Call For Papers by Machine Learning and Applications: An International Journal ( MLAIJ )
6
th International Conference on Machine learning and Cloud Computing (MLCL 2025)
will provide an... more 6
th International Conference on Machine learning and Cloud Computing (MLCL 2025)
will provide an excellent international forum for sharing knowledge and results in theory,
methodology and applications of on Machine Learning & Cloud computing. The aim of the
conference is to provide a platform to the researchers and practitioners from both academia as
well as industry to meet and share cutting-edge development in the field.
This conference aims to bring together researchers and practitioners in all aspects of machine
learning and cloud-centric and outsourced computing, including (but not limited to):
We modeled an SVM radial classification machine learning algorithm to determine the ruptured and
... more We modeled an SVM radial classification machine learning algorithm to determine the ruptured and
unruptured risk of saccular cerebral aneurysms using 60 samples with 6 predictors as the gender, the age,
the Womersley number, the Time-Averaged Wall Shear Stress (TAWSS), the Aspect Ratio (AR) and the
bottleneck of the aneurysms, considering real cases of patients. We reconstructed computationally each
geometry from an angiography image to realize a CFD simulations, where the TAWSS was computed by
CFD analysis. A cross validation method was used in the training sample to validate the classification
model, getting an accuracy of 92.86% in the test sample. This result may be used to help in medical
decisions to avoid a complicated operation when the probability of rupture is low.
The problem of autonomous vehicle navigation between lanes, around obstacles and towards a short ... more The problem of autonomous vehicle navigation between lanes, around obstacles and towards a short term
goal can be solved using Reinforcement Learning. The multi-lane road ahead of a vehicle may be
represented by a Markov Decision Process (MDP) grid-world containing positive and negative rewards,
allowing for practical computation of an optimal path using either value iteration (VI) or policy iteration
(PI).
The world has witnessed mass forced population displacement across the globe. Pop- ulation displa... more The world has witnessed mass forced population displacement across the globe. Pop- ulation displacement has various indications, with different social and policy consequences. Mitigation of the humanitarian crisis requires tracking and predicting the population movements to allocate the necessary resources and inform the policymakers. The set of events that triggers pop-ulation movements can be traced in the news articles. In this paper, we propose the Population Displacement-Signal Extraction Framework (PD-SEF) to explore a large news corpus and extract the signals of forced population displacement. PD-SEF measures and evaluates violence signals, which is a critical factor of forced displacement from it. Following signal extraction, we propose a displacement prediction model based on extracted violence scores. Experimental results indicate the effectiveness of our framework in extracting high quality violence scores and building accurate prediction models.
With the spread of smartphones incorporating various sensors, accelerometers and gyro sensors hav... more With the spread of smartphones incorporating various sensors, accelerometers and gyro sensors have become familiar to us. Based on such situations, sensor-based human activity recognition (HAR) that uses human sensor data to identify human activity has come to use smartphones as data acquisition sources. In the early studies of HAR using smartphones, handcrafted methods were used if various statistical values were required as feature quantities and high accuracy was realized. Meanwhile, the popularization of deep learning in recent years has not been discussed, and its application has been made to HAR. Although deep learning has the advantage of being able to automatically extract feature quantities from data, it has not reached a step beyond precision in handcrafted methods. Furthermore, in the previous research, to divide data by time window of a fixed interval, except for some part, inference could not be performed unless the data for the time window was secured. We attempted to overcome these limitations using recurrent neural network. Our method records higher accuracy than previous studies using convolutional neural network and long short term memory, which are typical methods in deep learning and display results comparable to handcrafted methods. We also succeeded in pre-calculating many feature quantities, whose calculation was a problem in the previous research, and eliminating the time window.
In this project, we continuously collect data from the RSS feeds of traditional news sources. We ... more In this project, we continuously collect data from the RSS feeds of traditional news sources. We apply
several pre-trained implementations of named entity recognition (NER) tools, quantifying the success of
each implementation. We also perform sentiment analysis of each news article at the document, paragraph
and sentence level, with the goal of creating a corpus of tagged news articles that is made available to the
public through a web interface. We show how the data in this corpus could be used to identify bias in news
reporting, and also establish different quantifiable publishing patterns of left-leaning and right-leaning
news organisations.
