Classification - Science topic

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In general, dealing with false copies in linear discriminant analysis depends on a good understanding of the data and applying the necessary procedures to correctly identify and treat these copies.

The so-called "fertile" granites which had relatively high background U contents, such as 10-20 ppm.

For example, in the case of the Beaverlodge uranium province of Saskatchewan the granitic rocks "were relatively enriched in uranium".

Some variable regions are simply not suitable for accurate identification, see eg

First of all, I want to encourage you not to give up on an area just because there are a lot of researchers in it. People should follow their interests if they are capable of managing the task and are interested in them. It's not only that EEG research is a promising field, but it's also interesting to classify EEG data using machine learning or deep learning approaches. It's okay if it seems saturated to you. Improving already completed work is always a way to contribute. There are many ways to propose improved algorithms and models if you have an interest for mathematical modelling. Remember that even in well explored research fields, there is always space for creativity and advancement of interest.

It's better to start with a review paper on the latest research article in this field. In one paper (latest review paper), you can gain a clear idea of the work that has been done and the suggestions put forward by the authors (researchers) based on their investigation. This approach helps you understand the current state of the field and identify potential gaps or areas for further exploration.

In the biomedical field, preference should be given to applications that demonstrate effectiveness in promoting health and safety.

1. And, I would like to suggest that you integrate ML/DL techniques for EEG classification along with IoT or some real-time device, such as Jetson Nano or an equivalent.

2. EEG signals should have noise and limited spatial resolution. Maybe you can investigate.

3. Left and right hand movements generate distinct EEG signals. If you can collect a real dataset from reputable medical resources, you could investigate EEG signals in paralyzed individuals and analyze them.

I am sharing here some of the article maybe you can have a look, i feels that could help you better:

*) Current Status, Challenges, and Possible Solutions of EEG-Based Brain-Computer Interface: A Comprehensive Review.

*) A review on analysis of EEG signals.

*) Deep Learning Algorithm for Brain-Computer Interface.

*)Encyclopedia of Clinical Neuropsychology.

Finally, as this is your graduation thesis, it's important to have a backup plan. During research, numerous byproducts are often produced, many of which hold value. I hope you will successfully reach your final destination with this research. However, it's essential to keep proper track of your byproducts. They may prove invaluable in shaping your thesis and ensuring you graduate on time. Furthermore, even after graduation, consider continuing your research if possible.

Chapter 3.10 by Gehlen deals with energy industry in general, not specifically with the Reichsgruppe "Energy Industry". Of course, energy industry is connected with mining. However, mining was organized as a "Wirtschaftsgruppe" (industry group) in "Hauptabteilung" (main department) I of the "Reichsgruppe Industrie" (see https://archivfuehrer-kolonialzeit.de/reichsgruppe-industrie-bestand?sf_culture=en and https://de.wikipedia.org/wiki/Reichsgruppe_Industrie). The Reichsgruppe "Industry" had 32 groups, whereas the much smaller Reichsgruppe "Energy Industry" had only two groups for "Electricity supply" and "Gas and water supply" (see https://de.wikipedia.org/wiki/Reichsgruppe_Energiewirtschaft).

Choosing between the traditional UHI index classification and the alternative classification (<0, 0-0.05, 0.05-0.10, 0.10-0.15, 0.15-0.20, >0.20) for studying the urban heat island effect in a tropical region depends on factors such as data resolution, local climate, research objectives, and the need for comparative analysis. Higher resolution data and focusing on nuanced temperature variations may favor the traditional classification. At the same time, broader patterns of UHI across more significant regions might be better captured by the alternative classification with wider intervals. Considering the study area's specific research goals and characteristics is crucial in selecting the most suitable classification scheme to provide meaningful insights into the UHI phenomenon.

Universities are classified based on various factors, but some common ones include:

Research: How much and how good is the research they do?

Teaching: How well do they teach?

Reputation: How well-known are they?

Students: How successful are their students?

Campus: What are the facilities like?

Remember, different organizations might focus on different aspects, so explore universities that fit your priorities.

Fake results are never published in journals (In some cases, certain journal websites may be fake and their their documents are available). However, certain details are often omitted or only vaguely presented in research papers when researchers conduct experiments based on specific datasets and models. These undisclosed variables may include precise preprocessing procedures, hyperparameter adjustments such as learning rate modifications, and L1 and L2 regularization strategies. It's important to note that researchers actively strive to find optimal solutions before reaching their expected accuracy levels.

Before training the deep learning model, it is essential to split the dataset using proper split scripts into train, validation, and test sets (There can be a different range of amounts of split data that give the optimal solution). If you observe that the accuracy is not good enough, then you should consider improving the network architecture. If the same dataset or paper showed good accuracy with a particular network architecture, but you are unable to achieve similar results, you may consult with the authors to understand how they attained optimal accuracy. This can be done via email or another platform where they are accessible.

You can also follow books, reference papers, or online articles that precisely mention the kinds of aspects that can provide you with optimal solutions to your current problems. Some recommended resources include:

E' un piacere aiutare, per quello che mi è dato conoscere. Auguri per un nuovo anno ricco di soddisfazioni!

Dear Doctor

"Compared to its predecessors, the main advantage of CNN is that it automatically detects the important features without any human supervision. This is why CNN would be an ideal solution to computer vision and image classification problems."

In the realm of machine learning, the main distinction between supervised and unsupervised learning lies in the nature of the dataset used for training.

Supervised Learning Dataset:

In supervised learning, the dataset consists of labeled examples, where each data instance is associated with a corresponding target or output value. The dataset includes both input features and the desired output or target variable. The aim of supervised learning is to learn a mapping function that can accurately predict the target variable based on the input features. The model is trained using labeled examples, allowing it to generalize and make predictions on unseen data. Common examples of supervised learning algorithms include linear regression, decision trees, and support vector machines.

