ADKU/ResearchGPT_model_dataset huggingface dataset:
Comparative Analysis of Machine Learning, Ensemble Learning and Deep Learning Classifiers for Parkinson’s Disease Detection
Authors: Palak Goyal, Rinkle Rani

A progressive neurodegenerative ailment called Parkinson's disease (PD) is marked by the death of dopamine-producing cells in the substantia nigra area of the brain. The exact etiology of PD remains elusive, but it is believed to involve the presence of Lewy bodies, abnormal protein aggregates, in affected brain regions, leading to the mobile symptoms of PD. Hence, as the management of PD continues to evolve, there is a growing demand for the establishment of a descriptive system that enables the early detection of PD. In this study, we conducted an extensive analysis using machine learning, ensemble learning, and deep learning models with different hyperparameters to develop accurate classification models for PD prediction. To enhance classifier performance and address overfitting, we employed principal component analysis (PCA) for feature selection along with various preprocessing techniques. The dataset used consisted of voice samples, comprising 188 PD patients and 64 normal individuals. Our results demonstrated that the Random Forest (RF) model with accuracy of 82.37% outperformed the other base classifiers Among the ensemble classifiers, the LGBM model exhibited the highest accuracy of 85.90% when compared to both base and ensemble classifiers. Notably, the deep learning model has 91.33% training accuracy and 85.02% testing accuracy, suggesting that deep learning models perform comparably equivalent on small datasets compared to machine learning classifiers. Overall, our findings underscore the effectiveness of machine learning, ensemble techniques and deep learning models in accurately predicting PD.
Employing deep learning and transfer learning for accurate brain tumor detection
Authors: Sandeep Kumar Mathivanan, Sridevi Sonaimuthu, Sankar Murugesan, Hariharan Rajadurai, Basu Dev Shivahare, Mohd Asif Shah

Artificial intelligence-powered deep learning methods are being used to diagnose brain tumors with high accuracy, owing to their ability to process large amounts of data. Magnetic resonance imaging stands as the gold standard for brain tumor diagnosis using machine vision, surpassing computed tomography, ultrasound, and X-ray imaging in its effectiveness. Despite this, brain tumor diagnosis remains a challenging endeavour due to the intricate structure of the brain. This study delves into the potential of deep transfer learning architectures to elevate the accuracy of brain tumor diagnosis. Transfer learning is a machine learning technique that allows us to repurpose pre-trained models on new tasks. This can be particularly useful for medical imaging tasks, where labelled data is often scarce. Four distinct transfer learning architectures were assessed in this study: ResNet152, VGG19, DenseNet169, and MobileNetv3. The models were trained and validated on a dataset from benchmark database: Kaggle. Five-fold cross validation was adopted for training and testing. To enhance the balance of the dataset and improve the performance of the models, image enhancement techniques were applied to the data for the four categories: pituitary, normal, meningioma, and glioma. MobileNetv3 achieved the highest accuracy of 99.75%, significantly outperforming other existing methods. This demonstrates the potential of deep transfer learning architectures to revolutionize the field of brain tumor diagnosis.
Detecting Suicidality in Arabic Tweets Using Machine Learning and Deep Learning Techniques
Authors: Asma Abdulsalam, Areej Alhothali, Saleh Al-Ghamdi