4
th International Conference on NLP, Data Mining and Machine Learning (NLDML
2025) will provide... more 4
th International Conference on NLP, Data Mining and Machine Learning (NLDML
2025) will provide an excellent international forum for sharing knowledge and results in theory,
methodology and applications of Natural Language Computing, Data Mining and Machine
Learning. It will provide an excellent international forum for sharing knowledge and results in
theory, methodology and applications of Natural Language Computing, Data Mining and Machine
Learning. The Conference looks for significant contributions to all major fields of the Natural
Language Computing, Data Mining and Machine Learning in theoretical and practical aspects.
In today's era the issue of misinformation poses a challenge to public discussions and decision m... more In today's era the issue of misinformation poses a challenge to public discussions and decision making
processes. This study examines how machine learning (ML) models fare in detecting misinformation on
online platforms using the LIAR dataset. By comparing unsupervised and deep learning methods the
research aims to pinpoint the effective strategies for distinguishing between true and false information.
Performance measures like accuracy, precision, recall, F1 score and AUC ROC curve are employed to
evaluate each model's performance. The results indicate that ensemble models that combine ML techniques
tend to outperform others by striking a balance between accuracy and the ability to detect forms of
misinformation. This research contributes to endeavors in fostering digital spaces by enhancing ML tools
capabilities, in identifying and curbing the spread of false information.
2
nd International Conference on Machine Learning and IoT (MLIoT 2024)will provide an
excellent ... more 2
nd International Conference on Machine Learning and IoT (MLIoT 2024)will provide an
excellent international forum for sharing knowledge and results in theory, methodology and
applications of Machine Learning and Internet of Things (IoT).
Authors are solicited to contribute to the conference by submitting articles that illustrate research
results, projects, surveying works and industrial experiences that describe significant advances in
the following areas, but are not limited to these topics only.
Developments in natural language processing (NLP) techniques, convolutional neural networks (CNNs... more Developments in natural language processing (NLP) techniques, convolutional neural networks (CNNs), and long-short- term memory networks (LSTMs) allow for a state-of-the-art automated system capable of predicting the status (pass/fail) of congressional roll call votes. The paper introduces a custom hybrid model labeled "Predict Text Classification Network" (PTCN), which inputs legislation and outputs a prediction of the document's classification (pass/fail). The convolutional layers and the LSTM layers automatically recognize features from the input data's latent space. The PTCN's custom architecture provides elements enabling adaptation to the input's variance from adjustment to the kernel weights over time. On the document level, the model reported an average evaluation of 67.32% using 10-fold crossvalidation. The results suggest that the model can recognize congressional voting behaviors from the associated legislation's language. Overall, the PTCN provides a solution with competitive performance to related systems targeting congressional roll call votes.
6
th International Conference on Signal Processing and Machine Learning (SIGML
2025) will provid... more 6
th International Conference on Signal Processing and Machine Learning (SIGML
2025) will provide an excellent international forum for sharing knowledge and results in
theory, methodology and applications of on Signal Processing and Machine Learning. The
aim of the conference is to provide a platform to the researchers and practitioners from both
academia as well as industry to meet and share cutting-edge development in the field.
Authors are solicited to contribute to the conference by submitting articles that illustrate
research results, projects, surveying works and industrial experiences that describe significant
advances in the areas of Signal Processing and Machine Learning.
6
th International Conference on Natural Language Processing, Information Retrieval and AI
(NIAI... more 6
th International Conference on Natural Language Processing, Information Retrieval and AI
(NIAI 2025) will provide an excellent international forum for sharing knowledge and results in
theory, methodology and applications of Natural Language Computing, Information Retrieval and
AI.
In this new era, where tremendous information is available on the internet, it is of most importa... more In this new era, where tremendous information is available on the internet, it is of most important to provide the improved mechanism to extract the information quickly and most efficiently. It is very difficult for human beings to manually extract the summary of large documents of text. Therefore, there is a problem of searching for relevant documents from the number of documents available, and absorbing relevant information from it. In order to solve the above two problems, the automatic text summarization is very much necessary. Text summarization is the process of identifying the most important meaningful information in a document or set of related documents and compressing them into a shorter version preserving its overall meanings. More specific, Abstractive Text Summarization (ATS), is the task of constructing summary sentences by merging facts from different source sentences and condensing them into a shorter representation while preserving information content and overall meaning. This Paper introduces a newly proposed technique for Summarizing the abstractive newspapers’ articles based on deep learning.
