Miracle-dz/UniFAR-FTUs huggingface dataset:
Precise Detection of Tomato Leaf Diseases using Deep Learning Approach with EfficientNet

This research paper presents a comprehensive solution for the precise identification of tomato plant diseases using deep learning techniques. The primary objective is to develop a real-time model capable of accurately identifying a broad spectrum of diseases present on tomato leaves thereby allowing for opportune interventions to mitigate extensive crop losses. Utilizing the capabilities of the EfficientNet-B1 architecture this method enables the classification of fifteen distinct disease categories including healthy leaves. Utilizing the publicly accessible PlantVillage dataset in conjunction with diligent data preprocessing ensures robust training and validation of the model. Using a meticulous five-fold cross-validation strategy, the performance of the model is thoroughly evaluated. Precision, recall, F1-score and accuracy metrics consistently exceed 98% and in some cases exceed 99%. The exceptional level of accuracy exhibited in this study exceeds past research endeavors hence showcasing the effectiveness of the presented methodology. Utilizing the capabilities of deep learning, this methodology substantially enhances the effectiveness of disease management, leading to healthier crop yields, increased food security and more sustainable agricultural practices. However, the prospective application of the developed model in the real world has the potential to revolutionize precision agriculture and the identification of plant diseases. The systematic method, meticulous evaluation and exceptional precision of the paper all contribute to its significant impact in the field.
Efficient weight-based segmentation of scan-chains using NLFSR

This paper presents an efficient Built-in self-test (BIST) module using a Non-Linear Feedback Shift Register (NLFSR) and Weight based segmentation of scan-chains. The proposed technique reduces the test application time and consumption of power of the BIST module by segmenting the scan-chain into multiple scan-chains by grouping the cells of the scan chain having similar weights (logic 1's and 0's). Every scan- chain thus grouped will be given test patterns through the combinational circuit. Pseudo-random Number Generator (PRNG) generates test patterns for testing the circuit under test (CUT). By using NLFSR as PRNG the power consumption is reduced. The weight calculations and NLFSR are simulated and synthesized using Xilinx Vivado. Experiments are carried over on the ISCAS89 benchmark circuits on the Fsim tool.
Joint VNF Decomposition and Migration for Cost-Efficient VNF Forwarding Graph Embedding

Network Function Virtualization (NFV) enables the decoupling of network functions from dedicated hardware to run them as software instances on commodity servers through virtualization, replacing hardware-based network functions with software-based Virtual Network Functions (VNFs). In this paper, we study the joint problem of VNF decomposition and migration to address VNF embedding in NFV resource allocation (NFV-RA). More specifically, we investigate how VNF migration and VNF decomposition can be mutually beneficial to minimize the embedding cost of network services. After presenting a novel formulation of the problem as an integer linear programming (ILP), we validate it by CPLEX and show that our joint VNF decomposition and migration approach can outperform the VNF decomposition-only approach by 20% in terms of embedding cost.
Developing a Bangla Handwritten Text Recognition Framework using Deep Learning

There are 250 million speakers of Bangla, the second most common language on the Indian subcontinent, making it one of the most significant tongues in the world. In this highly populated subcontinent of over 300 million people, Bangla is the sixth most utilized language in the world. Despite its popularity, the slow progress in identifying handwritten Bengali writing is depressing. Bangla handwritten script’s complexities, which include compound characters, intricate punctuation, and cursive writing styles, provide difficult hurdles. Recent breakthroughs in machine learning, particularly Convolutional Neural Network (CNN)-based architectures, have shown amazing accuracy in Bangla Handwritten Text Recognition. This study aims to provide a fresh and distinct Bangla handwritten text recognition model by using the capabilities of the "Bangla Handwriting Dataset. " This extensive dataset includes 260 single-page handwritten samples from people of all ages and personalities. The word labels and boundary boxes in the dataset were meticulously created. This research emphasizes using cutting-edge tools and technologies, uniting them under a unified framework. Among the different CNN-based architectures investigated, DenseNet121 outperforms competitors such as Xception, MobileNet, MobileNetV2, and NASNetMobile for text recognition. Furthermore, when combined with a GRU RNN layer, DenseNet121 obtains the most remarkable test accuracy of 95.643%. A prototype with a user interface has been created to improve accessibility and user-friendliness. This program quickly scans photos of manuscripts, interprets each word, and turns them into editable text. This comprehensive technique for recognizing Bangla handwritten text promises to open new channels of communication and information access in a linguistically varied and technologically linked society.
A Machine Learning Framework for Robust Epileptic Seizure Detection from EEG Sign