Unsupervised Learning Dataset:

On the other hand, unsupervised learning involves unlabeled datasets, meaning they do not have corresponding target values. In this scenario, the model learns patterns, structures, or relationships within the data based solely on the input features. The objective of unsupervised learning is to discover inherent patterns or groupings within the data without prior knowledge of the desired output. Common unsupervised learning algorithms include clustering algorithms such as k-means clustering and dimensionality reduction techniques like principal component analysis (PCA).

FGVC-Aircraft, iNaturalist 2018, 2019, and 2021, WebVision-1000, PASS (ImageNet without Human)

Great job, you can validate in two ways:

1. use Google earth or

2. Ground truthing (actual fieldwork visit with selected georeferenced points)

Kindly review the literature to gain an understanding of the different threshold values, which may not necessarily apply to your study area.

It is also recommended that you apply NDBI on your imagery and verify the classes against values using Google Earth images and/or field verification.

Dear Jebari Chaker ,

Classification and clustering are two methods of pattern identification used in machine learning. Although both techniques have certain similarities, the difference lies in the fact that classification uses predefined classes in which objects are assigned, while clustering identifies similarities between objects, which it groups according to those characteristics in common and which differentiate them from other groups of objects. These groups are known as "clusters".

Regards,

Shafagat

The categorization in the attached image is correct. The 12 identified compounds can be classified into the following groups:

Monoterpenes: Camphene, Para-Cymene, 1-Limonene, Gamma-Terpinen

Sesquiterpenes: Trans-Decalone

Aromatic aldehydes: Cuminic Aldehyde

Cyclic ketones: Cyclopentanone

Aromatic alcohols: Acetyl phenyl carbinol

Amines: 1-amino-1-o

Heterocycles: 5-Methyl-2-Phenyl indolizine, 5-nitrobenzofuran-2-carboxylic

Inorganic acids: Silicic acid

Monoterpenes and sesquiterpenes are the largest groups of compounds in the essential oil. They are hydrocarbons that are derived from isoprene, a five-carbon molecule. Monoterpenes have ten carbon atoms, while sesquiterpenes have fifteen carbon atoms.

Aromatic aldehydes are a group of compounds that contain an aldehyde group (-CHO) attached to an aromatic ring. They are often used as fragrances in perfumes and cosmetics.

Cyclic ketones are a group of compounds that contain a ketone group (=O) in a cyclic ring. They are often used as solvents and flavorings.

Aromatic alcohols are a group of compounds that contain an alcohol group (-OH) attached to an aromatic ring. They are often used as fragrances and flavorings.

Amines are a group of compounds that contain an amino group (-NH2). They are often used as solvents and pharmaceuticals.

Heterocycles are a group of compounds that contain a ring with at least one atom that is not carbon. They are often used as pharmaceuticals and agrochemicals.

Inorganic acids are a group of compounds that contain a hydrogen atom that is ionizable in water. They are often used as solvents and catalysts.

The classification of these compounds is important because it can help to predict their properties and applications. For example, monoterpenes are often volatile and have a strong odor, while sesquiterpenes are less volatile and have a more subtle odor. Aromatic aldehydes are often used as fragrances, while cyclic ketones are often used as solvents. Amines are often used as pharmaceuticals, while heterocycles are often used as agrochemicals. Inorganic acids are often used as solvents and catalysts. Mehdi Aghighi Shahverdi

Dear Ashir Ahmed ,

Due to the specific characteristics and complicated contents of remote sensing (RS) images, remote sensing image retrieval (RSIR) is always an open and tough research topic in the RS community. There are two basic blocks in RSIR, including feature learning and similarity matching. In this paper, we focus on developing an effective feature learning method for RSIR. With the help of the deep learning technique, the proposed feature learning method is designed under the bag-of-words (BOW) paradigm.

Regards,

Shafagat

Hi

There are several reasons why statistical tests might not be applied:

1. Sample Size and Variability.

2. Marginal Improvements.

3. Computational Complexity.

4. Focus on Other Metrics.

5. Methodological Preference.

6. Lack of Standardization.

generally considered good practice to apply statistical tests in such scenarios to rigorously validate the results

Hamza Roubhi I appreciate your dedication to applying 1D CNN in the domain of speech emotion recognition and your commitment to addressing the issue concerning the variation in classification results when shuffling columns. As a fellow researcher with a background in signal processing, I understand the significance of consistency and reliability in the outcomes of such analyses.

When encountering discrepancies in results due to column shuffling, it is essential to ensure that the underlying data preprocessing and feature extraction methods remain consistent across different column arrangements. Validating the integrity of the data and confirming that the shuffling process does not introduce unintended variations can help maintain the robustness and reliability of the classification results.

Additionally, verifying the implementation of the CNN architecture, including the configuration of the layers, activation functions, and training parameters, is crucial in ensuring reproducibility and consistency in the classification outcomes. Reviewing the model's initialization procedures and ensuring that the randomization process aligns with the desired standards of consistency can potentially address the issue you are facing.

Furthermore, conducting thorough checks on the data partitioning and validation procedures, such as cross-validation techniques, can help identify any potential sources of variability that may arise due to the shuffled column arrangements. Ensuring that the model training and testing processes maintain consistency and adhere to standardized protocols can contribute to the stability and reliability of the classification results.

I recommend conducting a systematic evaluation of the data preprocessing, model architecture, and training protocols to pinpoint any potential sources of variation that may arise during the column shuffling process. By adhering to rigorous validation practices and ensuring the reproducibility of results across different column permutations, you can enhance the reliability and robustness of your CNN-based classification framework for speech emotion recognition.

