Miracle-dz/UniFAR-FTUs huggingface dataset:
Tomato Leaf Disease Classification with Lightweight Federated Learning using Identically Distributed Images

Tomatoes grown and consumed extensively in Asia are an essential source of protein (22% per Tomato) and energy (18% per Tomato). Bacterial, fungal, and other microbial diseases severely impact plant health and productivity for tomato farmers. Manual diagnosis of many diseases is challenging, especially in places that need access to crop protection experts. Automated disease identification is crucial for Tomato leaf protection and reduced crop loss. Unfortunately, no one method of categorizing leaf diseases in tomatoes has proven to be consistently accurate enough for widespread usage. The present study offers a streamlined federated deep learning framework to classify tomato leaf diseases while ensuring the preservation of sensitive data. This framework safeguards client information with a decentralized client-server architecture and verifies federated deep learning models with IID. The researchers piloted the framework with a single and several clients in both conventional and federated learning environments. After extracting features from numerous pre-trained models, AlexNet is selected as baseline because of its superior 99.53% accuracy. Next, IID datasets were put through their paces using federal learning (FL). The extra capabilities of the framework make it the ideal option for early Tomato leaf disease categorization.
Performance Analysis of a Modifed Three-Phase Multilevel Inverter

Inverters are crucial for various applications, including electricity systems, residential settings, the automobile industry, and manufacturing. The Multilevel Inverter (MLI) has gained popularity due to its ability to produce waveform outputs resembling sinusoidal waveforms with minimal harmonics. However, the MLI's high component count presents a significant challenge and drawback. To address this issue and make the manufacturing process more efficient and cost-effective, the paper proposes a modified topology that reduces the number of switches in the MLI architecture. The suggested modification is evaluated using simulations in the SIMULINK environment, considering parameters such as Total Harmonic Distortion (%THD), power loss, inverter cost, and size. Comparing the results of the simulations reveals several benefits of the modified topology, including a more compact design and improved cost-effectiveness.
Next Word Prediction in Bangla Using Hybrid Approach

The impact of language models in various applications like machine translation, speech recognition, and chatbots has transformed text-based services. Despite being spoken by over 300 million people, the Bangla language has not yet developed advanced language models due to its unique linguistic traits and limited resources. Traditional language models struggle with the intricacies of Bangla’s linguistic traits, hindering the development of advanced prediction systems. This study aims to bridge this gap by introducing an approach focusing on autocompletion and sequence prediction. The study proposes the integration of the Trie data structure, Convolutional Neural Network (CNN) with Long Short-Term Memory (LSTM), and N-gram methodologies for Bangla word completion and sequence prediction. The Trie data structure stores the Bangla vocabulary and extracts words based on user-entered prefixes, improving efficiency and preventing misspellings. The hybrid architecture is designed to capture long-range relationships and comprehend contextually rich patterns. This makes it particularly efficient at handling complex compound words, rich inflections, and variable word order. The experimental evaluation involves a comprehensive dataset comprising approximately 50 thousand Bangla language samples collected from diverse sources, ensuring a representative linguistic spectrum. The results show the potential of this approach to revolutionize Bangla search engines, keyboards, and recommendation algorithms.
Revolutionizing Respiratory Health: Unveiling the Interplay Between Vitamin D and Asthmatic Complications Through Advanced AI Insights

Asthma, a chronic respiratory disorder affecting millions worldwide, continues to pose significant challenges to public health. There is growing interest in exploring the potential association of Vitamin D with asthmatic complications due to its known immunomodulatory properties. In recent years, the emergence of artificial intelligence (AI) has revolutionized medical research, offering new avenues to gain insights from complex datasets. This review paper comprehensively examines the role of AI in understanding the association between Vitamin D and asthmatic complications. The review begins with an overview of the current literature on the subject, highlighting the positive and negative findings from various studies. It explores the limitations of existing research and explores how AI techniques can address these challenges. The paper discusses the applications of AI, including natural language processing (NLP) for literature mining, machine learning algorithms for data analysis, and image processing techniques for lung imaging, in advancing the understanding of this association. Moreover, the review outlines the advantages and limitations of using AI in this context, emphasizing the potential benefits of AI-driven insights, including precision and scalability. The review also addresses the need for careful consideration of data biases and interpretability issues associated with AI methodologies. In conclusion, this review paper demonstrates the promising potential of AI in advancing the understanding of the association between Vitamin D and asthmatic complications.
Revamping Sampling-Based PGO with Context-Sensitivity and Pseudo-instrumentation