Millions of individuals throughout the world suffer from epilepsy, a neurological illness characterized by frequent seizures. To improve patient safety and quality of life, seizure detection must be timely and reliable. With the possibility for realtime monitoring and intervention, machine learning has recently become a promising tool for automated seizure detection. This paper provides a thorough analysis of recent developments in machine learning-based epileptic seizure identification. We talk about the transition from using conventional feature engineering techniques to applying deep learning algorithms. Additionally, we consider the function of publicly accessible datasets and assess standard performance indicators. The difficulties and unsolved issues in the field, including inter-subject variability and realtime application, are also highlighted in this review. This paper synthesizes the literature and offers useful insights for researchers and practitioners striving to enhance the precision and effectiveness of epileptic seizure detection systems, thereby enhancing patient care and management.
Rainfall Presage Forecast Using Machine Learning Approach

Rainfall prediction is vital for various applications such as agriculture, flood control, and water resource management. Accurate rainfall prediction can help farmers to make decisions about irrigation and crop selection. It can also help authorities to take necessary measures to prevent or mitigate the impact of floods. In the proposed study, multiple algorithms are examined and compared in terms of how accurately they predict rainfall. The accuracy of rainfall forecasts may be increased by using machine learning algorithms, which have shown considerable promise in predicting rainfall patterns. The authors solely used the AUC score and categorial standards to analyze six ML algorithms. Based on their accuracy score, AUC, precision, recall, and f1 score, authors compared algorithms including Random Forest, K-Nearest Neighbours (KNN), Logistic Regression, Cat Boost, Naive Bayes, and Gradient Boost. Since they provide a useful solution for categorization and prediction of the amount of rainfall as well as precise rate prediction, the applied algorithms are supervised learning algorithms.
CIS2VR: CNN-based Indoor Scan to VR Environment Authoring Framework

We present CIS2VR (CNN-based Indoor Scan to VR), an authoring framework designed to transform input RGB- D scans captured by conventional sensors into an interactive VR environment. Existing state-of-the-art 3D instance segmentation algorithms are employed to extract object instances from RGB- D scans. A novel 3D Convolutional Neural Network (3D CNN) architecture is used to learn 3D shape features common to both classification and 3D pose estimation problems, enabling rapid shape encoding and pose estimation of objects detected in the scan. The generated embedding vector and predicted pose are then used to retrieve and align a matching 3D CAD (Computer-Aided-Design) model. The aligned models, along with the estimated layout of the scene, are transferred to Unity, a 3D game engine, to create a VR scene. An optional human-in-the- loop system allows users to validate results at various steps of the pipeline, improving the quality of the final VR scene. We evaluate and compare our approach to existing semantic reconstruction methods on key metrics. The proposed approach outperforms several existing methods in object alignment, coming close to the state-of-the-art, while speeding up the process an order of magnitude. CIS2VR takes an average of 0.68 seconds for the entire conversion across our test dataset of 312 scenes. The code for the proposed framework will be made publicly available on GitHub.
Deep Learning-Enabled Squirrel Detecting Prototype Development and Processing Analysis

In this paper, the authors present the development of an object detection prototype aimed at detecting living objects in front of vehicles. The proposed and implemented prototype focuses on detecting squirrels and alerting the driver to avoid collision. This can be further extended to other small animals. The prototype uses transfer learning to leverage pre-existing knowledge from a larger, more robust model and apply it to a smaller, more specific task. The model is able to reach an accuracy of greater than 95% on color images with the help of this approach. The detection models are designed to be implemented on small, low-power devices integrated into vehicle electronics that are useful for object detection. The paper further focuses on a detailed comparison of different compression techniques to reduce the size of the data required for classification, enabling the system to run more efficiently and effectively. Further, the drop in accuracy and the total inference time are compared for different compression techniques. The study uniquely compares the different image compression and inference processing techniques which can be used as a base for future studies.
An Efficient Energy Consumption of a Miner Node in a Local Blockchain Network

A miner node in a blockchain network is important in validating and incorporating transactions into the blockchain ledger. The mining process demands a good amount of computational power, resulting in high energy consumption. Energy usage has notable environmental consequences, notably in the form of increased carbon emissions, which contribute to global warming and climate change. Existing research works evaluate the energy consumption of the entire blockchain network, encompassing factors like total network energy and the number of nodes. There is a requirement for research work specifically addressing miner nodes within local blockchain networks. In this paper, we present an approach to assess the energy consumption of miner nodes in local blockchain networks. Our analysis considers several key factors that influence energy utilization, including computational power (hashrate), power consumption, and electricity costs. To obtain precise data, we established a local blockchain network where we measured the hashrate and power consumption of the miner node. This empirical data helps us understand the exact amount of energy consumed during miner node activities. In our experiments, we used the Ethereum network to compute the energy consumption associated with various transaction types. Our proposed approach enhances the efficiency of blockchain applications. Also, it solves the environmental impact, contributing to the creation of a more sustainable and eco-friendly blockchain network.
Design and Analysis of a Scan Chain in Subthreshold Region