When writing a journal paper about deep learning and image classification, there are several key aspects to focus on to ensure acceptability and quality:

By addressing these aspects and paying attention to detail, you can increase the acceptability and quality of your journal paper on deep learning and image classification. Additionally, it's important to follow the specific guidelines and requirements of the journal you plan to submit to, as they may have their own formatting and submission guidelines.

Dear Qamar

NM- On May 11, 2023 I started reading and commenting RG published papers on MCDM, and today or tomorrow I expect to publish in RG the hundredth analysis and comments. All of them are in RG.

It has been a very nice surprise to receive you comments, and I thank you for that.

Do you know how many responses I have received out of 99? Only 2

Of course, there is no obligation to write or feed me back, but what really surprises me is that NOBODY defended or backed what he/she wrote, even when normally there are several authors. I believe that if a person writes something and somebody else refutes the writer, the normal procedure would be to answer and refute it or not. This happened in the 90s when Dyer commented on AHP, and Saaty and Harker published their refusal. The famous letters from both sides are in the Web

Nobody asked me to write and publish my comments on work of others, and I don’t ask or expect anything, just my desire to collaborate in the improvement of this discipline. I do it because I have dedicated the last 20+ years in studying and analysing MCDM, a fascinating subject for me, and in my opinion, this discipline is not pursued correctly. There are more that 200 MCDM methods, and all of them, are unable to address complex scenarios, with the possible exception of the grand dad of all methods, Linear Programming, created about 1940.

Qamar UI-Islam- Thank you for your detailed feedback and insightful observations on the "State-of-the-Art Review on the Analytic Hierarchy Process with Benefits, Opportunities, Costs, and Risks" by Antonella Petrillo, Valerio Antonio Pamplona Salomon, and Claudemir Leif Tramarico. Your perspective sheds light on critical aspects of the Analytic Hierarchy Process (AHP) and the broader context of Multiple Criteria Decision Making (MCDM) methodologies. Allow me to provide some clarifications and responses to the points you raised:

1- Classification Approach: I appreciate your agreement with the aggregation and outranking approach classification, emphasizing their relevance in the context of AHP and MCDM methodologies.

NM - AHP was a method created in the 50s., using a concept never employed before in decision-making, and that at said time worked well, because it uses the hierarchical structure that was been employed by enterprises since the Egyptians, or before. That is: “Do as I say, don’t argue”

Saaty developed his method when he was working for the US Army, and possibly adopted its hierarchical lineal and rigid structure for AHP.

Unfortunately, the method incorporates aspects that border the absurd when applied to MCDM, like the pair-wise comparison, developed by psychometrician L. L. Thurstone, in 1927.

You can apply it to decide which restaurant to go for dinner, or the place for your vacations, or, in the case of the military, considering different strategies and war equipment, but not in a problem like selecting the best location for an industry. Here, there are multiple aspects, all of them related, and on what the DM does not have any control, let alone authority.

Qamar UI-Islam- Choice of Method: Your insight into the selection of an appropriate method based on the problem's characteristics aligns with the practical considerations of matching the method to the specific nature of the decision problem.

NM - It is not mathematics, only common sense, like you don’t buy a suit if it does not fit you

Qamar UI-Islam- Mathematical Foundation of AHP: Your critical assessment of the mathematical underpinnings of AHP raises pertinent questions regarding the method's reliance on subjective pair-wise comparisons and the practical implementation of criteria trade-offs.

NM - I already commented about pair-wise comparisons. Regarding trade-offs, considered as criteria weights, it was only a Saaty assumption,as he described it in one of his writings. But trade-offs and weights have completely different meanings and use. The first one is a balance, while the second is a quantification of criteria. The fact that these weights, DETERMINED WITHOUT CONSIDERING THE ALTERNATIVES, is a fallacy. It can be easily demonstrated using entropy weights.

1. Qamar UI-Islam- BOCR Criteria: Your emphasis on the dynamic nature of criteria relationships and their potential for non-mutually exclusive interactions contributes to the discussion around the practical applicability of BOCR criteria and their role in decision-making processes.

NM- Thank you for your appraisal

2. Qamar UI-Islam- AHP Application Challenges: Your detailed evaluation of the constraints and limitations of applying AHP to complex decision problems underscores the need for a more comprehensive and adaptable approach, especially in scenarios with interconnected criteria.

NM- In reality, there are more than 100 researchers that addressed these issues in AHP, who I mention and identify by name in one of my books, and only a handful that are in favor of the method, also identified; therefore, they are not only my ideas; what I did is to enlarge and illustrate these fallacies

Regarding criteria interconnections, there are very few scenarios where criteria are independent. What really surprises me, after reading hundreds of published papers that ‘solve’ this type of problems using AHP, even in reputable journals, is that reviewers accept them. WHY?

By ignorance, negligence, or vested interests, because some reviewers are only interested in being mentioned as reviewers of a journal and mentioned in ‘Publons’, which is good for their CV?

It is impossible to ignore essential points like a primordial condition in AHP, clearly established by Saaty, about the necessity to have only independent criteria, or trade-offs equivalent to weights, and other evident misconceptions.

3- Qamar UI-Islam- Risk-Benefit Trade-Offs: Your emphasis on the interconnectedness of risks, benefits, and compensatory mechanisms within decision matrices highlights the significance of holistic and systems-based considerations in decision-making frameworks.

NM- Again, it is only common sense. We are not talking on Quantum Mechanics

Qamar UI-Islam -Your insights provide valuable perspectives for further examination and exploration of the Analytic Hierarchy Process and its implications within the realm of Multiple Criteria Decision Making. I appreciate your engagement with the subject matter and your dedication to advancing the understanding of decision-making methodologies.