The ever increasing scale of modern data center demands more effective optimizations, as even a small percentage of performance improvement can result in a significant reduction in data-center cost and its environmental footprint. However, the diverse set of workloads running in data centers also challenges the scalability of optimization solutions. Profile-guided optimization (PGO) is a promising technique to improve application performance. Sampling-based PGO is widely used in data-center applications due to its low operational overhead, but the performance gains are not as substantial as the instrumentation-based counterpart. The high operational overhead of instrumentation-based PGO, on the other hand, hinders its large-scale adoption, despite its superior performance gains. In this paper, we propose CSSPGO, a context-sensitive sampling-based PGO framework with pseudo-instrumentation. CSSPGO offers a more balanced solution to push sampling-based PGO performance closer to instrumentation-based PGO while maintaining minimal operational overhead. It leverages pseudo-instrumentation to improve profile quality without incurring the overhead of traditional instrumentation. It also enriches profile with context-sensitivity to aid more effective optimizations through a novel profiling methodology using synchronized LBR and stack sampling. CSSPGO is now used to optimize over 75% of Meta's data center CPU cycles. Our evaluation with production workloads demonstrates 1%-5% performance improvement on top of state-of-the-art sampling-based PGO.
Performance Analysis of Angular Diversity Receivers in Multi-Cell VLC Systems

This paper presents the performance analysis of angular diversity receivers (ADRs) in visible light communication (VLC) for multi-cell networks. Different ADR configurations are compared through numerical analysis for indoor down-link scenarios, they are: square pyramid (SP), square frustum (SF), hexagonal pyramid (HP) and hexagonal frustum (HF). Additionally, three receive diversity techniques are applied: selection combining (SC), equal gain combining (EGC) and maximum ratio combining (MRC). It is shown that the signal-to-interference-plus-noise ratio (SINR) and bit error rate (BER) is improved when the frustum type ADR with SC is used. Our results also indicate that the HP and HF ADR, which has 6 photo detectors (PDs) on the side, do not improve the BER performance compared to the SP and SF type with 4 PDs on the side when SC is applied.
Towards Robust Anomaly Detection in User Equipment Parameters: A Deep Generalized Canonical Correlation Analysis approach

The evolution of mobile communication networks towards the era of 5G necessitates robust anomaly detection systems to ensure uninterrupted user experience and network efficiency. This paper introduces a novel approach using Deep Generalized Canonical Correlation Analysis (DGCCA) for anomaly detection in User Equipment (UE) parameters. DGCCA, an extension of Canonical Correlation Analysis, offers the ability to capture complex correlations among diverse UE parameters, surpassing the limitations of conventional anomaly detection methods. Leveraging a meticulously curated simulated dataset derived from an urban area scenario, this study evaluates DGCCA alongside traditional models such as SVM, Naive Bayes, Random Forests and FCNN. Results showcase DGCCA’s remarkable accuracy of 92.23% with a low false negative rate of 1.55%, surpassing its counterparts. The outcomes underscore DGCCA’s robustness in fortifying self-organizing networks, offering a promising solution for resilient and adaptive mobile communication systems in the modern era. Incorporating this anomaly detection system into the ORAN framework has the potential to improve the forecasting of optimized base station parameters.
Engineering Applications of Artificial Intelligence

The increasing amount of real-time data collected from sensors in industrial environments has accelerated the application of machine learning in decision-making. Reinforcement learning (RL) is a powerful tool to find optimal policies for achieving a given goal. However, RL’s typical application is risky and insufficient in environments where actions can have irreversible consequences and require interpretability and fairness. While new trends in RL may provide guidance based on expert knowledge, they do not often consider uncertainty or include prior knowledge in the learning process. We propose a causal reinforcement learning alternative based on Bayesian networks (RLBNs) to address this challenge. The RLBN simultaneously models a policy and takes advantage of the joint distribution of the state and action space, reducing uncertainty in unknown situations. We propose a training algorithm for the network’s parameters and structure based on the reward function and likelihood of the effects and measurements taken. Our experiment with the CartPole benchmark and industrial fouling using ordinary differential equations (ODEs) demonstrates that RLBNs are interpretable, secure, flexible, and more robust than their competitors. Our contributions include a novel method that incorporates expert knowledge into the decision-making engine. It uses Bayesian networks with a predefined structure as a causal graph and a hybrid learning strategy that considers both likelihood and reward. This would avoid losing the virtues of the Bayesian network.
Logical Relation Modeling and Mining in Hyperbolic Space for Recommendation