Social media platforms have revolutionized traditional communication techniques by allowing people to connect instantaneously, openly, and frequently. As people use social media to share personal stories and express their opinions, negative emotions such as thoughts of death, self-harm, and hardship are commonly expressed, particularly among younger generations. Accordingly, the use of social media to detect suicidality may help provide proper intervention that will ultimately deter the spread of self-harm and suicidal ideation on social media. To investigate the automated detection of suicidal thoughts in Arabic tweets, we developed a novel Arabic suicidal tweet dataset, examined several machine learning models trained on word frequency and embedding features, and investigated the performance of pre-trained deep learning models in identifying suicidal sentiment. The results indicate that the support vector machine trained on character n-gram features yields the best performance among conventional machine learning models, with an accuracy of 86% and F1 score of 79%. In the subsequent deep learning experiment, AraBert outperformed all other machine and deep learning models with an accuracy of 91% and F1-score of 88%, significantly improving the detection of suicidal ideation in the dataset. To the best of our knowledge, this study represents the first attempt to compile an Arabic suicidality detection dataset from Twitter and to use deep learning to detect suicidal sentiment in Arabic posts.
Deep learning for code generation: a survey
Authors: Huangzhao Zhang, Kechi Zhang, Zhuo Li, Jia Li, Jia Li, Yongmin Li, Yunfei Zhao, Yuqi Zhu, Fang Liu, Ge Li, Zhi Jin

In the past decade, thanks to the powerfulness of deep-learning techniques, we have witnessed a whole new era of automated code generation. To sort out developments, we have conducted a comprehensive review of solutions to deep learning-based code generation. In this survey, we generally formalize the pipeline and procedure of code generation and categorize existing solutions according to taxonomy from perspectives of architecture, model-agnostic enhancing strategy, metrics, and tasks. In addition, we outline the challenges faced by current dominant large models and list several plausible directions for future research. We hope that this survey may provide handy guidance to understanding, utilizing, and developing deep learning-based code-generation techniques for researchers and practitioners.
Privacy enhanced course recommendations through deep learning in Federated Learning environments
Authors: Chandra Sekhar Kolli, Sreenivasu Seelamanthula, Venkata Krishna Reddy V, Padamata Ramesh Babu, Mule Rama Krishna Reddy, Babu Rao Gumpina

The increasing concerns around data security and privacy among users have significantly pushed the interest of the research community towards developing privacy-preserving recommendation systems. Amidst this backdrop, our study introduces a novel course recommendation methodology leveraging Federated Learning (FL) coupled with advanced Deep Learning techniques. This method executes the recommendation process across local nodes through several stages, including agglomerative matrix formulation, course clustering, bi-level matching, identification of learner-preferred courses, and ultimately, course recommendation. Notably, course clustering is achieved through Deep Fuzzy Clustering (DFC), while Deep Convolutional Neural Networks (DCNN) are employed for the recommendation phase. The efficacy of our DFC-DCNN-FL approach is rigorously evaluated based on several metrics: accuracy, False Positive Rate (FPR), loss function, Mean Square Error (MSE), Root MSE (RMSE), and Mean Average Precision (MAP). The results demonstrate remarkable performance with scores of 0.909, 0.116, 0.126, 0.291, 0.539, and 0.925, respectively.
DEEP-squared: deep learning powered De-scattering with Excitation Patterning
Authors: Navodini Wijethilake, Mithunjha Anandakumar, Cheng Zheng, Peter T. C. So, Murat Yildirim, Dushan N. Wadduwage

Limited throughput is a key challenge in in vivo deep tissue imaging using nonlinear optical microscopy. Point scanning multiphoton microscopy, the current gold standard, is slow especially compared to the widefield imaging modalities used for optically cleared or thin specimens. We recently introduced “De-scattering with Excitation Patterning” or “DEEP” as a widefield alternative to point-scanning geometries. Using patterned multiphoton excitation, DEEP encodes spatial information inside tissue before scattering. However, to de-scatter at typical depths, hundreds of such patterned excitations were needed. In this work, we present DEEP2, a deep learning-based model that can de-scatter images from just tens of patterned excitations instead of hundreds. Consequently, we improve DEEP’s throughput by almost an order of magnitude. We demonstrate our method in multiple numerical and experimental imaging studies, including in vivo cortical vasculature imaging up to 4 scattering lengths deep in live mice.
Harnessing deep learning for population genetic inference
Authors: Xin Huang, Aigerim Rymbekova, Olga Dolgova, Oscar Lao, Martin Kuhlwilm