The International Conference on AI, Machine Learning, and Data Science (AIMDS 2024) will be an
e... more The International Conference on AI, Machine Learning, and Data Science (AIMDS 2024) will be an
exceptional virtual platform for sharing knowledge and research findings in the theory, methodology, and
applications of AI, Machine Learning, and Data Science. AIMDS 2024 emphasizes both theoretical
research and practical applications in these fields. The conference seeks high-quality papers that address all
technical aspects of AI, Machine Learning, and Data Science. Submissions from academia, industry, and
government are encouraged, covering both traditional and emerging topics, as well as innovative
paradigms, with a strong focus on real-world problems, systems and applications.
2nd International Conference on Information Theory and Machine Learning (ITEORY 2024) will
provi... more 2nd International Conference on Information Theory and Machine Learning (ITEORY 2024) will
provide an excellent international forum for sharing knowledge and results in theory, methodology and
applications of Information Theory. Authors are solicited to contribute to the conference by submitting
articles that illustrate research results, projects, surveying works and industrial experiences that describe
significant advances in the area soft Information Theory, Applications and Machine Learning.
10th International Conference on Data Mining and Database Management Systems
(DMDBS 2024) will p... more 10th International Conference on Data Mining and Database Management Systems
(DMDBS 2024) will provides a forum for researchers who address this issue and to present
their work in a peer-reviewed forum. Authors are solicited to contribute to the conference by
submitting articles that illustrate research results, projects, surveying works and industrial
experiences that describe significant advances in Data mining and Application
International Conference on Education and Artificial Intelligence (EDUAI 2024) serves as a
pivot... more International Conference on Education and Artificial Intelligence (EDUAI 2024) serves as a
pivotal virtual forum for showcasing groundbreaking ideas, innovative approaches, and
significant research developments at the intersection of education and AI. This event aims to
foster a dynamic exchange of ideas, focusing on emerging trends that require greater attention
and exploration. By offering a platform for collaboration and discussion, EDUAI 2024 seeks to
publish and promote proposals that align with and strengthen our collective goals, driving
forward the integration of AI in educational contexts.
Authors are solicited to contribute to the conference by submitting articles that illustrate research
results, projects, surveying works and industrial experiences that describe significant advances in
the following areas, but are not limited to
4
th International Conference on Automation and Engineering (AUEN 2025) will provide an
excellen... more 4
th International Conference on Automation and Engineering (AUEN 2025) will provide an
excellent international forum for sharing knowledge and results in theory, methodology and new
advances and research results in the fields of Automation and Engineering. The conference will
bring together researchers and practitioners from both academia as well as industry to meet and
share cutting-edge development in the field. The Conference welcomes significant contributions
in all major fields of the Automation in theoretical and practical aspects. Authors are solicited to
contribute to the conference by submitting articles that illustrate research results, projects,
surveying works and industrial experiences that describe significant advances in the following
areas, but are not limited to.
5th International Conference on Data Science and Applications (DSA 2024) will act as a major
for... more 5th International Conference on Data Science and Applications (DSA 2024) will act as a major
forum for the presentation of innovative ideas, approaches, developments, and research projects
in the areas of Data Science and Applications.
This paper delves into the intricate realm of generative Artificial Intelligence (AI) models, spe... more This paper delves into the intricate realm of generative Artificial Intelligence (AI) models, specifically focusing on transformers like GPT (Generative Pre-trained Transformer). Despite their remarkable capabilities, these models pose challenges in terms of interpretability and accountability, owing to their complex architectures and vast training data. This paper employs a model to investigate the importance of words within the corpus, employing sensitivity analysis techniques. Specifically, attention weights are used to measure the impact of individual words on the model's predictions. The paper proposes a novel approach to rank the importance of words by leveraging attention weights and conducting sensitivity analysis across the dataset. To quantify the discrepancies between model-generated outputs and ground truth, the Kullback-Leibler (KL) divergence is employed. This divergence measure aids in evaluating how well the model captures the underlying distribution of words in the corpus. By integrating KL divergence into the sensitivity analysis, the study aims to provide a more comprehensive understanding of word importance.
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Call For Papers by Machine Learning and Applications: An International Journal ( MLAIJ )
th International Conference on Machine learning and Cloud Computing (MLCL 2025)
will provide an excellent international forum for sharing knowledge and results in theory,
methodology and applications of on Machine Learning & Cloud computing. The aim of the
conference is to provide a platform to the researchers and practitioners from both academia as
well as industry to meet and share cutting-edge development in the field.