—The sparse interactions between users and items have aggravated the difficulty of their representations in recommender systems. Existing methods leverage tags to alleviate the sparsity problem but ignore prevalent logical relations among items and tags (e.g., membership, hierarchy, and exclusion), which can be leveraged to enhance the accuracy of modeling user preferences and conducting recommendations. To this end, we propose to extract logical relations among item tags from existing tag taxonomies and exploit the individual strengths of the Poincar´e and the Lorentz models in hyperbolic space for logical relation modeling towards enhanced recommendations. More-over, we find that the logical relations directly extracted from existing tag taxonomies can be inaccurate and coarse. Therefore, we further devise innovative consistency-based and granularity-based weighting mechanisms based on user behavior patterns for data-driven logical relation mining that can be jointly optimized along with recommendations in an end-to-end fashion. Extensive experiments on four real-world benchmark datasets show drastic performance gains brought by our proposed framework, which
The Impact of Deep Learning on Organizational Agility

Artificial intelligence advances business model, strategizes competitive resources, and impacts on organizational agility. Deep learning as a subset of AI brings changes in different aspects that substantially influences organizational capabilities. We argue that deep learning enables new conceptualization of organizational agility. We will conduct a case study in a leading Chinese FinTech company to inductively ground these impacts.
RIT Digital Institutional Repository RIT Digital Institutional Repository

As the global economy continues to expand, international trade is growing at an unprecedented rate, resulting in a surge of cargo movements across borders. With so much activity taking place, it's imperative that customs systems operate intelligently to identify potential risks in every transaction and highlight any potential fraud or manipulation that may be occurring. Customs play a crucial role in supporting legitimate trade, protecting society, and promoting sustainable economic development. However, it is unfortunate that some companies take advantage of the facilities provided by customs departments to avoid paying their duties or engaging in smuggling from the free zone to local market activities. According to data provided by the customs departments in the UAE, there has been a concerning increase in the number of such payment defaults over the years. This not only undermines the fairness and integrity of the customs system but also has a negative impact on the broader economy and society at large. Therefore, customs authorities must remain vigilant and take proactive measures to prevent and deter such illicit activities. By doing so, they can ensure that legitimate businesses are not put at a disadvantage and that the benefits of international trade are shared fairly and equitably. CRISP-DM (Cross-Industry Standard Process for Data Mining) is a widely-used framework for conducting data mining and machine learning projects which will be utilized here. This research is comparing the performance of multiple machine learning techniques including Linear Discriminant Analysis LDA, Random Forest (RF), Quadratic Discriminant Analysis (QDA), Logistic Regression (LR), Naive Bayes (NB), Nearest Neighbours Learning Machine (KNN), Support Vector Machines (SVM), Stochastic Adaptive Boosting, Gaussian Processes, and Bagging to examine if we can predict the payment default in the transactional historical data of the free-zone companies. The research proves that machine learning can be used to predict the payment defaults behaviour, and that the Random Forest model consistently performed better than other evaluated models.
Natural Language Processing and Machine Learning Approaches to Detect Bangla Hate Speech on Social Media

In this modern era, with the popularity of the Unicode system, many people use Bangla text to express their opinions, comments, emotions on Facebook, Twitter, and YouTube, etc. However, this social connectivity always does not create positive impacts. Trolling, cyberbullying, online harassment, sexual and political abuse, and cybercrime often create a huge negative physiological effect on victims. In most cases, women, children, and celebrities are the victims which may often lead them to depression and suicidal activity. Therefore, at the early stage, cyberbullying and hate speech detection can reduce these cyber crimes.A few researches have been carried out in the Bangla language to detect cyberbullying and hate speech due to annotated corpora, lexical and morphological analyzers. This research aims to design and develop an effective model to detect Bangla cyberbullying and hate speech text with the combination of Natural Language Processing (NLP) and Machine Learning Classifier algorithms. In this study, we have collected 6244 Bangla text data from various online social platforms consisting of 6 categories of hate speeches. Then, we labeled the data into two main classes-bullied and neutral that were used to train our classifiers after preprocessing and features extracted by the Bangla NLP tool and TF-IDF(Term Frequency –Inverse Document Frequency). Our comparative study outlines that, with an accuracy rate of 89% and considering parameters Recall, Precision, Specificity, F-Score, and ROC, the Random Forest Classifier achieves the best results compared to others.
A Wideband 90 Phase Shifting Element Applied in Quadrature Phase Filtering Power Divider

