Miracle-dz/UniFAR-FTUs huggingface dataset:
Precision Agriculture using Internet of Things and Machine Learning

The agriculture industry is facing a significant challenge due to poor water management, resulting in a considerable decline in crop production. A lot of water used in irrigation is wasted due to inadequate management practices. The inefficient irrigation systems often have poor water flow control, leading to over-extraction of groundwater, which can lead to harming crop yield. To tackle this problem, precision agriculture, which leverages technology to optimize crop production while minimizing waste and environmental harm, has emerged as a viable solution. IoT-based smart irrigation management systems are an integral part of this approach and can play a vital role in achieving water efficiency goals. In this context, an open-source intelligent system that utilizes machine learning-based algorithms and monitors parameters of soil, such as its moisture and temperature, has been developed to predict the irrigation needs of a field. By forecasting the amount of water required by crops, this system can ensure that the water is utilized more efficiently. The results of the system, which has been successfully deployed on a demo scale, are very promising. The smart system has the potential to revolutionize the way irrigation is managed and help address the water efficiency challenges facing the agriculture industry.
Hybrid Deep Learning for Wheat Bunt Disease Severity Assessment

Wheat farming, a fundamental aspect of world agriculture, is continually put in jeopardy by illnesses like wheat bunt, which are brought on by the fungal infections Tilletia caries and Tilletia foetida. It is essential to have an accurate assessment of the severity of a disease to practice educated disease management and maximize crop output. In response, the current research presents a novel method for classifying the severity of wheat bunt disease. The proposed study has painstakingly curated a comprehensive dataset consisting of 15,000 high-resolution photos that cover six different severity levels. This provides a broad depiction of many real-world scenarios. By utilizing this dataset, the suggested work offers a unique hybrid model that combines the interpretability of a decision tree (DT) with the feature extraction capabilities of Convolutional Neural Networks (CNN). Our hybrid model was successful in categorizing illness severity levels, as evidenced by the fact that it attained an amazing overall accuracy of 93.2%. The layer-by-layer parameter analysis performed on the model demonstrated a well-structured architecture, and the precisionrecall study highlighted the model's equilibrium between accurate classification and recall of information. In analyses that compared it to models that are considered to be state-ofthe-art, our model displayed a performance that was competitive with those models, showing its potential as a trustworthy instrument for determining the severity of wheat bunt disease. The versatility of the model places it in a position to make broader contributions to the field of precision agriculture, in addition to its immediate applications. This research not only provides a reliable solution for the classification of the severity of the wheat bunt disease, but it also contributes to the development of precision agriculture by providing a flexible framework that can be extended to perform the classification tasks associated with other plant diseases. Our hybrid model promises to assist agricultural practitioners in their pursuit of sustainable and resilient crop production systems thanks to its high overall accuracy.
Energy-Aware Tile Size Selection for Affine Programs on GPUs

Loop tiling is a high-order transformation used to increase data locality and performance. While previous work has considered its application to several domains and architectures, its potential impact on energy efficiency has been largely ignored. In this work, we present an Energy-Aware Tile Size Selection Scheme (EATSS) for affine programs targeting GPUs. We automatically derive non-linear integer formulations for affine programs and use the Z3 solver to find effective tile sizes that meet architectural resource constraints, while maximizing performance and minimizing energy consumption. Our approach builds on the insight that reducing the liveness of in-cache data, together with exploiting automatic power scaling, can lead to substantial gains in performance and energy efficiency. We evaluate EATSS on NVIDIA Xavier and GA100 GPUs, and report median performance-per-Watt improvement relative to PPCG on several affine kernels. On Polybench kernels, we achieve 1.5 × and 1.2 × improvement and obtain up to 6.3 × improvement on non-Polybench high-dimensional affine kernels.
Convolution Driven Vision Transformer for the Prediction of Mild Cognitive Impairment to Alzheimer’s disease Progression

Alzheimer’s disease (AD) poses a significant challenge to global health, affecting over 50 million individuals worldwide with no current cure. Early identification and treatment are vital to mitigate its impact on memory, language, and actions. Recent strides in artificial intelligence (AI) and neural networks, particularly Vision Transformers (ViT), show promise in enhancing early AD diagnosis through advanced medical image analysis. ViT, surpassing conventional Convolutional Neural Networks (CNNs), proves adept at discerning subtle changes in brain structure and function, crucial for early AD detection. However, ViT models face challenges like iterative attention biases hindering effective target class focus. Our proposed solution incorporates an augmenting method and convolution patch embedding, addressing these limitations, and improving patch extraction efficiency while preserving original dimensions. Utilizing the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset, our comprehensive study focuses on stable and convertible mild cognitive impairment (MCIs and MCIc) to AD progression. The proposed method achieves a diagnostic accuracy of 91.35%, sensitivity of 91.09%, and specificity of 91.63%, surpassing existing methodologies. This highlights the practical viability and effectiveness of our approach in AD diagnosis. In conclusion, early and accurate AD detection is crucial for patient outcomes. Our innovative methods showcase the potential of ViT in brain image analysis, paving the way for future advancements in medical imaging research.
Outage Analysis of Single-Stage Relay NOMA Over Power Line Communication Under Impulsive Noise