This conference aims to bring together researchers and practitioners in all aspects of machine
learning and cloud-centric and outsourced computing, including (but not limited to):
unruptured risk of saccular cerebral aneurysms using 60 samples with 6 predictors as the gender, the age,
the Womersley number, the Time-Averaged Wall Shear Stress (TAWSS), the Aspect Ratio (AR) and the
bottleneck of the aneurysms, considering real cases of patients. We reconstructed computationally each
geometry from an angiography image to realize a CFD simulations, where the TAWSS was computed by
CFD analysis. A cross validation method was used in the training sample to validate the classification
model, getting an accuracy of 92.86% in the test sample. This result may be used to help in medical
decisions to avoid a complicated operation when the probability of rupture is low.
goal can be solved using Reinforcement Learning. The multi-lane road ahead of a vehicle may be
represented by a Markov Decision Process (MDP) grid-world containing positive and negative rewards,
allowing for practical computation of an optimal path using either value iteration (VI) or policy iteration
(PI).
several pre-trained implementations of named entity recognition (NER) tools, quantifying the success of
each implementation. We also perform sentiment analysis of each news article at the document, paragraph
and sentence level, with the goal of creating a corpus of tagged news articles that is made available to the
public through a web interface. We show how the data in this corpus could be used to identify bias in news
reporting, and also establish different quantifiable publishing patterns of left-leaning and right-leaning
news organisations.
th International Conference on NLP, Data Mining and Machine Learning (NLDML
2025) will provide an excellent international forum for sharing knowledge and results in theory,
methodology and applications of Natural Language Computing, Data Mining and Machine
Learning. It will provide an excellent international forum for sharing knowledge and results in
theory, methodology and applications of Natural Language Computing, Data Mining and Machine
Learning. The Conference looks for significant contributions to all major fields of the Natural
Language Computing, Data Mining and Machine Learning in theoretical and practical aspects.
processes. This study examines how machine learning (ML) models fare in detecting misinformation on
online platforms using the LIAR dataset. By comparing unsupervised and deep learning methods the
research aims to pinpoint the effective strategies for distinguishing between true and false information.
Performance measures like accuracy, precision, recall, F1 score and AUC ROC curve are employed to
evaluate each model's performance. The results indicate that ensemble models that combine ML techniques
tend to outperform others by striking a balance between accuracy and the ability to detect forms of
misinformation. This research contributes to endeavors in fostering digital spaces by enhancing ML tools
capabilities, in identifying and curbing the spread of false information.
nd International Conference on Machine Learning and IoT (MLIoT 2024)will provide an
excellent international forum for sharing knowledge and results in theory, methodology and
applications of Machine Learning and Internet of Things (IoT).
Authors are solicited to contribute to the conference by submitting articles that illustrate research
results, projects, surveying works and industrial experiences that describe significant advances in
the following areas, but are not limited to these topics only.
th International Conference on Signal Processing and Machine Learning (SIGML
2025) will provide an excellent international forum for sharing knowledge and results in
theory, methodology and applications of on Signal Processing and Machine Learning. The
aim of the conference is to provide a platform to the researchers and practitioners from both
academia as well as industry to meet and share cutting-edge development in the field.
Authors are solicited to contribute to the conference by submitting articles that illustrate
research results, projects, surveying works and industrial experiences that describe significant
advances in the areas of Signal Processing and Machine Learning.
th International Conference on Natural Language Processing, Information Retrieval and AI
(NIAI 2025) will provide an excellent international forum for sharing knowledge and results in
theory, methodology and applications of Natural Language Computing, Information Retrieval and
AI.
exceptional virtual platform for sharing knowledge and research findings in the theory, methodology, and
applications of AI, Machine Learning, and Data Science. AIMDS 2024 emphasizes both theoretical
research and practical applications in these fields. The conference seeks high-quality papers that address all
technical aspects of AI, Machine Learning, and Data Science. Submissions from academia, industry, and
government are encouraged, covering both traditional and emerging topics, as well as innovative
paradigms, with a strong focus on real-world problems, systems and applications.
provide an excellent international forum for sharing knowledge and results in theory, methodology and
applications of Information Theory. Authors are solicited to contribute to the conference by submitting
articles that illustrate research results, projects, surveying works and industrial experiences that describe
significant advances in the area soft Information Theory, Applications and Machine Learning.
(DMDBS 2024) will provides a forum for researchers who address this issue and to present
their work in a peer-reviewed forum. Authors are solicited to contribute to the conference by
submitting articles that illustrate research results, projects, surveying works and industrial
experiences that describe significant advances in Data mining and Application
pivotal virtual forum for showcasing groundbreaking ideas, innovative approaches, and
significant research developments at the intersection of education and AI. This event aims to
foster a dynamic exchange of ideas, focusing on emerging trends that require greater attention
and exploration. By offering a platform for collaboration and discussion, EDUAI 2024 seeks to
publish and promote proposals that align with and strengthen our collective goals, driving
forward the integration of AI in educational contexts.
