Estimation of Stock Price Using Machine and Deep Learning Techniques DOI

K Akshitha,

Ashwini Kodipalli,

Trupthi Rao

et al.

Published: June 22, 2023

Analysis of Stock price has always been a disparaging topic research and it is one the important aspect in area machine learning. Prediction Price helps estimating future value company stock some other financial exchange. The main aim prediction to procure significant profits trend. Predicting theway how market may perform tedious labor. Some factors which can be involved are psychological physical factors, rational irrational practices, many more. Such make share prices differ alter making hard predict with high amount accuracy. Therefore, this paper newer skeleton proposed using two popular fields: Machine learning (ML) Deep (DL) models. Various types algorithms taken forecasting trend previous years like Linear Regression, Ridge, Lasso Polynomial Regression from LSTM its variants proposed. purpose see or algorithm cam values accurately.

Language: Английский

Multiple perception contrastive learning for automated ovarian tumor classification in CT images DOI

Lingwei Li,

Tongtong Liu, Peng Wang

et al.

Abdominal Radiology, Journal Year: 2025, Volume and Issue: unknown

Published: March 12, 2025

Language: Английский

Citations

0
Artificial Intelligence for Ovarian Cancer Detection with Medical Images: A Review of the Last Decade (2013–2023) DOI
Amir Reza Naderi Yaghouti, Ahmad Shalbaf, Roohallah Alizadehsani

et al.

Archives of Computational Methods in Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: March 17, 2025

Language: Английский

Citations

0
Deformable Dual Graph Aggregation Transformer Convolutional Networks with Spider Wasp Optimizer for Ovarian Tumor Classification Using Magnetic Resonance Imaging DOI

V. Shanmugaveni,

M. Jotheeswari,

R Abarnaswara

et al.

Deleted Journal, Journal Year: 2025, Volume and Issue: unknown

Published: April 4, 2025

Language: Английский

Citations

0
X-ray image classification with dual-model information fusion and improved PSO algorithm DOI
Zhi Weng,

Hailong Zuo,

Zhiqiang Zheng

et al.

Signal Image and Video Processing, Journal Year: 2025, Volume and Issue: 19(7)

Published: May 9, 2025

Language: Английский

Citations

0
A Comprehensive Study on Deep Learning Models for the Detection of Ovarian Cancer and Glomerular Kidney Disease using Histopathological Images DOI

S J K Jagadeesh Kumar,

G. Prabu Kanna,

D. Prem Raja

et al.

Archives of Computational Methods in Engineering, Journal Year: 2024, Volume and Issue: unknown

Published: June 1, 2024

Language: Английский

Citations

3
Deep fine-KNN classification of ovarian cancer subtypes using efficientNet-B0 extracted features: a comprehensive analysis DOI Creative Commons
Santi Kumari Behera, Ashis Das, Prabira Kumar Sethy

et al.

Journal of Cancer Research and Clinical Oncology, Journal Year: 2024, Volume and Issue: 150(7)

Published: July 25, 2024

This study presents a robust approach for the classification of ovarian cancer subtypes through integration deep learning and k-nearest neighbor (KNN) methods. The proposed model leverages powerful feature extraction capabilities EfficientNet-B0, utilizing its features subsequent fine-grained using fine-KNN approach. UBC-OCEAN dataset, encompassing histopathological images five distinct subtypes, namely, high-grade serous carcinoma (HGSC), clear-cell (CC), endometrioid (EC), low-grade (LGSC), mucinous (MC), served as foundation our investigation. With dataset comprising 725 images, divided into 80% training 20% testing, exhibits exceptional performance. Both validation testing phases achieved 100% accuracy, underscoring efficacy methodology. In addition, area under curve (AUC), key metric evaluating model's discriminative ability, demonstrated high performance across various with AUC values 0.94, 0.78, 0.69, 0.92, 0.94 MC. Furthermore, positive likelihood ratios (LR

Language: Английский

Citations

2
Neural Networks and Emotions: A Deep Learning Perspective DOI

M. Madhura,

S Meghana,

Varshitha VS

et al.