In population genetics, the emergence of large-scale genomic data for various species and populations has provided new opportunities to understand the evolutionary forces that drive genetic diversity using statistical inference. However, the era of population genomics presents new challenges in analysing the massive amounts of genomes and variants. Deep learning has demonstrated state-of-the-art performance for numerous applications involving large-scale data. Recently, deep learning approaches have gained popularity in population genetics; facilitated by the advent of massive genomic data sets, powerful computational hardware and complex deep learning architectures, they have been used to identify population structure, infer demographic history and investigate natural selection. Here, we introduce common deep learning architectures and provide comprehensive guidelines for implementing deep learning models for population genetic inference. We also discuss current challenges and future directions for applying deep learning in population genetics, focusing on efficiency, robustness and interpretability.
An enhanced deep learning method for multi-class brain tumor classification using deep transfer learning
Authors: Sohaib Asif, Ming Zhao, Fengxiao Tang, Yusen Zhu

Multi-class brain tumor classification is an important area of research in the field of medical imaging because of the different tumor characteristics. One such challenging problem is the multiclass classification of brain tumors using MR images. Since accuracy is critical in classification, computer vision researchers are introducing a number of techniques; However, achieving high accuracy remains challenging when classifying brain images. Early diagnosis of brain tumor types can activate timely treatment, thereby improving the patient’s chances of survival. In recent years, deep learning models have achieved promising results, especially in classifying brain tumors to help neurologists. This work proposes a deep transfer learning model that accelerates brain tumor detection using MR imaging. In this paper, five popular deep learning architectures are utilized to develop a system for diagnosing brain tumors. The architectures used is this paper are Xception, DenseNet201, DenseNet121, ResNet152V2, and InceptionResNetV2. The final layer of these architectures has been modified with our deep dense block and softmax layer as the output layer to improve the classification accuracy. This article presents two main experiments to assess the effectiveness of the proposed model. First, three-class results using images from patients with glioma, meningioma, and pituitary are discussed. Second, the results of four classes are discussed using images of glioma, meningioma, pituitary and healthy patients. The results show that the proposed model based on Xception architecture is the most suitable deep learning model for detecting brain tumors. It achieves a classification accuracy of 99.67% on the 3-class dataset and 95.87% on the 4-class dataset, which is better than the state-of-the-art methods. In conclusion, the proposed model can provide radiologists with an automated medical diagnostic system to make fast and accurate decisions.
A Procedural Constructive Learning Mechanism with Deep Reinforcement Learning for Cognitive Agents
Authors: Leonardo de Lellis Rossi, Eric Rohmer, Paula Dornhofer Paro Costa, Esther Luna Colombini, Alexandre da Silva Simões, Ricardo Ribeiro Gudwin

Recent advancements in AI and deep learning have created a growing demand for artificial agents capable of performing tasks within increasingly complex environments. To address the challenges associated with continuous learning constraints and knowledge capacity in this context, cognitive architectures inspired by human cognition have gained significance. This study contributes to existing research by introducing a cognitive-attentional system employing a constructive neural network-based learning approach for continuous acquisition of procedural knowledge. We replace an incremental tabular Reinforcement Learning algorithm with a constructive neural network deep reinforcement learning mechanism for continuous sensorimotor knowledge acquisition, thereby enhancing the overall learning capacity. The primary emphasis of this modification centers on optimizing memory utilization and reducing training time. Our study presents a learning strategy that amalgamates deep reinforcement learning with procedural learning, mirroring the incremental learning process observed in human sensorimotor development. This approach is embedded within the CONAIM cognitive-attentional architecture, leveraging the cognitive tools of CST. The proposed learning mechanism allows the model to dynamically create and modify elements in its procedural memory, facilitating the reuse of previously acquired functions and procedures. Additionally, it equips the model with the capability to combine learned elements to effectively adapt to complex scenarios. A constructive neural network was employed, initiating with an initial hidden layer comprising one neuron. However, it possesses the capacity to adapt its internal architecture in response to its performance in procedural and sensorimotor learning tasks, inserting new hidden layers or neurons. Experimentation conducted through simulations involving a humanoid robot demonstrates the successful resolution of tasks that were previously unsolved through incremental knowledge acquisition. Throughout the training phase, the constructive agent achieved a minimum of 40% greater rewards and executed 8% more actions when compared to other agents. In the subsequent testing phase, the constructive agent exhibited a 15% increase in the number of actions performed in contrast to its counterparts.
Classification of Different Plant Species Using Deep Learning and Machine Learning Algorithms
Authors: Siddharth Singh Chouhan, Uday Pratap Singh, Utkarsh Sharma, Sanjeev Jain