Authors are solicited to contribute to the conference by submitting articles that illustrate research
results, projects, surveying works and industrial experiences that describe significant advances in
the following areas, but are not limited to
th International Conference on Automation and Engineering (AUEN 2025) will provide an
excellent international forum for sharing knowledge and results in theory, methodology and new
advances and research results in the fields of Automation and Engineering. The conference will
bring together researchers and practitioners from both academia as well as industry to meet and
share cutting-edge development in the field. The Conference welcomes significant contributions
in all major fields of the Automation in theoretical and practical aspects. Authors are solicited to
contribute to the conference by submitting articles that illustrate research results, projects,
surveying works and industrial experiences that describe significant advances in the following
areas, but are not limited to.
forum for the presentation of innovative ideas, approaches, developments, and research projects
in the areas of Data Science and Applications.
th International Conference on Machine learning and Cloud Computing (MLCL 2025)
will provide an excellent international forum for sharing knowledge and results in theory,
methodology and applications of on Machine Learning & Cloud computing. The aim of the
conference is to provide a platform to the researchers and practitioners from both academia as
well as industry to meet and share cutting-edge development in the field.
This conference aims to bring together researchers and practitioners in all aspects of machine
learning and cloud-centric and outsourced computing, including (but not limited to):
unruptured risk of saccular cerebral aneurysms using 60 samples with 6 predictors as the gender, the age,
the Womersley number, the Time-Averaged Wall Shear Stress (TAWSS), the Aspect Ratio (AR) and the
bottleneck of the aneurysms, considering real cases of patients. We reconstructed computationally each
geometry from an angiography image to realize a CFD simulations, where the TAWSS was computed by
CFD analysis. A cross validation method was used in the training sample to validate the classification
model, getting an accuracy of 92.86% in the test sample. This result may be used to help in medical
decisions to avoid a complicated operation when the probability of rupture is low.
goal can be solved using Reinforcement Learning. The multi-lane road ahead of a vehicle may be
represented by a Markov Decision Process (MDP) grid-world containing positive and negative rewards,
allowing for practical computation of an optimal path using either value iteration (VI) or policy iteration
(PI).
several pre-trained implementations of named entity recognition (NER) tools, quantifying the success of
each implementation. We also perform sentiment analysis of each news article at the document, paragraph
and sentence level, with the goal of creating a corpus of tagged news articles that is made available to the
public through a web interface. We show how the data in this corpus could be used to identify bias in news
reporting, and also establish different quantifiable publishing patterns of left-leaning and right-leaning
news organisations.
th International Conference on NLP, Data Mining and Machine Learning (NLDML
2025) will provide an excellent international forum for sharing knowledge and results in theory,
methodology and applications of Natural Language Computing, Data Mining and Machine
Learning. It will provide an excellent international forum for sharing knowledge and results in
theory, methodology and applications of Natural Language Computing, Data Mining and Machine
Learning. The Conference looks for significant contributions to all major fields of the Natural
Language Computing, Data Mining and Machine Learning in theoretical and practical aspects.
processes. This study examines how machine learning (ML) models fare in detecting misinformation on
online platforms using the LIAR dataset. By comparing unsupervised and deep learning methods the
research aims to pinpoint the effective strategies for distinguishing between true and false information.
Performance measures like accuracy, precision, recall, F1 score and AUC ROC curve are employed to
evaluate each model's performance. The results indicate that ensemble models that combine ML techniques
tend to outperform others by striking a balance between accuracy and the ability to detect forms of
misinformation. This research contributes to endeavors in fostering digital spaces by enhancing ML tools
capabilities, in identifying and curbing the spread of false information.
nd International Conference on Machine Learning and IoT (MLIoT 2024)will provide an
excellent international forum for sharing knowledge and results in theory, methodology and
applications of Machine Learning and Internet of Things (IoT).
Authors are solicited to contribute to the conference by submitting articles that illustrate research
results, projects, surveying works and industrial experiences that describe significant advances in
the following areas, but are not limited to these topics only.
th International Conference on Signal Processing and Machine Learning (SIGML
2025) will provide an excellent international forum for sharing knowledge and results in
theory, methodology and applications of on Signal Processing and Machine Learning. The
aim of the conference is to provide a platform to the researchers and practitioners from both
academia as well as industry to meet and share cutting-edge development in the field.