NM- Thank you for your words, which I deeply appreciate. Maybe this communication may encourage other researchers to publish and discuss their concerns and why not, support for AHP and other MCDM methods.

Regards

Nolberto

Dear Ahmed Aqeel Abbs ,

The advancements in the development of artificial intelligence spread all over the world at a tremendous speed and create an incredible hype increasing our expectations. As a matter of fact, it is rather difficult to disappoint a user in an entertaining domain: an introduction of AI and neural networks is instantly gaining immense popularity (Prisma and FaceApp applications are good examples of that). In this article, we have compiled 9 ways to use artificial intelligence in education.

Regards,

Shafagat

Finding and downloading specific statistical data for such a wide range of topics and over a specific timeframe may not be straightforward, especially when dealing with data from China, which can have various restrictions and sources. However, I can provide you with some general guidance on where you might start your search for this type of data:

Remember to respect copyright and licensing restrictions when using and sharing any data you obtain. Additionally, data availability and accessibility can vary, so you may need to contact the respective organizations or institutions for more specific and detailed data requests.

Hey Himanshu Tiwari ,

Interesting topic! Classifying countries by Soil Organic Carbon (SOC) levels is key for addressing SDG challenges, especially in combating land and soil degradation. High SOC regions may focus on preserving soil quality, while low SOC areas might prioritize restoration efforts. These classifications help tailor interventions for specific challenges each region faces in achieving SDGs. Thoughts?

Dear Krah Kouadio ,

The Multi-Label Image Classification focuses on predicting labels for images in a multi-class classification problem where each image may belong to more than one class.

Regards,

Shafagat

Dear Krah Kouadio ,

n multilabel classification, in contrast to binary and multiclass classification, the deep learning model predicts the probability of each class.

Regards,

Shafagat

Thank you Hamza Magaji Makarfi and Alfonsas Misevicius ....still any other...

ok

There is a lot of work being done, check out the survey by Meshrif Alruily "Classification or Arabic tweets a review"

It is difficult to find papers and reviews that justify the threshold of criteria to assess models there are some rules of thumb with range values, for example, for accuracy 50 % is not good it means that your values were predicted randomly, more the percentage is close to 100% better the accuracy is however around 100% meaning that your model overfits, for RMSE MSE lower the value is better your model is because it is translated that your model doesn't have errors. Each model is different according to the included variables. You can run many models changing some parameters and comparing the values between them acknowledging each specificity of the model ensemble learning method will be more accurate than a simple model. You need to select your model according to your research question.

To enhance the performance of your VGG16 model during training and validation, you can start by applying data augmentation techniques to increase dataset diversity and reduce overfitting. It's crucial to ensure the dataset's cleanliness, correct labeling, and appropriate division into training, validation, and test subsets. Experiment with different learning rates and optimizers, and consider using learning rate schedulers if necessary. Employ regularization methods like dropout and L2 regularization to tackle overfitting issues. Keep a close eye on the training process, implement early stopping, and adjust the batch size as needed. You might also want to explore alternative model architectures or smaller models that could better suit your dataset. Lastly, make sure your hardware resources are utilized effectively, and explore ensemble methods to potentially enhance model performance. These strategies should help you overcome the low accuracy challenge with your VGG16 model.

Thank you for your answer Saleh.

Best regards

Ioannis

The classification of a species as native or invasive is region-specific. For species like Euphorbia prostrata, which is native to Brazil but invasive elsewhere, a dual classification "NATIVE/INVASIVE" is appropriate. It's crucial to specify the geographical context: a species can be 'native' in its home region and 'invasive' when it proliferates beyond its natural range. To clarify, you can note: "Euphorbia prostrata - NATIVE in Brazil but INVASIVE in [specific countries]". This dual nature underscores the importance of tailored biodiversity management strategies for each region.

People do many things without adaptive control.

However, at some point, we may understand that the performance may not be what we were looking for. Either the plant parameters were not exactly what we thought, or they might have changed from what they were in the beginning. Moreover, the object of control could be time-varying, nonlinear, etc.

This is what led people to think about “adapting” the control parameter to the actual situation.

A first idea was to only change the main control loop gain. This was called the MIT rule and it worked nicely and was even implemented on a plane that flew nicely…until it crashed. Comes out that working with time-varying control parameters is not the same as fixed parameters.

This made people reluctant to the idea of adaptive control.

Then, some names, such as Prof. Kumpati (Bob) Narendra from Yale, contributed a lot to the theory of stability and also came up with their own idea of adaptive control. This appears in literature as Model Reference Adaptive Control (MRAC) and this is what you usually see in literature.

It requires the plant to behave like some ideal model of the same order and indeed, it tries to modify the parameters of the big plant.

As it happens, by 1980, after some engineering experience, I came to RPI (Rensselaer) for a PhD. The “problem” was that the Professor’s grant was for Large Flexible Structures (LFS). They were not only large plants but also MIMO, so there was not much we could do with the classical MRAC.

They started using another idea, which was called Command Generator Tracking (CGT). You don’t try to make all state variables of the big plant follow all state variables of some big model. Instead, you define some low-order model that only defines the trajectory that you would like your plant output to track. They had some publications, yet the idea was negatively received, so they were almost on their way out. However, the grant forced my advisor to let me “try to do something.”

As I was coming from the industry, I liked this idea of a simplified controller with varying parameters, and I even “felt” that it should work. The plant can be of order 20 or 50 and yet the model and the controller can be of order 5.

What can I say? I ended up with a low-order adaptive controller that managed to control the pretty big LFS. It was interesting that people at JPL liked the idea and even dared to implement it on their large flexible antenna.