In the present situation, a lot of research has been directed towards the potency of plants. These natural resources contain characteristics valuable in combat against a number of diseases. But due to lack of familiarity of these plants among human beings, an appropriate advantage of their significance cannot be drawn away. Plants also shares the certain similar characteristics of leaves like color, texture, shape or size, making them hard to classify them among others. So, to eradicate this problem, a deep learning model has been used for the purpose for classification of different plants species captured in real-time using internet of things practice. Six different plants namely Ashwagandha, Black Pepper, Garlic, Ginger, Basil, and Turmeric has been selected for this purpose. Our proposed convolutional neural network (CNN) model achieved higher performance with an accuracy of 99% when compared with other benchmark deep learning models. Also, to analyze the performance of deep learning versus machine learning models like logistic regression, decision tree, random forest, Gaussian naïve Bayes, support vector machine results were evaluated and when compared CNN outperforms against all machine learning models. The future study will be directed towards the automated plant growth estimation.
A deep learning model for anti-inflammatory peptides identification based on deep variational autoencoder and contrastive learning
Authors: Yujie Xu, Shengli Zhang, Feng Zhu, Yunyun Liang

As a class of biologically active molecules with significant immunomodulatory and anti-inflammatory effects, anti-inflammatory peptides have important application value in the medical and biotechnology fields due to their unique biological functions. Research on the identification of anti-inflammatory peptides provides important theoretical foundations and practical value for a deeper understanding of the biological mechanisms of inflammation and immune regulation, as well as for the development of new drugs and biotechnological applications. Therefore, it is necessary to develop more advanced computational models for identifying anti-inflammatory peptides. In this study, we propose a deep learning model named DAC-AIPs based on variational autoencoder and contrastive learning for accurate identification of anti-inflammatory peptides. In the sequence encoding part, the incorporation of multi-hot encoding helps capture richer sequence information. The autoencoder, composed of convolutional layers and linear layers, can learn latent features and reconstruct features, with variational inference enhancing the representation capability of latent features. Additionally, the introduction of contrastive learning aims to improve the model's classification ability. Through cross-validation and independent dataset testing experiments, DAC-AIPs achieves superior performance compared to existing state-of-the-art models. In cross-validation, the classification accuracy of DAC-AIPs reached around 88%, which is 7% higher than previous models. Furthermore, various ablation experiments and interpretability experiments validate the effectiveness of DAC-AIPs. Finally, a user-friendly online predictor is designed to enhance the practicality of the model, and the server is freely accessible at http://dac-aips.online.
Fraud Detection Using Machine Learning and Deep Learning
Authors: Akash Gandhar, Kapil Gupta, Aman Kumar Pandey, Dharm Raj