Authors are solicited to contribute to the conference by submitting articles that illustrate
research results, projects, surveying works and industrial experiences that describe significant
advances in the areas of Signal Processing and Machine Learning.
th International Conference on Natural Language Processing, Information Retrieval and AI
(NIAI 2025) will provide an excellent international forum for sharing knowledge and results in
theory, methodology and applications of Natural Language Computing, Information Retrieval and
AI.
exceptional virtual platform for sharing knowledge and research findings in the theory, methodology, and
applications of AI, Machine Learning, and Data Science. AIMDS 2024 emphasizes both theoretical
research and practical applications in these fields. The conference seeks high-quality papers that address all
technical aspects of AI, Machine Learning, and Data Science. Submissions from academia, industry, and
government are encouraged, covering both traditional and emerging topics, as well as innovative
paradigms, with a strong focus on real-world problems, systems and applications.
provide an excellent international forum for sharing knowledge and results in theory, methodology and
applications of Information Theory. Authors are solicited to contribute to the conference by submitting
articles that illustrate research results, projects, surveying works and industrial experiences that describe
significant advances in the area soft Information Theory, Applications and Machine Learning.
(DMDBS 2024) will provides a forum for researchers who address this issue and to present
their work in a peer-reviewed forum. Authors are solicited to contribute to the conference by
submitting articles that illustrate research results, projects, surveying works and industrial
experiences that describe significant advances in Data mining and Application
pivotal virtual forum for showcasing groundbreaking ideas, innovative approaches, and
significant research developments at the intersection of education and AI. This event aims to
foster a dynamic exchange of ideas, focusing on emerging trends that require greater attention
and exploration. By offering a platform for collaboration and discussion, EDUAI 2024 seeks to
publish and promote proposals that align with and strengthen our collective goals, driving
forward the integration of AI in educational contexts.
Authors are solicited to contribute to the conference by submitting articles that illustrate research
results, projects, surveying works and industrial experiences that describe significant advances in
the following areas, but are not limited to
th International Conference on Automation and Engineering (AUEN 2025) will provide an
excellent international forum for sharing knowledge and results in theory, methodology and new
advances and research results in the fields of Automation and Engineering. The conference will
bring together researchers and practitioners from both academia as well as industry to meet and
share cutting-edge development in the field. The Conference welcomes significant contributions
in all major fields of the Automation in theoretical and practical aspects. Authors are solicited to
contribute to the conference by submitting articles that illustrate research results, projects,
surveying works and industrial experiences that describe significant advances in the following
areas, but are not limited to.
forum for the presentation of innovative ideas, approaches, developments, and research projects
in the areas of Data Science and Applications.
unruptured risk of saccular cerebral aneurysms using 60 samples with 6 predictors as the gender, the age,
the Womersley number, the Time-Averaged Wall Shear Stress (TAWSS), the Aspect Ratio (AR) and the
bottleneck of the aneurysms, considering real cases of patients. We reconstructed computationally each
geometry from an angiography image to realize a CFD simulations, where the TAWSS was computed by
CFD analysis. A cross validation method was used in the training sample to validate the classification
model, getting an accuracy of 92.86% in the test sample. This result may be used to help in medical
decisions to avoid a complicated operation when the probability of rupture is low.
Earlier studies on cancer classification have limited diagnostic ability. The recent development of DNA
microarray technology has made monitoring of thousands of gene expression simultaneously. By using this
abundance of gene expression data researchers are exploring the possibilities of cancer classification.
There are number of methods proposed with good results, but lot of issues still need to be addressed. This
paper present an overview of various cancer classification methods and evaluate these proposed methods
based on their classification accuracy, computational time and ability to reveal gene information. We have
also evaluated and introduced various proposed gene selection method. In this paper, several issues
related to cancer classification have also been discussed.
cartoon images. The similarities between a query cartoon character image and the images in database are
computed by the feature extraction using the fusion descriptors of SIFT (Scale Invariant Feature
Transforms) and HOG (Histogram of Gradient). Based on the similarities, the cartoon images same or
similar to query images are identified and retrieved. This method makes use of indexing technique for more
efficient and scalable retrieval of the cartoon character. The experiment results demonstrate that the
proposed method is efficient in retrieving the cartoon images from the large database.
translations. They only give the general meaning of a sentence not the exact translation. As Machine
Translation (MT) is gaining a position in the whole world, there is a need for estimating the quality of
machine translation outputs. Many prominent MT-Researchers are trying to make the MT-System, that
produces very good and accurate translations and that also covers maximum language pairs. If good
translations out of all translations can be categorized then the time and cost can be saved to a great extent.