2022 IEEE 7th International conference for Convergence in Technology (I2CT), Journal Year: 2024, Volume and Issue: unknown, P. 1 - 7

Published: April 5, 2024

Emotion recognition represents a critical facet of human-centric artificial intelligence systems. This paper delves into the forefront emotion detection by leveraging cutting-edge deep learning models across three distinct modalities: textual, visual, and auditory. Our text-based model harnesses potency Bidirectional Long Short-Term Memory (BiLSTM) networks, adept at capturing intricate semantic relationships contextual nuances within textual data. Simultaneously, we employ Convolutional Neural Networks (CNNs) in domain image-based detection, effectively extracting discriminative spatial features to discern emotional states visual content. For speech-based recognition, (LSTM) capitalizing on their ability capture temporal dependencies acoustic signals. These modalities converge offer comprehensive insights multimodal where fusion auditory information enhances classification accuracy. research not only underscores importance analysis but also holds great potential for applications human-computer interaction, sentiment analysis, mental health diagnostics, multimedia content understanding. By elucidating strengths synergies these modalities, this contributes significantly burgeoning field emotion-aware AI systems, promising more nuanced understanding human emotions an increasingly digital world.

Language: Английский

Citations

1
Intelligent system based on multiple networks for accurate ovarian tumor semantic segmentation DOI Creative Commons
Mohamed El-Khatib, Dan Popescu,

Oana Mihaela Teodor

et al.

Heliyon, Journal Year: 2024, Volume and Issue: 10(17), P. e37386 - e37386

Published: Sept. 1, 2024

Ovarian tumors, especially malignant ones, represent a global concern, with increased prevalence in recent years. More accurate medical support systems are urgently needed to staff obtaining an efficient ovarian tumors diagnosis since detection early stages could lead immediately applying appropriate treatment, and implicitly improving the survival rate. The current paper aims demonstrate that more be designed by combining different convolutional neural networks using custom combination approaches selecting involved ensemble model achieve best performance metrics. It is essential understand if all experimented or only best-performing ones always most effective results not. structured three main phases. first step propose individual experiments. Five DeepLab-V3+ encoders (ResNet-18, ResNet-50, MobileNet-V2, InceptionResNet-V2, Xception) were used. In second step, proposes algorithm combine multiple semantic segmentation networks, while last describes iterative selection approach for combined so obtained. system performing types of covering both benign achieved 91.18 % Intersection over union (IoU), thus overperforming networks. proposed method extended powerful deep learning models

Language: Английский

Citations

1
Emotion Detection from Textual Data Using Supervised Machine Learning Models DOI

Rakshit R Malagi,

R Yogith,

Sai Prashanth T K

et al.

Published: May 26, 2023

Emotion detection and recognition from text is a recent field of research that closely related to Sentiment analysis. Many people express themselves using text, photographs, music, video. Text communication web-based networking platforms, however, could be little overwhelming. Every second, substantial amount unstructured data produced on the Internet as result social media sites. This where sentiment analysis, which recognises polarity in texts, can useful. It assesses author's attitude towards specific object, administration, person, or location concludes if it positive, negative, neutral. In some cases, analysis inadequate, necessitating emotion detection, precisely ascertains person's mental/emotional state. The development text-based prediction model primary goal this work. confronting several market hurdles, with accuracy being key one. As result, Decision Trees, Naive Bayes, Support Vector Machine, Logistic Regression, k-Nearest Neighbors Random Forest, supervised machine learning classification algorithms were examined. six main emotions recognized by Ekman are joy, fear, anger, love, surprise, sadness, these foundation through was constructed. strategies for preprocessing containing stemming, stop-words, numerals, punctuation marks removal, tokenization, spelling correction implemented. review paper delves into degrees models well technique text.

Language: Английский

Citations

2
Predicting Alzheimer’s Disease Progression through Machine Learning Algorithms DOI

Mekhala Bharath,

S. Gowtham,

S Vedanth

et al.

Published: Nov. 23, 2023

This study revolves around the crucial task of early Alzheimer's disease (AD) detection using machine learning algorithms. Leveraging a dataset 6400 preprocessed MRI images, research rigorously evaluates spectrum models, encompassing Support Vector Machines (SVM) with diverse kernels, multidimensional Linear Discriminant Analysis (LDA), comprehensive Principal Component (PCA), and Convolutional Neural Networks (CNN) integrated within architecture EfficientNetB0. Significantly, SVM model, utilizing linear kernel, emerges as standout performer, achieving an impressive accuracy 98% in AD remarkable 98.7% classification. These findings distinctly underscore efficacy particularly when harnessed potent tools for precise

Language: Английский

Citations

1