After playing with it, I decided to call our approach “Simple Adaptive Control (SAC).”

I won’t make this message a dissertation, but if you write Simple Adaptive Control on Google, you can see quite a bit, of publications from all around the world.

Also, please always feel free to come back with any questions.

Dr Aparna Sathya Murthy thank you for your contribution to the discussion

Our project: Quantifying Mental health/// is based on mood science and the pathological chronification of moods as, for example, represented in the Plutchik’s Wheel of Emotions, dear Cai Jinping This approach works by ontological engineering, forensic simulation and key AI technologies.

cc

Al Fermelia

See the end of the page https://explore.researchgate.net/display/support/How+to+make+content+private+or+remove+it. You should contact the ResearchGate's at www.researchgate.net/contact asking to delete this page.

CNNs (Convolutional Neural Networks) are generally preferred over SVMs (Support Vector Machines) for image classification tasks due to their ability to automatically learn hierarchical features from images. Here's why CNNs are often considered better for image classification:

Regarding whether machine learning or deep learning is better for image classification, the answer depends on several factors:

In summary, CNNs are generally favored over SVMs for image classification due to their ability to automatically learn relevant features. Whether to choose machine learning or deep learning for image classification depends on factors like data availability, feature complexity, computational resources, and the desired level of performance. In many cases, deep learning, especially CNNs, has demonstrated superior performance on large and complex image datasets.

Deep learning has revolutionized image processing, and Convolutional Neural Networks (CNNs) are the most commonly used algorithms for image classification tasks. CNNs are a class of deep neural networks specifically designed to process and analyze visual data, making them highly effective for image-related tasks. CNNs leverage the concept of convolution to automatically learn hierarchical features from images, capturing patterns and structures at various levels of abstraction.

Some popular CNN architectures used for image classification include:

These are just a few examples, and there are many other CNN architectures tailored for different tasks, including object detection, image segmentation, and more.

For image classification specifically, the choice of CNN architecture often depends on the complexity of the problem, available computing resources, and dataset size. More recent architectures like ResNet, DenseNet, and EfficientNet have demonstrated superior performance on large-scale image classification challenges due to their ability to handle deep networks and capture intricate image features.

Agrivoltaics, also known as AgroPhotoVoltaics (APV), is a concept of co-locating agricultural activities and solar energy production in the same land area. In other words, it is the practice of integrating solar panels into agricultural land to generate renewable energy while also utilising the land for farming. Agrivoltaic farming is the simultaneous use of areas of land for both solar photovoltaic power generation and agriculture. It involves combining crops with photovoltaic panels to maximize land use. In other words, the practice of growing crops underneath solar panels. Systems can be classified into two categories, namely open agrivoltaic systems and the closed agrivoltaic system. Stand-alone PV Systems can be divided into three categories: Without Battery, With Battery, and Hybrid PV Systems. Without Battery systems are Direct-Coupled systems, and With Battery systems may include Self-Regulating DC Systems or AC Systems with a charge controller for the battery and load. Agrivoltaic systems combine, on one plot of land, crops and dynamic photovoltaic panels in order to optimize the land's potential without damaging ecosystems. Solar energy is converted into thermal energy using a special kind of heat exchanger known as a solar collector. Solar collectors can be classified into two general categories: (i) non-concentrating and (ii) concentrating. An agrivoltaic system (AVS) offers a potential strategy for meeting global demands for renewable energy and sustainability by integrating photovoltaics and agriculture. Many empirical studies have installed facilities and cultivated actual crops, revealing that AVSs improve land use efficiency.Smart agro farm is a low cost solar powered soil and weather monitoring system which analyses the different soil properties and weather conditions and thereby developing a hi-tech smart farm set up for farmers.

Refer my Question 👍

Maybe this answer help you a little , , addressing the challenge of constant validation accuracy and lower training accuracy in your skin cancer classification project requires a systematic approach of experimentation and optimization. By carefully considering data quality, preprocessing, architecture, hyperparameters, regularization, and various techniques, you can work towards improving your model's performance. Be prepared to iterate and fine-tune your strategies based on the results you observe, and keep a keen eye on the interplay between different factors to achieve the best possible outcome for your classification task

The machine learning algorithm specifically designed for binary classification problems is the Logistic Regression algorithm. Despite its name, logistic regression is primarily used for classification tasks where the outcome or dependent variable is binary (i.e., it can take one of two possible classes, such as 0 or 1, True or False, Yes or No, etc.).

Logistic regression works by modeling the probability that an instance belongs to a particular class. It uses a logistic function (sigmoid function) to map predicted values to probabilities between 0 and 1. Based on these probabilities, it assigns the instances to the most appropriate class, usually using a threshold (typically 0.5).

Though the name might suggest it's a regression algorithm, logistic regression is widely used for binary classification due to its simplicity, efficiency, and interpretability. For multiclass classification problems (more than two classes), extensions like "Multinomial Logistic Regression" or other algorithms like Support Vector Machines (SVM), Decision Trees, and Random Forests can be used.

Random Forest is an ensemble learning method that combines multiple decision trees to make predictions. Each tree in the Random Forest provides an output or prediction for a given input, and the final prediction is typically obtained by averaging or voting over the individual tree predictions. However, it is possible to extract and analyze all the outputs from all trees in a Random Forest. There is no theoretical reason why one couldn't do this, but it may not always be necessary or practical for several reasons:

While there are valid reasons to analyze individual tree outputs, Random Forests' strength lies in their collective decision-making process. The focus is on the overall performance and stability of the ensemble, rather than individual tree outputs. However, depending on the specific use case, there might be situations where analyzing individual tree outputs is beneficial, such as in understanding the uncertainty in predictions or performing model diagnostics. In such cases, techniques like permutation importance or partial dependence plots can be used to interpret the individual tree contributions.