Testing of manufactured Integrated Circuit (IC) is performed using design for testability (DFT) techniques such as scan chain which is most popular in sequential circuits. The scan cell involves the modification of a D flip-flop (DFF) with a multiplexer at its input. During testing, a pattern is applied through the scan input pin (SI) in which individual flip-flops toggle their values as the test patterns are shifted in hence a significant amount of power is consumed in scan chain. Although moving to a lower technology node decreases the power consumption in a circuit, a further drastic reduction (i.e. 106 order) in power consumption is obtained by operating the circuit in the subthreshold region. In this work scan chain is designed to operate correctly in the subthreshold region using suitable device sizes, using both transmission gate (TG) based and true single phase clocked (TSPC) logic for 16, 22 & 32 nm technology nodes. Further, their average powers are compared. In addition, the Monte Carlo simulation and comparative analysis are performed to study the effect of variation of power supply and temperature.
Hybrid Feature Selection Techniques in Intrusion Detection System

Network and information security have become difficult problems as a result of the network's phenomenal expansion. The main aim of intrusion detection is to detect and prevent security flaws in information systems. Intrusion detection system works with a vast volume of data. The processing time required by intrusion detection systems is high and detection rate is poor if all the features are used. Therefore, these unnecessary and redundant elements must be removed in order to improve intrusion detection system effectiveness. Three hybrid feature selection strategies are utilized in this study to choose features. K-Nearest Neighbor, Support Vector Machines, and Genetic Programming are used to analyze and evaluate the performance of selected features.
Evaluation of Image Inpainting Methods for Face Reconstruction of Masked Faces

In the field of facial recognition, recognizing masked faces presents a persistent challenge, with significant implications for various industries. Image inpainting emerges as a potential solution, aiding in the reconstruction of obscured facial features. Typically used to restore old or damaged images or remove unwanted elements, inpainting's application to masked faces is notably complex. In this study, we compare three cutting-edge image inpainting models: PatchMatch, Edge Connect, and Free-form image inpainting with gated convolution, assessing their performance in reconstructing masked faces. While these models excel at recreating natural scenes and objects, generating visually convincing unmasked faces from obscured images proves demanding. Our evaluation is based on two synthetic datasets we curated, MaskedFace-CelebA and MaskedFace-CelebA-HQ, containing masked facial images. Notably, the Gated Convolution model outperforms the other two in reconstructing facial images compared to their original counterparts. Furthermore, we employ a simple feed-forward neural network, the Extreme Learning Machine(ELM) classifier, to gauge the classification accuracy of reconstructed and ground truth images. The classifier's results substantiate both quantitative and qualitative image quality assessments.
Cross-Subject Motor Imagery EEG Classification using Deep Learning

Brain Computer Interface (BCI) has gained a lot of attention recently due to its ability to assist people with neurological disabilities. Motor Imagery (MI) Electroencephalogram (EEG) signals are useful in BCI systems due to their high temporal resolution, which facilitates real time performance. One major limitation of BCI systems is the difficulty in obtaining generalized models that are independent of intra-subject and cross-subject variability. In this work, an approach that uses an Autoencoder for feature extraction followed by a Convolutional Neural Network (CNN) classifier is employed for improving the cross-subject generalization. The proposed method is validated using the Physionet EEG Motor Movement/Imagery Dataset, achieving an accuracy as high as 86.7% for cross-subject analysis.
Neurosymbolic Value-Inspired Artificial Intelligence (Why, What, and How)

The rapid progression of artificial intelligence (AI) systems, facilitated by the advent of large language models (LLMs), has resulted in their widespread application to provide human assistance across diverse industries. This trend has sparked significant discourse centered around the ever-increasing need for LLM-based AI systems to function among humans as a part of human society. Toward this end, neurosymbolic AI systems are attractive because of their potential to enable and interpretable interfaces for facilitating value-based decision making by leveraging explicit representations of shared values. In this article, we introduce substantial extensions to Kahneman’s System 1 and System 2 framework and propose a neurosymbolic computational framework called value-inspired AI (VAI). It outlines the crucial components essential for the robust and practical implementation of VAI systems, representing and integrating various dimensions of human values. Finally, we further offer insights into the current progress made in this direction and outline potential future directions for the field.
ADEPTS: An Advanced Deep Ensemble and Progressively Training Strategy for High-speed Shuttlecock Localization