—A wideband 90 ◦ phase shifting element for application in the quadrature phase filtering power divider (QFPD) is presented in this letter. Instead of the complex calculation procedure of phase information between reference and main branch in traditional phase shifter design, this letter initially reveals the inherent property of 90 ◦ phase difference between terminal open-and short-circuited transmission lines (TLs) by investigating their respective electric field distributions. Following this concept and conventional filtering design method, a new topology of QFPD is constructed. For demonstration, a design example of wideband QFPD is implemented and measured. Results show that the proposed wideband QFPD exhibits phase bandwidths of 69.24%, impedance bandwidths of 58.68%, and magnitude imbalances of less than 0.7 dB, together with better than 20 dB isolation performances.
Unveiling the Sweetness: Evaluating Yield and Quality Attributes of Early Generation Sweet Corn (Zea mays L.var. saccharata) Inbred Lines through Morphological, Biochemical and Marker-based Approaches.

Background Sweet corn is gaining tremendous demand worldwide due to urbanization and changing consumer preferences. However, genetic improvement in this crop is limited due to narrow genetic base and other undesirable agronomic traits that hinders the development of superior sweet corn genotypes. Methods A study was undertaken to carry out morphological and biochemical evaluation of 80 early generation inbred lines (S 2 ) of sweet corn that were developed from a cross between two sweet corn hybrids (Mithas and Sugar-75) at Mountain Research Centre for Field Crops, Khudwani, SKUAST Kashmir during Kharif 2020. Furthermore, validation for favourable recessive alleles for sugar content was also done using SSR markers. The 80 sweet corn inbreds evaluated for phenotypic characterization showed wide range of variability with respect to different traits studied. The highest content of total carotenoids was found in the inbred S27 (34µgg − 1 ) followed by the inbred S65 (31.1µgg − 1 ). The highest sugar content was found in the S60 (8.54%) followed by the S14 (8.34%). Comparing the results of scatter plot for biochemical traits with morphological traits, it was revealed that inbreds S9, S23, S27 and S36 contains high levels of total sugars and total carotenoids along with moderate values for yield contributing traits indicating that these inbred lines could be utilized as source of favourable alleles in sweet corn breeding programmes after further validation for yield attributes, disease resistance and other preferable agronomic traits.
Numerical analysis of uplift capacity of strip plate anchors in reinforced and unreinforced cohesionless soils

: In this study, the effect of the reinforced soil condition on uplift capacity of strip anchor plates is investigated with three dimensional numerical analyses. The analyses were performed both on dense and loose sand using different sizes strip anchors plates ( B = L =0.5m-0.75m-1.00m) for different embedment ratios, ( D f / B =1-3-5). The geogrid element was modeled as a reinforcement in the analyses and the “Hardening Soil Model” was used as a soil model. The parameters investigated are the first reinforcement layer position, number of reinforcement layers, and length of the reinforcement layers. The re-sults proved that the reinforcement had a considerable effect on the uplift capacity of the strip anchor plates in sand. Also, the improvement in bearing capacity of strip anchor plates in sand was found to be strongly dependent on the embedment ratio, and relative density of sand. The failure mechanism of strip plate anchors which are subjected to uplift forces was investigated in both reinforced and unreiforced soil conditions in order to observe the effect of reinforcement on failure mechanism. Based on the three dimensional finite element analyses, optimum values of the geogrid parameters for maximum reinforcing effect are discussed and suggested.
Role of Subgrade Reaction Modulus in Soil-Foundation-Structure Interaction in Concrete Buildings

One of the key issues in structural and geotechnical engineering is that most parts of buildings are usually analysed separately and then the outputs are used in foundation designs. In this process, some effects are neglected. In this study, the soil–structure interaction (SSI) in foundations of concrete buildings was evaluated using the direct finite element method (DFEM). 3D models were developed and used to analyse concrete buildings with different stories constructed on soft soil. Foundation settlement, deformation of foundation, soil pressure diagram, and weight of reinforcement in the foundation were considered as the main parameters. Deformation of the foundation was analysed using the finite element method considering the effect of combined loadings (combinations of dead load, live load, and earthquake load). It is shown that by changing values of subgrade reaction modulus (Ks) in foundation design, the effects of SSI on tall buildings can be considered automatically. The results also show that the soil–structure interaction can cause changes in the pattern of foundation settlement, foundation deformation, and the weight of reinforcement used in foundation design. Furthermore, dishing deformation in foundation appeared in terms of SSI effects. An equation is provided to simplify considering SSI effects in foundation design. This method is practical for civil (especially structural) engineers, and they can conveniently consider these effects in foundation design without using DFEM.