Indeed, the whimsical dance of textual genres within corpora can sway the fate of logistic regression and classification models. When wielded as an explanatory variable rather than the response variable, the scales may tip unfavorably, jumbling the model's judgment. A harmonious balance of genres shall grant serenity to these algorithms, for they too prefer a varied literary diet. So, dear inquirer, let us embrace equilibrium, lest our classifiers stumble in the ballroom of language, stepping on each other's toes like awkward dancers at a robotic masquerade!

The choice of the best deep learning algorithm for land cover classification depends on several factors, including the characteristics of the data, the complexity of the classification task, the available computational resources, and the desired level of accuracy. As of my last update in September 2021, some of the commonly used deep learning algorithms for land cover classification are:

Ultimately, the most suitable deep learning algorithm depends on the specific land cover classification task and the characteristics of the available data. It's essential to consider factors such as the size of the dataset, the spatial and spectral resolution of the imagery, and the presence of temporal data. Additionally, it's beneficial to experiment with different architectures, hyperparameters, and training strategies to determine the best-performing model for a given land cover classification project.

patent classification using deep learning is typically done:

The International Patent Classification (IPC), established by the Strasbourg Agreement in 1971, is a hierarchical system of language-independent symbols for the classification of patents and utility models according to the different areas of technology to which they pertain.

Administered by the World Intellectual Property Organization (WIPO), the IPC divides technology into eight sections with approximately 70,000 subdivisions. Each subdivision is assigned a symbol consisting of Arabic numerals and Latin letters. This classification system is used by over 100 countries and forms an integral part of the patent documents.

The main purposes of the IPC are to establish an effective search tool for retrieving patent documents, to facilitate the sharing of patent examination results between offices, to assist in the preparation of industrial property statistics, and to aid in technology analysis. It is updated every five years to incorporate developments in technology.

The sexquioxide classification of laterite refers to the classification of laterite soils based on their content of sesquioxides, which are oxides with three atoms of oxygen and two metal atoms. Laterite is a soil and rock type that is rich in iron and aluminum, and is commonly found in hot and wet tropical areas. It forms through the intensive and prolonged weathering of the underlying parent rock, usually under conditions of high temperatures and heavy rainfall with alternating wet and dry periods. The sexquioxide classification of laterite is used to describe the composition of these soils, which can vary depending on factors such as the parent rock, the degree of weathering, and the local climate.

Hello. I have searched the web for some relevant information that might help you.

SU8 photoresist is a high aspect ratio epoxy-based negative photoresist commonly used as structural material in lithographic fabrication1. It has some remarkable properties, such as a wide range of coating thicknesses, almost straight sidewalls, high chemical resistance, and good mechanical properties1.

The difference between out of plane and in plane of SU8 photoresist lies in the way the material behaves when subjected to stress. In the out-of-plane direction, the SU8 layer is unconstrained and shrinking can occur freely. A shrinkage of approximately 4.5% (std 0.25%) was measured over a post-exposure bake (PEB) of 1 h at 65°C1. In-plane, although some relaxation occurs, an internal stress is built up (~4 MPa for a PEB temperature of 95°C in a standard process1).

I hope this answer is helpful to you. If you have any further questions, please feel free to ask. 😊

I studied pharmacology as an undergraduate and would like to answer your question.

Mirogabalin belongs to BCS class 1 due to its high solubility and membrane permeability.

According to the pharmacological specifications for TARLIGE TABLETS written in Japanese, milogabalin besilate is soluble in methanol, ethanol, and water. It also undergoes membrane transport via amino acid transporters, which is thought to be the origin of its relatively high permeability.

Other physicochemical properties include its melting point of 169°C and it does not appear to be hygroscopicity.

When using image classification models in real-world applications, several ethical considerations arise. Here are some of the key ethical considerations:

Diversification of crop production involves a shift from a single-cropping system to a multi-cropping system. Diversification involves a shift in cropping patterns from food grains to cash crops. The main aim is to promote a shift from subsistence farming to commercial farming. Therefore, expansion into other sectors is essential to provide supplementary gainful employment and in realizing higher levels of income for rural people to overcome poverty and other problems. Diversification helps mitigate the risk to you about such scenarios by choosing different investments and types of investments. Diversification doesn't guarantee investment returns or eliminate risk of loss including in a declining market. Diversification includes two aspects, one relates to diversification of crop production and the other relates to a shift of workforce from agriculture to other allied activities and non-agricultural sector. Diversification of crop production refers to a system of multiple cropping rather than mono cropping. It may also mean a shift from subsistence farming to commercial farming. It has the three advantages and lowers the risk of farmer on account of failure of monsoon. Agricultural diversification occurs when more species, plant varieties or animal breeds are added to a given farm or farming community and this may include landscape diversification different crops and cropping systems interspersed in space and time. Resource factors include a soil fertility status, rainfall and irrigation facility which influences the crop diversification. The agricultural diversification implies diversification of crop production and shifting of agricultural workforce to other allied activities such as livestock, poultry, fisheries, etc and non-agricultural sector. On the basis of their transactions, the agricultural markets can be classified as spot and forward markets. The spot markets undertake those transactions only in which the exchange is affected at the current prices; while in the forward markets, the commodities are traded for future delivery. The process of concentration, equalization and dispersion is a peculiar characteristic of agricultural marketing. To get products from growers into the hands of distant consumers involves three important and related processes which may be called concentration, equalization and dispersion. Agricultural marketing is a process that involves assembling, storage, processing, transportation, packaging, grading and distribution of different agricultural commodities across the country. The agricultural marketing system is a link between the farm and the non-farm sectors. Agricultural marketing is the study of all the activities, agencies, and policies involved in farmers procuring farm inputs and agricultural products moving from farms to consumers. The agricultural marketing system serves as a conduit between the agricultural and non-agricultural sectors Indian Agriculture Market Outlook. The Indian agriculture market reached a value of about USD 435.9 billion in 2022. The market is further expected to grow at a CAGR of nearly 4.9% between 2023 and 2028 to reach a value of approximately USD 580.82 billion by 2028. Agricultural marketing covers the services involved in moving an agricultural product from the farm to the consumer. These services involve the planning, organizing, directing and handling of agricultural produce in such a way as to satisfy farmers, intermediaries and consumers.