Now, Good quality translations will be sent for post-editing and rest will be sent for pre-editing or
retranslation. In this paper, Kneser Ney smoothing language model is used to calculate the probability of
machine translated output. But a translation cannot be said good or bad. Based on its probability score
there are many other parameters that effect its quality. The quality of machine translation is made easier to
estimate for post-editing by using two different predefined famous algorithms for classification.
computational method is required to predict the function of enzymes. Many feature selection technique
have been used in this paper by examining many previous research paper. This paper presents supervised
machine learning approach to predict the functional classes and subclass of enzymes based on set of 857
sequence derived features. It uses seven sequence derived properties including amino acid composition,
dipeptide composition, correlation feature, composition, transition, distribution and pseudo amino acid
composition .Support vector machine recursive Feature elimination (SVRRFE) is used to select the optimal
number of features. The Random Forest has been used to construct a three level model with optimal
number of features selected by SVMRFE, where top level distinguish a query protein as an enzyme or nonenzyme,
second level predicts the enzyme functional class and the third layer predict the sub functional
class. The proposed model reported overall accuracy of 100%, precision of 100% and MCC value of 1.00
for the first level, whereas accuracy of 90.1%,precision of 90.5% and MCC value of 0.88 for second level
and accuracy of 88.0%, precision of 88.7% and MCC value of 0.87 for the third level.
classifiers, Naive Bayes and SVM, is investigated in combination with different feature selection schemes to
obtain the results for sentiment analysis. Thirdly, the proposed model for sentiment analysis is extended to
obtain the results for higher order n-grams.
l part of crime detection and prevention. In this
research, we use WEKA, an open source data mining s
oftware, to conduct a comparative study between the
violent crime patterns from the Communities and Cri
me Unnormalized Dataset provided by the University
of California-Irvine repository and actual crime st
atistical data for the state of Mississippi that ha
s been
provided by neighborhoodscout.com. We implemented t
he Linear Regression, Additive Regression, and
Decision Stump algorithms using the same finite set
of features, on the Communities and Crime Dataset.
Overall, the linear regression algorithm performed
the best among the three selected algorithms. The s
cope
of this project is to prove how effective and accur
ate the machine learning algorithms used in data mi
ning
analysis can be at predicting violent crime pattern
s.
aid of predicting lexical categories of unknown words is the use of syntactical knowledge of the language. The distinction between open class words and closed class words together with syntactical features of the language used in this research to predict lexical categories of unknown words in the tagging process. An experiment is performed to investigate the ability of the approach to parse unknown words using syntactical knowledge without human intervention. This experiment shows that the performance of the tagging process is enhanced when word class distinction is used together with syntactic rules to parse sentences containing unknown words in Sinhala language.
suggested with a list of products or items they may interest by analyzing their browsing or purchasing
history. These systems generally require user-item rating information to find similar users (neighbors) in
order to produce a list of suggested items. However, explicit user-item rating data is difficult to collect in
real world applications because most users do not want to give item ratings explicitly. Nowadays, social
tagging applications have the content of items as well as users’ interest and preferences. Therefore, this
paper proposes an alternative approach based on social tagging information to improve the performance
of RS The proposed system extracts latent topics from tagging data and uses these topics to build user
profile to be used in the system for resource recommendation. The proposed system is tested by using the
real world datasets of popular social tagging applications. The experimental results show that the
proposed system outperforms the other state-of-the-art approaches.
impacts caused by the season, the imaging condition and so on. To optimize the state-of-the-art algorithms
and to deal with the mentioned difficulty, a novel unsupervised classification algorithm is proposed based
on deep learning, where the complex correspondence among the images is built up by Auto-encoder Model.
With the proper usage of deep neural network model, we could classify differencing SAR images into two
classes more accurately and preciously. Experiments well demonstrate the effectiveness of the proposed
approach.
tems is to present users with information that most
relevant user needs. So, IR researchers have begun to expand their efforts to understand the nature of the
information need that users express in their queries. If system is able to understand the intensi
on behind
user’s needs and contents, it will retrieve more accurate results. This system presents algorithm and
techniques for increasing a search service's understanding of user search queries.
Web query classification
is to classify a web search query in
to a set of user intended categories. Previous query classification
techniques performed classification process on query logs and
neighbouring
queries in search session time.