In this paper, we study and present the performance based on the outage probability of a single-stage, relay-aided, cooperative non-orthogonal multiple access over power line communication under a Bernoulli-Gaussian impulsive noise. The network setup comprises one source node and two destination nodes, among which one of the nodes closer to the source acts as a relay, transmitting a symbol from the source node to the destination. The relay considered is a decoded-and-forward relay, which helps to transmit the source symbol to the destination over two-time frames. We derive the mathematical expressions to determine outage probabilities at the relay and destination nodes within the studied network. Furthermore, we develop an optimization problem related to the power distribution coefficient at the source, deriving the corresponding mathematical expressions. To validate our analysis, we employ simulations and numerical methods.
Efficient Brain Tumor Classification through Transfer Learning Models

One of the most dangerous diseases, brain tumors need a quick and accurate method of detection. Wide-ranging symptoms can be caused by brain tumors, and both diagnosing and treating them can be difficult. Neurologists and radiologists occasionally fail to find the brain tumor. The American Brain Tumor Association says, about 700,000 people are coping with brain tumors. Each year, more than 80,000 new cases are diagnosed. The goal of this work is to identify brain tumors from MRI images in order to prevent earlier death. Brain image classification is done using the DenseNet121 and EfficientNetB0 pretrained transfer learning model. DenseNet121 and EfficientNetB0 have been selected for brain tumor classification because of their robust performance in image analysis tasks, their pretrained knowledge from extensive datasets like ImageNet, and their computational efficiency. The experimental result shows that the proposed model performs better than the previous work model VGG16, Support Vector Machine (SVM), Discrete Wavelet Transform (DWT), Gray Level Co-occurrence Matrix(GLCM), Hidden Markov Model (HMM), and Convolution Neural Network (CNN). These previous models’ classification accuracy ranges from 94% to 96%, while the suggested model where we used 80% data for training and 20% data used for testing and average accuracies are 97.09% for DenseNet121 and 98.32% for EfficientNetB0. Also, the purpose of this work to categorize 4 classes in these 3 classes for tumor and 1 class for non tumor. Additionally, the proposed model’s precision, recall, and F1-score are all 97% and 98%, indicating very good performance. However, these techniques have shown a significant potential for improving the diagnosis and treatment of brain tumors by providing a quick, accurate, and non-invasive way to categorize different types of tumors.
Low-Complexity Downlink Transmission with NOMA for IRS-Aided Integrated Satellite-Terrestrial Network

This paper investigates a low-complexity downlink transmission to provide diverse services for different users in an integrated satellite-terrestrial network (ISTN). Specifically, given that the location information-based channel state information (LoI-CSI) of each user is available, we formulate an optimization problem to minimize the outage probability (OP) of the terrestrial network by jointly optimizing the transmit power and beamforming (BF) weight vector at the base station (BS), and the phase shift vector at the intelligent reflecting surface (IRS), while meeting the quality-of-service (QoS) requirement of the satellite network. To make the optimization problem tractable, we first use the LoI-CSI and propose a low-complexity BF algorithm to obtain the transmit power and BF weight vector. Then, employing IRS and non-orthogonal multiple access (NOMA) in the terrestrial network, we derive its closed-form expression for the OP of the terrestrial network, which is explored to calculate the IRS phase shift vector. Finally, simulation results confirm the validity of the theoretical formulas and reveal the proposed algorithms superiority in system performance.
Propionylation Characterization Ensembling Classifiers on Varied Features