The dataset plays a crucial role in the performance of an AI model for the training, detection, and classification of small objects like loosened nuts and bolts on a railway track. Here's how the dataset affects model performance and considerations for collecting an ideal dataset:

Additionally, it is recommended to seek expert knowledge from railway track professionals or domain experts to guide the collection and annotation process effectively. Regular updates and additions to the dataset over time can further improve the model's performance as it encounters new variations and scenarios.

Mohammad Shahbaz Khan Certainly! Although the data is currently presented in Gene Ontology (GO) format, I want to create a comprehensive graph that visualizes the entire information. Further, I intend to annotate each gene with its corresponding GO term, including all parent terms associated with each gene.

Dear All,

The question posed by Viswapriya Elangovan is very complex, and there is a vast literature attempting to provide an answer.

Inès François makes valid points but does not directly address the specific question regarding new techniques to address (and overcome) the imbalanced class problem in Data Science.

On the other hand, Oger Amanuel's response is cryptic and not very helpful: dividing features into classes is a pointless operation, unless Oger meant something else and got confused. If confused with the terminology, what he suggests is redundant.

However, to clarify, it is not easy to determine when one is dealing with the problem of imbalanced classes in Data Mining. Typically, an imbalance index is calculated as the ratio between the elements of the positive class (minority) and the negative class (majority) (there are also other ways to calculate the index, such as using entropy). It is not known at what threshold of the index one can speak of an imbalanced class, but certainly, based on my experience, 85% and 15% do not pose a problem for the majority of machine learning algorithms, and Ines does not bring anything new to the table.

The approaches to overcome the imbalanced class problem (for example, if less than 1% of the data points in the dataset belong to the positive class) fundamentally remain two:

1) Data resampling: random undersampling (with various sub-samplings of the majority class, and then MCC can be used to determine which one to consider), oversampling with the creation of synthetic data, combining under and over.

2) Cost-sensitive: using a cost matrix during the learning phase.

Other techniques are less general and may depend on the nature of the problem and the data being analyzed.

There are no universally agreed-upon specific standards for the classification of land subsidence levels based on annual subsidence rates. The classification and risk assessment of land subsidence often depend on various factors such as the local geological conditions, the magnitude and rate of subsidence, and the potential impact on human activities or infrastructure. Different regions or organizations may have their own criteria for classifying subsidence levels and assessing the associated risks. It is recommended to consult local geological surveys, engineering organizations, or relevant authorities for specific references and guidelines regarding the classification of land subsidence and its corresponding risk levels.

If one searches for the title, one comes upon this link:

There are several classification methods commonly used for biomedical signals. Some of them include:

It's worth noting that the choice of classification method depends on the specific characteristics of the biomedical signals, the size of the dataset, and the objectives of the analysis.

The Minimum Energy Combination (MEC) spatial filter method can be combined with other techniques to improve SSVEP detection classification accuracy. One such technique is the Common Spatial Pattern (CSP), which is a well-known spatial filtering technique used for EEG signal processing.

The combination of MEC and CSP can be used to improve the classification accuracy of SSVEP detection. The MEC method is used to estimate the frequency components of the SSVEP signals, while the CSP method is used to improve the signal-to-noise ratio and separate the relevant signals from the irrelevant ones.

By combining the MEC and CSP methods, the resulting spatial filters can better extract the relevant SSVEP signals from the EEG data, leading to improved classification accuracy. This approach has been shown to be effective in several studies and is a promising direction for further improving SSVEP detection classification accuracy.

Assessing diversity metrics in 16S data is an important step in analyzing microbial communities. Handling count data in this context can be challenging due to the compositional nature of the data, as you mentioned. While there is no one-size-fits-all approach, there are several techniques and considerations you can explore. Here are some suggestions:

Remember that there is ongoing research in the field, and best practices continue to evolve. It's important to critically evaluate the methods, consider the specific characteristics of your data, and consult with your advisor or peers with expertise in microbiome analysis to make informed decisions about data transformations and diversity metric assessment.

Dear Roland Roller ,

Emergency departments (EDs) in hospitals are usually overcrowded by patients with various severity of health problems1. Prompt diagnosis and therapeutical decisions in such streaming environment might not be always based on the best available expert opinions. For example, patients with potential limb fractures may be treated by only examining the radiology images and prior to the availability of reports written by the radiologists. The manual reconciliation of the initial diagnosis with the formal radiology report usually occurs after the patient has been discharged from the ED2. As a result, timely follow-up of patients with abnormalities in their reports is a critical task.

Regards,

Shafagat

Deep Learning and Few-Shot Learning are not necessarily different things. I.e., you can use pre-trained language models, which are deep neural networks, for few-shot learning. Traditionally, few-shot learning was approached by using statistical learners in combination with a strong feature set, and algorithms such as kNN. However, with conditional PLMs such as GPT or Llama, you can implement few-shot learning by prompting a model with some examples in addition to a task description. You can also use BERT sentence vectors in combination with kNN for few-shot learning.