We propose Query Classification Algorithm (QCA) for automatic topical classificat
ion of web queries
based on domain specific ontology. Ontology is a specialization of concepts in domain and relationships
that holds between those concepts. Using ontology as a controlled vocabulary in the process of
classification, performance accuracy i
s improved in the classification process.
Evaluation of classification
accuracy and retrieving performance are explored. The system measures the performance accuracy of
retrieving
documents
by using the number of documents relevant with the user intended c
ategory by the
total
number of
retrieved
documents. Classification accuracy is measured with recall, precision and f
-
measure
that will easily lead to a remarkable growth in bandwidth demand by in-vehicle users. In Examples the
applications of vehicular communication proliferate, and range from the updating of road maps to the
repossession of nearby points of interest, downloading of touristic information and multimedia files. This
content downloading system will induce the vehicular user to use the resource to the same extent as today’s
mobile customers. By this approach communication-enabled vehicles are paying attention in downloading
different contents from Internet-based servers. We summarize the performance limits of such a vehicular
multimedia content downloading system by modeling the content downloading process as an effective
problem and developing the overall system throughput with density measurement. Results highlight the
methods where the Roadside infrastructure i.e., access points are working at different capabilities
irrespective of vehicle density, the vehicle-to-vehicle communication.
discrete Kalman filter as an iris tracker. In an event driven scenario, the proposed iris-tracking method is
used to locate and track the subject’s eye movement and gesture by comparing it to a set of classified
normal and typical gestures. The database is based on available videos as well as a number of experiments
conducted with normal subjects. Proposed approach Utilized feature based methods of face, such as skin
color while detection of the eyes utilized a histogram-based approach and SVM was used as a two-class
classifier to divide region into eyes and non- eyes patterns. Based on the results, the proposed approach
provide an efficient eye detection and tracking method with an average of 99.1% accuracy.
retrieval, speech signals, fMRI scans, electrocardiogram signal analysis, multimedia retrieval, market
based applications etc., to improve the performance of the system, the dimensions should be reduced into
lower dimension. There are many techniques for both linear and non linear dimensionality reduction. Some
of the techniques are suitable linear sample data and not suitable for non linear data and sample size is
another criteria in dimensionality reduction. Each technique has its own features and limitations. This
paper presents the various techniques used to reduce the dimensions of the data.
Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to:
The journal is devoted to the publication of high quality papers on theoretical and practical aspects of machine learning and applications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on machine learning dvancements, and establishing new collaborations in these areas. Original research papers, state-of-the-art reviews are invited for publication in all areas of machine learning.
preserving its overall meanings. More specific, Abstractive Text Summarization (ATS), is the task of constructing summary sentences by merging facts from different source sentences and condensing them into a shorter representation while preserving information content and overall meaning. This Paper introduces a newly proposed technique for Summarizing the abstractive newspapers’ articles based on deep learning.
th International Conference on NLP Trends & Technologies (NLPTT 2024) will provide an excellent
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Natural Language Computing technologies and its applications.
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following areas, but are not limited to:
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Authors are solicited to contribute to the conference by submitting articles that illustrate
research results, projects, surveying works and industrial experiences that describe significant
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provide an excellent international forum for sharing knowledge and results in theory,
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Authors are solicited to contribute to the conference by submitting articles that illustrate research
results, projects, surveying works and industrial experiences that describe significant advances in
the following areas, but are not limited to these topics only.
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Authors are solicited to contribute to the Conference by submitting articles that illustrate
research results, projects, surveying works and industrial experiences that describe significant
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th International Conference on Big Data, Machine Learning and IoT (BMLI 2024) will act as a
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th International Conference on NLP & Artificial Intelligence Techniques (NLAI 2023)
will provide an excellent international forum for sharing knowledge and results in theory,
methodology and applications of NLP & Artificial Intelligence Techniques. The Conference
looks for significant contributions to all major fields of NLP and Artificial Intelligence in
theoretical and practical aspects. The aim of the Conference is to provide a platform to the
researchers and practitioners from both academia as well as industry to meet and share
cutting-edge development in the field.
Authors are solicited to contribute to the conference by submitting articles that illustrate
research results, projects, surveying works and industrial experiences that describe significant
advances in the following areas, but are not limited to.
4 th International Conference on Data Science and Machine Learning (DSML 2023) will act as a major forum for the presentation of innovative ideas, approaches, developments, and research projects in the areas of Data Science and Machine Learning. It will also serve to facilitate the exchange of information between researchers and industry professionals to discuss the latest issues and advancement in the area of Data Science and Machine Learning. Authors are solicited to contribute to the Conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the Computer Networks & Communications.