A recently discovered post-translational modification (PTM) called propionylation where propionyl group is added in the lysine amino acid’s side chains causing diversity and evolution. This alteration occurs in both eukaryotic and prokaryotic cellular environments under a variety of circumstances. It plays a significant part in metabolic functions, the control of biomolecules and several biological processes with consequences for a number of unhealthy disorders. One approach is to use mass spectrometry methods that distinguish propionylation PTMs in a laboratory setting. However, this procedure is characterized by a slow pace and significant cost inefficiency. The development of computational models has the potential to replace traditional laboratory techniques, increasing its availability to users of all skill levels worldwide. We developed a simplified efficient technique that combines positional and evolutionary data from classical machine learning with VGG-16 and RNN architectures applied to semantic embedding features taken from natural language processing. We were able to obtain accuracy, sensitivity along with specificity and MCC of 96.53 %, 97.27 %, 94.56 % and 0.53 % respectively which fared better than the most recent state-of-the-art models built.
FedFaSt: Selective Federated Learning Using Fittest Parameters Aggregation and Slotted Clients Training

This paper proposes a novel selective federated learning (FL) algorithm, called fittest aggregation and slotted training (FedFaSt). It relies on a “free-for-all” client training process to score clients' efficiency while applying the “natural selection” principle to elect the fittest clients to be used in FL training and aggregation processes. While relying on a combined data quality and training performance metric for scoring clients, FedFaSt implements a slotted training model enabling teams of fittest clients to participate in the training and aggregation processes for a fixed number of successive rounds, called slots. Performance validation using X-ray datasets reveals that FedFaSt outperforms selective federated learning algorithms like FedAVG, FedRand, and FedPow in terms of accuracy, convergence to the global optimum, time complexity, and robustness against attacks.
Exploring the Performance of Fluid Antenna System (FAS)-Aided B5G mmWave Networks

Reconfigurability and innovative design approaches to radio frequency components and network infrastructure are critical for the development of future communication networks, particularly beyond 5G (B5G), which aim to support the proliferation of Internet of Things (IoT) devices. Leveraging its favorable performance characteristics and potentially low cost, the fluid antenna system (FAS) has emerged as a compelling solution, garnering significant interest due to its reconfigurability, small form factor, flexibility, and transparency. This paper presents a comprehensive analysis of FAS in the context of B5G networks, with a focus on its theoretical performance and practical implementations. By deriving formulas for the semi-infinite outage probability and ergodic capacity of FAS receivers in equally correlated Nakagami-m channels, we showcase the remarkable diversity performance exhibited by FAS receivers, even with a half-wavelength antenna size. Monte Carlo simulations are employed to validate our theoretical findings in terms of the number of antenna ports and transmission power.
A Survey of the Optimization System Road Damage Detection on CNN Model for Edge Device

Road damage survey problems are carried out conventionally. It is necessary to develop tools for automatically detecting road damage using technology and artificial intelligence (AI). The difficulty in detecting road damage is that it necessitates the use of an edge device to examine visual data while collecting road defect data in motion. However, quick detection of road damage objects is crucial because of edge devices' constrained processing and storage capacity. Due to the limited computation and storage capabilities of edge sensors, a road damage detection method with high accuracy and low detection time is required. In this research, we present an overview based on sensor cameras using CNN architectures that can be implemented on embedded targets to increase the performance of road damage detection for edge devices. As a result, it is necessary to develop CNN models of one-stage detectors, two-stage detector and a combination of both models to increase accuracy. Additional study is required to improve system performance in order to boost accuracy and inference time by combining tuning hyperparameter, pruning, and quantization methods.
Reinforcement Learning Based Energy-Efficient Routing with Latency Constraints for FANETs

Reinforcement learning (RL) enables flying ad-hoc networks (FANETs) to choose the next hop unmanned aerial vehicles (UAV s) with shorter routing path, but may raise the retransmission rate and fails to guarantee the quality of service (QoS) under the high mobility and fast fading channels. In this paper, we propose an RL based routing scheme that optimizes both the routing and the power allocation to protect the latency QoS and save routing energy consumption of the FANET. Based on the routing history, the channel conditions, the battery level and the shared knowledge from the neighbors, this scheme formulates the routing policy distribution with safe exploration to select the stable path and thus reduce the retransmission rate. Specifically, the risk value with respect to end-to-end latency constraint is designed to evaluate the routing policy and reduce the exploration probability of the high-latency routing. Based on the distributed value function approach, the learning parameter such as the state value functions shared among neighbors is exploited to accelerate the routing process and enhance the routing stability under the dynamic network topology. Simulation results verify the routing performance gain of our proposed scheme over the benchmark.
The role of human activity in decreasing ecologically sound land use in China

