networks. Existing systems that have hitherto been commercially available, can operate within a few feet of the operator to the target and are large in size. This impacts portability, making the operator more vulnerable to attacks by suspects or unable to connect devices to Wi-Fi or the chance of suspects getting evaded. In this research, we build an interception system using Wi-Fi Pineapple, which can create rogue APs by scanning distant Wi-Fi networks with custom directional antennas and intercepting traffic from targets.
An approach to Arduino Uno-based smart car parking system

Since both the population and the number of cars in Bangladesh are growing quickly, a modern parking infrastructure is crucial. Since there aren't enough places for people to park their cars efficiently, more and more drivers are having to leave their cars parked on the road, contributing to congestion and a halt in the flow of traffic. Only approved users are able to park their vehicles in our automated parking, making it a secure and convenient option for car storage. When there is less traffic on the road, there is less pollution from the cars and trucks on that route. There is no possibility of paying a bribe to park a car since the whole system is computerized. Better infrastructure can be built since all the money will go to the government.
Securing the Future of IoT: A Comprehensive Framework for Real-Time Attack Detection and Mitigation in IoT Networks

Increased security complexity and variety come with IoT devices. Existing security solutions generally fail to handle multiple difficulties, particularly in real-time attack detection and response. This research introduces a novel, comprehensive architecture that synergistically incorporates various cutting-edge security measures to protect IoT networks from all cyber threats. The proposed framework incorporates NodeMCU ESP8266-based real-time response, safe boot, hardware-based security modules, and zero trust architecture. It recommends incorporating blockchain for security, real-time logging and monitoring, machine learning-assisted intrusion detection, and indicators of compromise (IOCs) for incident response. For improved malware analysis and threat mitigation, integration of APIs for smooth communication with third-party services such as VirusTotal and Hybrid Analyzer is suggested. This paper also discusses data encryption, updates and patching, identity and access management, risk assessment and threat modelling, incident response and recovery, and privacy. This holistic approach to IoT security provides a robust, flexible, and scalable framework to address the changing threat landscape and growing complexity of IoT ecosystems. The proposed methodology defends IoT networks against sophisticated cyberattacks through careful analysis and empirical validation. In an increasingly interconnected world, this research could be useful for practitioners, academics, and stakeholders aiming to strengthen IoT infrastructure resilience.
Stroke prediction analysis using federated machine learning

Stroke is one of the main cause of disability across the world. A stroke can be treated with the right medicine and recovered if it is detected early enough. In the existing method a stroke is predicted utilising various machine learning algorithms such as random forest, AdaBoost, stochastic gradient, decision tree, logistic regression, and naïve bays classifier etc. There may be drawbacks such as scalability problems, no security and minimum accuracy. The minimum accuracy, privacy problem and scalability problem has been overcome by the proposed method. The three datasets are chosen two from the internet and one from hospital which is the real time dataset which includes ten features. Each dataset has maximum values which results in better prediction of accuracy. The stroke dataset is taken and the data are pre-processed to overcome the missing values in the dataset. The Logistic Regression model is built in a federated way while keeping track of the training loss and the training accuracy. Three datasets are taken and simulated as three hospitals using Pysyft by using the abstraction of the Virtual Worker object; there could be more hospitals. The Model is shipped to individual dataset and it is trained with the local data present. The three part weights are sent to the model , which averages all the part weights and the averaged weights is sent to the 4 parts at the start of each iteration . It has been done for five iterations and ten epochs. Finally the testing has been improved and the scalability problem has been solved since the dataset is split into three parts and the privacy problem has been overcome since a federated model has been used which is the privacy concern model. In the existing method random forest gives an accuracy of 96% and in the proposed method the accuracy has been improved to 98.8 %.
Weighted Adaptive Spectrum Sensing for Cognitive Vehicular Networks

Vehicular Network is an advanced application which enables smarter management of traffic and ensures safer, and more coordinated travel. The US Federal Communication Commission (FFC) has designated 75MHz of spectrum for vehicular communications (V2V and V2I). As the number of vehicles on the road increases, the spectrum available for vehicular networks becomes congested. Spectrum scarcity is a major problem in vehicular networks which needs to be addressed for improved performance. Cognitive Radio (CR) technology is a promising solution which is used to solve the scarcity of the spectrum through Dynamic Spectrum Access (DSA). CR technology integrated with Vehicular Networks (CVN) aids in the complete exploitation of underutilized licensed spectrum. Sensing the state of the channel is a fundamental step in CR networks which is carried out by the CR/Vehicular Users (VUs). However, sensing of spectrum is a challenging issue due to the continuous movement of vehicle (mobility), which results in a dynamic network topology. These challenges make the decision made by an individual VU uncertain. In the proposed work, we introduced a weight based adaptive cooperative sensing approach to enhance channel detection probability in Cognitive Vehicular Networks. The weights are calculated by considering certain parameters which includes probability of VU being outside the protection range of the PU, probability of PU being within the sensing range of the VU, Signal to Noise Ratio (SNR), velocity of the VUs, distance between VU and PU and the time of sensing. The calculated weight for the decision made by VU indicates the reliability of the signal received. A global decision is made by combining the weighted individual decisions received from VUs by applying the majority fusion rule. In addition to this, a reward-based channel recommendation for sensing is done to make the system more viable for wideband sensing. The rewards to the channels are given based on the previous states of the channels. The computational results suggest that the proposed technique outperforms conventional spectrum sensing approaches.
CNN and Transfer Learning Modeling for Jujube Spices Recognition

Jujube make up a major portion of Bangladesh's total fruit production. It might be challenging to tell the differences between the many different species of jujube. The manual examination of jujube' physical qualities, which is time-consuming and prone to human mistakes, is the method of identification most commonly used in traditional methods. In this investigation, we make use of computer vision methods to zero in on particular jujube types that are native to the Bangladeshi region. In our approach, the question is solved with the assistance of a deep convolutional neural network (CNN) and Transfer Learning. Our method obtains an outstanding 98.0% accuracy on a test dataset after being trained on photographs of jujube taken in and around Bangladesh. Our work contributes to the growing body of research on applying computer vision and deep learning techniques to agricultural problems. Further research can be conducted to improve the accuracy of our system by collecting a larger dataset of jujube images, exploring the generalizability of our system to other regions and countries, and investigating the potential for using our system to recognize other fruit crops in Bangladesh or other countries.