Detecting fraudulent activities is a major worry for businesses and financial organizations because they can result in significant financial losses and reputational harm. Traditional fraud detection a method frequently depend on present rules and patterns that skilled scammer can easily circumvent. Machine learning and deep learning algorithms have surfaced as promising methods for detecting fraud in order to handle this problem. Authors present a thorough overview of the most recent ML and DL techniques for fraud identification in this article. These approaches are classified based on their fundamental tactics, which include supervised learning, unsupervised learning, and reinforcement learning. We review recent developments in each area, as well as their strengths and weaknesses. Additionally, we draw attention to some of the major problems with imbalanced datasets, adversarial assaults, and the interpretability of models as well as other important research tasks and difficulties in fraud detection. We also stress the value of feature science and data pre-processing techniques in enhancing the effectiveness of scam detection systems. Finally, we show a case study on the use of DL and ML techniques in the financial sector for fraud detection. Authors show how these algorithms can successfully identify fraudulent transactions, minimize false positives, and keep high precision and scalability. The overall aim of this article is to provide a comprehensive evaluation of the most cutting-edge ML and DL techniques for fraud identification and to shed light on potential future paths for this field of study.
Robot autonomous grasping and assembly skill learning based on deep reinforcement learning
Authors: Chengjun Chen, Hao Zhang, Yong Pan, Dongnian Li

This paper proposes a deep reinforcement learning-based framework for robot autonomous grasping and assembly skill learning. Meanwhile, a deep Q-learning-based robot grasping skill learning algorithm and a PPO-based robot assembly skill learning algorithm are presented, where a priori knowledge information is introduced to optimize the grasping action and reduce the training time and interaction data needed by the assembly strategy learning algorithm. Besides, a grasping constraint reward function and an assembly constraint reward function are designed to evaluate the robot grasping and assembly quality effectively. Finally, the effectiveness of the proposed framework and algorithms was verified in both simulated and real environments, and the average success rate of grasping in both environments was up to 90%. Under a peg-in-hole assembly tolerance of 3 mm, the assembly success rate was 86.7% and 73.3% in the simulated environment and the physical environment, respectively.
Enabling business sustainability for stock market data using machine learning and deep learning approaches
Authors: S. Divyashree, Christy Jackson Joshua, Abdul Quadir Md, Senthilkumar Mohan, A. Sheik Abdullah, Ummul Hanan Mohamad, Nisreen Innab, Ali Ahmadian

This paper introduces AlphaVision, an innovative decision support model designed for stock price prediction by seamlessly integrating real-time news updates and Return on Investment (ROI) values, utilizing various machine learning and deep learning approaches. The research investigates the application of these techniques to enhance the effectiveness of stock trading and investment decisions by accurately anticipating stock prices and providing valuable insights to investors and businesses. The study begins by analyzing the complexities and challenges of stock market analysis, considering factors like political, macroeconomic, and legal issues that contribute to market volatility. To address these challenges, we proposed the methodology called AlphaVision, which incorporates various machine learning algorithms, including Decision Trees, Random Forest, Naïve Bayes, Boosting, K-Nearest Neighbors, and Support Vector Machine, alongside deep learning models such as Multi-layer Perceptron (MLP), Artificial Neural Networks, and Recurrent Neural Networks. The effectiveness of each model is evaluated based on their accuracy in predicting stock prices. Experimental results revealed that the MLP model achieved the highest accuracy of approximately 92%, outperforming other deep learning models. The Random Forest algorithm also demonstrated promising results with an accuracy of around 84.6%. These findings indicate the potential of machine learning and deep learning techniques in improving stock market analysis and prediction. The AlphaVision methodology presented in this research empowers investors and businesses with valuable tools to make informed investment decisions and navigate the complexities of the stock market. By accurately forecasting stock prices based on news updates and ROI values, the model contributes to better financial management and business sustainability. The integration of machine learning and deep learning approaches offers a promising solution for enhancing stock market analysis and prediction. Future research will focus on extracting more relevant financial features to further improve the model’s accuracy. By advancing decision support models for stock price prediction, researchers and practitioners can foster better investment strategies and foster economic growth. The proposed model holds potential to revolutionize stock trading and investment practices, enabling more informed and profitable decision-making in the financial sector.