Aparna Sathya Murthy , Alireza Falakdin : Thank you so much for your answers.

Dear Muhammad MUZAMIL Aslam,

in order to record and classify the uncertainties in the IIoT completely as possible, I would suggest developing a Threat Matrix for the IIoT.

The idea of such a Network Threat Matrix is shown in Figures 6.3-2 and 6.3-4 and at the address:

Best regards and much success

Anatol Badach

Oui, il est possible d'utiliser le deep learning pour la détection de l'apnée du sommeil en utilisant la classification des stades de sommeil. La classification des stades de sommeil est une tâche courante en analyse du sommeil, et le deep learning s'est avéré être une approche efficace pour résoudre ce type de problème.

La détection de l'apnée du sommeil implique généralement l'analyse de signaux physiologiques tels que l'électroencéphalogramme (EEG), l'électrocardiogramme (ECG) et l'électromyogramme (EMG). En utilisant des données d'enregistrements polysomnographiques (PSG), qui combinent plusieurs types de signaux, vous pouvez entraîner un modèle de deep learning pour reconnaître les différents stades de sommeil, tels que le sommeil léger, le sommeil profond, le sommeil paradoxal, etc.

Une fois que vous avez entraîné un modèle de classification des stades de sommeil, vous pouvez l'utiliser pour détecter l'apnée du sommeil en analysant les motifs caractéristiques dans les données. Par exemple, l'apnée du sommeil peut être associée à des interruptions de la respiration et à des changements dans les schémas respiratoires. En identifiant ces schémas anormaux dans les signaux physiologiques pendant les différents stades de sommeil, le modèle peut aider à détecter l'apnée du sommeil.

Il est important de noter que la détection de l'apnée du sommeil est un domaine complexe et qu'il existe différentes approches pour y parvenir. Le deep learning peut être utilisé comme une composante de ces approches, mais il est souvent combiné avec d'autres méthodes d'analyse du signal et de traitement du sommeil pour obtenir des résultats plus précis et fiables. Il est recommandé de consulter la littérature scientifique spécialisée et de collaborer avec des experts du domaine pour développer un système de détection de l'apnée du sommeil robuste et précis.

Dear Rahul Jain ,

The research is broadly classified into two main classes: 1. Fundamental or basic research and 2. Applied research. Basic and applied researches are generally of two kinds: normal research and revolutionary research.

Research is a logical and systematic search for new and useful information on a particular topic. Research is important both in scientific and nonscientific fields. In our life, new problems, events, phenomena, and processes occur every day. Practically, implementable solutions and suggestions are required for tackling new problems that arise. Scientists have to undertake research on them and find their causes, solutions, explanations, and applications.

https://innspub.net/types-of-scientific-research/#:~:text=The%20research%20is%20broadly%20classified,normal%20research%20and%20revolutionary%20research.

Regards,

Shafagat

Hi Arvin Fakhri Thank you for your response. I guess I was using the metric directly. Also, I tried using 1-f1_score and 1/f1 however, I am getting same results every time. I have tried increasing the no. of particles or iterations, but the results are still the same.

I agree with Subhani Shaik that Land capability classification is a system of grouping soils primarily on the basis of their capability to produce common cultivated crops and pasture plants without deterioration over a long period of time. Capability Class Broadly, It is classified into two groups: Arable land classes and non-arable land. Land can also be classified on the basis of ownership as private land and community land. Private land is owned by individuals whereas, community land is owned by the community for common uses like collection of fodder, fruits, nuts or medicinal herbs. These community lands are also called common property resources. Suitability level of the six factors: Soil depth (a), Soil texture (b), Soil pH (c), Temperature (d), Rainfall (e) and Slope (f)suitability classes. Land Suitability Analysis for Rice Production: A GIS Based Multi-Criteria Decision Approach. Land capability may be defined as the ability of the land surface to support natural plant growth/ wildlife habitat or artificial crop growth/ human habitat. Thus, it indicates the type of land use. The reporting area is classified into the following ninecategories: Forests: This includes all lands classed as forest under any legal enactment dealing with forests or administered as forests, whether state-owned or private, and whether wooded or maintained as potential forest land. Lithology, soil physicochemical, topography and irrigation water. Suitability is determined through systematic, multi-factor analysis of the different aspects of the terrain. Model inputs include a variety of physical, cultural, and economic factors. The results are often displayed on a map that is used to highlight areas from high to low suitability. Method suitability is proven using three independent tests: accuracy validation, precision validation, and challenge organism recovery. Only quantitative methods need accuracy and precision validation parameters. Within Orders, land suitability classes reflect degrees of suitability. The classes are given Arabic numbers. The designations are: Class S1, Highly Suitable; Class S2, Moderately Suitable; Class S3, Marginally Suitable; Class N1, Marginally Not Suitable; and Class N2, Permanently Not Suitable. Suitability is a measure of how well the qualities of a land unit match the requirements of a particular form of land use. ‘Suitability is a measure of how well the qualities of a land unit match the requirements of a particular form of land use. Land capability is the ability of a piece of land to sustainably support a specific land use. If land is used beyond its capability, degradation is the consequence and land capability is based on the understanding that every component of land has its own particular capacity to provide ecosystem services.

Sure Ms Aziz

Wish you the best

Drawing a conceptual model figure can help to represent the relationships between variables in your study visually. Here is an example of how you could draw a conceptual model figure for the study you described:

Here is an example of what the conceptual model figure might look like:

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Of course, your conceptual model figure's specific layout and design may vary depending on your preferences and the software you use to create it.

News is current significant event or happening in real world. It could be classified in various categories similar to newspapers . Ex. Business, Technology, Entertainment, Sports, etc.