Ecologically sound land (ESL) represents a meaningful geographical spatial unit that provides ecological goods and services at various scales. However, few studies focusing on national‐ or regional‐scale changes in ESL use and the degradation of the land ecosystem in China have been based on reliable and up‐to‐date land survey data. The study aimed to analyze the relationship between the changes in ESL use and human activities and regional policies, as well as land ecosystem degradation. The results indicated that land capitalization, resulting from rapid industrialization, urbanization, and the effects of regional land policies and large land projects, caused a clear decrease in grassland and wetland ecosystems, and an increase in forest ecosystem, varying in extent during different periods. Land ecosystem degradation, according to the decreased ecosystem service provision index (ESPI), was concentrated in the Yangtze River Delta, the southwestern regions, and the Inner Mongolia Plateau, where human activity was concentrated. From 2009 to 2015, such degraded land amounted to 9.51% of the Counties. The degradation in ESL use caused a decrease in the ecological goods and services provided by the ecosystems. Land ecosystem degradation resulting from the abandonment of natural grassland and farmland is a prominent and complex problem in China. Optimizing the regional spatial patterns of ESL use was critical for sustainable ecosystem management. Balancing compromises and synergies between providing ecological goods or socioeconomic benefits and ecological costs at a regional or national scale was found crucial for land policy making.
Adolescent's Purchase Behaviour Towards Green Products

The purpose of this study is to consider the relative variables that influence adolescent consumers' behaviour while making green product purchases. The research also determines adolescent consumers' intentions to purchase green products based on the intended behavioural theory (IBT). Research looks for various relative factors like parental influence, advertising means, society and environmental acquaintance, that influence young consumers' decisions to purchase green products. This research has used quantitative method with a survey of 300 young people, carried out at MMDU in Ambala. Research tools used are ANOVA and correlation method to study the association of the factors & its effect on adolescents buying behaviour for green items. The study's findings indicated that, of all the characteristics we looked at, parental influence was the best predictor of teenagers buying behaviour for green products. There are three key determinants (under IBT) like, Individual decisiveness, observations & assumed behavioral traits, that affects young customers' buying behaviour for green items. Model of IBT has been suggested & framed in order to comprehend teenage customers' purchasing patterns for green products.
Model Efficiency Improvement with Simplified Architectures

In previous lightweight methods, whether in the design of lightweight model architectures or in model compression and acceleration techniques, most design approaches focused on reducing the number of parameters as the primary objective. However, reducing the number of parameters does not always guarantee improved acceleration. Sometimes, the two factors are complementary, and during the process of reducing parameters, there is a simultaneous decrease in accuracy. Therefore, achieving a balance between efficiency and accuracy has always been an important issue in lightweight methods. This paper employs a different approach than previous model compression and acceleration methods. Instead of continuously reducing the number of parameters to achieve faster model inference, it optimizes the algorithm of the model inference stage and the overall complexity of the architecture. This approach ensures that the model does not incur additional inference time costs while maintaining the original architectural performance, thereby improving model inference speed without sacrificing accuracy.
Bengali Music Genre Classification Using WaveNet

Music genre classification plays a crucial role in music recommendation and information retrieval. While there have been numerous studies on classifying genres of English music using various machine learning approaches, there is a noticeable absence of similar work for Bangla music classification. Bangla music encompasses a wide range of types and styles that can be classified into different genres. In this research, we focus on six distinct Bangla music genres, including ’Bangla Adhunik,’ ’Bangla Hip-Hop,’ ’Bangla Band Music,’ ’Nazrulgeeti,’ ’Palligeeti,’ and ’Rabindra Sangeet. ’ Our dataset consists of 250-300 songs (in .MP3 format) for each genre. By comparing the performances of different models, we demonstrate that our proposed model, using a combination of Principal Component Analysis and K-Fold on the model WaveNet, achieves high accuracy in the multiclass classification of Bangla music genres by comparing with other machine learning and deep learning. This work contributes to the advancement of music genre classification specifically for Bangla music and paves the way for enhanced music recommendation systems and information retrieval in this rich musical tradition.
A Feasibility Study of Real-Time Image Processing Techniques for Small Flying Object Detection in Drones

Drone usage is increasing significantly in our daily life, from military to delivery purposes. Although drones are also used to detect objects by using different techniques, they are limited to detecting flying small objects such as birds and responding quickly not to cause unintended collisions while flying at high speed. In this paper, we investigate the feasibility of using machine learning and image processing methods in drones while detecting birds mid-flight and responding to ensure their safety. This Real Time Bird Detection system (RTBD) is designed to detect birds so that proper response or evasive action can be taken by the drone. To avoid erroneous responses and observe the auto-behavior of drones while acting not to collide, we have developed an application with a graphical interface to easily control the drone’s video feed and process that information using a machine-learning model. The application also has the capability to detect if a bird is close enough to interfere with the drone’s flight path. Our test results show that the drone identified bird images within a 50-millisecond window of time, with Precision exceeding 96%, when Confidence exceeded 80%.