In recent times, integrating sports events with deep learning architectures has attracted significant attention, resulting in an increasing demand for applications in this field. In regards to sports such as shuttlecock or tennis, the precise monitoring of player and ball positions holds significant importance. This task is indispensable for a thorough understanding of the game’s current state, serving both coaches and players. However, fast and unpredictable behavior makes extracting representative features a challenging issue that remains to be solved. To address this problem, we propose Advanced Deep Ensemble and Progressively Training Strategy (ADEPTS), which is an optimized training strategy designed for shuttlecock detection systems. ADEPTS combines multi-scale feature fusion and the progressive learning approach, allowing networks to capture the trajectory features of high-speed ball movement more accurately while improving training efficiency. Experimental results show that ADEPTS can significantly reduce training time by about 26.67% with high-resolution outputs. Additionally, it makes the network achieve even better localization accuracy, which makes it a practical and effective solution for real-world applications.
A Compressive Sensing Approach for MIMO-OFDM-Based Integrated Sensing and Communication

Future communication networks will integrate the sensing functions, requiring the system to utilize limited radio resources to simultaneously achieve high-throughput communication and high-precision sensing. Compressive sensing technology is of great potential for such applications. In this paper, we design an efficient multiple-input multiple-output (MIMO) - orthogonal frequency division multiplexing (OFDM) integrated sensing and communication (ISAC) system based on compressive sensing. Specifically, we obtain the delay-Doppler and angle of departure (AoD) / angle of arrival (AoA) measurements by exploiting the sparse and random time-frequency resource allocation patterns and array structures. Our approach leverages the sparsity of environmental information and employs the Kronecker method to construct a compressive measurement matrix with Vandermonde structure. Our method provides high-resolution performance comparable to Nyquist sampling, while significantly reducing the usage of time-frequency resource units and the number of antennas, without introducing excessive additional hardware links for sensing and computation. Numerical simulations demonstrate the feasibility of the method, and our results indicate that compressive sensing recovery algorithms outperform traditional method with a lower probability of recovery errors and better robustness.
A Trust Score calculation approach for Zero Trust Access System

Zero trust is a cybersecurity approach that focuses on cautious and continuous verification. Traditional trust presumptions are discarded in a zero trust scenario, and continual verification becomes necessary. This paper introduces a weighted average-based trust evaluation method specifically designed for zero trust environments. The approach incorporates multiple factors and assigns appropriate weights to these factors and calculates a trust score. A user or device is allowed access to a resource only if the calculated trust score is above a predefined threshold value. This method guarantees a comprehensive assessment of users and devices prior to granting them access to sensitive resources, and any weaknesses will result in a decrease in their trust score. The weighted average technique enables organizations to customize the trust evaluation process to their specific zero trust requirements through its capacity for openness, adaptability, and customization. The methodology offers a useful and effective way to evaluate trust in a dynamic and constantly changing security environment.
Recommendation of Tourist Attractions and Route Based on Social Network Data Mining

Since growing travellers’ more and more preferring customisation service, this project indicates that such application is more user-oriented and more particular in personal preferences. Through extracting users’ posted geotagged photos and their information from photo sharing website supported by Flickr API. And we design a content filtering algorithm and improve existed TF-IDF algorithm to get user profile before listing travel suggestions via Yelp API and simulated a graphical route for reference by Google API. As a result, a travel recommendation system based on users' travel preferences excavated from social platform photos is achieved. According to the average accuracy rate (MAP) verification, the CFC algorithm used in this study performs better than the LC and CA algorithms in recommending popular scenic spots.
Eyes Disease Detection Using Deep Learning Methodologies

Our eyes are fundamental part of our daily lives, enabling us to engage and interact with the surroundings. Our eyes serve as vital sensory organs, providing us with a wealth of information about the surrounding world. The growing occurrence of eye diseases resulting in vision impairment and blindness highlights the vital importance of promptly identifying and diagnosing these conditions to facilitate efficient therapeutic interventions. This research utilizes Convolutional-Neural-Networks (CNNs) to scrutinize images, extracting distinctive features that enable precise identification of diverse eye conditions. Recent progress in the field of CNNs has yielded promising results, showcasing their efficacy in identifying and diagnosing diverse eye conditions including bulging eyes, crossed eyes, uveitis, glaucoma, and cataracts. The utilization of Convolutional Neural Networks (CNNs) for detecting eye illnesses can enhance the precision, velocity, and effectiveness of diagnostic processes. This advancement enables earlier intervention and contributes to more favorable outcomes for patients. The remarkable precision exhibited by the model in diagnosing eye diseases underscores the effectiveness of incorporating transfer learning, specifically with pre-trained models like ResNet50. Employing CNNs in conjunction with transfer learning, particularly utilizing models such as ResNet50, has emerged as a successful strategy for identifying and diagnosing various eye conditions. This approach not only contributes to improved patient outcomes but also helps prevent the progression of more severe vision problems by accurately detecting and diagnosing eye illnesses.