AI-Driven Virtual Assistant Transforming Image into Voice

In this work, the approach used is to sequence powerful models that have achieved excellent performance on language translation encoding-decoding tasks. A language transformer model is used in this work based on the sequence to sequence approach, which uses a Long Short-Term Memory (LSTM) to map the input sequence to a vector of fixed dimensionality. Then another deep LSTM decodes the target sequence from the vector. Evaluated the model efficiency through BLEU score and LSTM's BLEU score was penalized on out-of-vocabulary words. Additionally, the LSTM did not have difficulty with long-short of sentences. This work performed the deep LSTM setup English-Japanese translation accuracy at an order of magnitude faster speed, both on GPU and CPU. The variety of the data is introduced into it to evaluate the robustness using the BLEU score. Finally, a better result is achieved on merging the two different types of datasets and got the highest BLEU score of 40.1 at the end.

Most Graph Neural Networks (GNNs) follow the message-passing scheme. Residual connection is an effective strategy to tackle GNNs' over-smoothing issue and performance reduction issue on non-homophilic networks. Unfortunately, the coarse-grained residual connection still suffers from class-imbalanced over-smoothing issue, due to the fixed and linear combination of topology and attribute in node representation learning. To make the combination flexible to capture complicated relationship, this paper reveals that the residual connection needs to be node-dependent, layer-dependent, and related to both topology and attribute. To alleviate the difficulty in specifying complicated relationship, this paper presents a novel perspective on GNNs, i.e., the representations of one node in different layers can be seen as a sequence of states. From this perspective, existing residual connections are not flexible enough for sequence modeling. Therefore, a novel node-dependent residual connection, i.e., Long Short-Term Graph Memory Network (LSTGM) is proposed to employ Long Short-Term Memory (LSTM), to model the sequence of node representation. To make the graph topology fully employed, LSTGM innovatively enhances the updated memory and three gates with graph topology. A speedup version is also proposed for effective training. Experimental evaluations on real-world datasets demonstrate their effectiveness in preventing over-smoothing issue and handling networks with heterophily.

The research study is to predict human activity recognition and analyze their strength in machine learning (ML) processes to achieve precision and also to determine performance of human activities. The framework to classify the Novel Long Short Term Memory (LSTM) and Decision Tree (DT) to perform all measures. This research study used the Novel Long Short Term Memory and Decision Tree to perform the required actions to accurately identify body movements in adjacent film sequence. Recent study findings and a threshold of 0.05%, as well as 95% confidence interval and standard parameter estimates, were castoff in a study 47 samples divided into two groups calculated using the G-power of 80%. Since the novel Long Short Term Memory (LSTM) algorithm and local invariant methods have achieved an accuracy of 93.83% in predicting the activity analysis, this research needs to obtain improved accuracy to perform activity prediction by using the Decision Tree algorithm. When applied to human activity analysis, the Novel Long Short Term Memory technique was shown to have an accuracy of 90.12 percent, with a 95% confidence interval including the value 0.045 (p 0.05). Researchers found that when comparing two algorithms for analyzing human behaviour, Long Short Term Memory (LSTM) performed much better than Decision Tree (DT).

The retina acts as the main entrance point that allows visual data to enter the brain. Diabetic retinopathy is a circumstance affecting the blood vessels and nerve tissues in the retina. In recent times DR is becoming a major problem of vision loss, especially among diabetic people. It is important to detect it early and well-timed intervention can help prevent further vision loss. Retina Scan Disease Detection focusses to ensure a promising tool for DR prediction and aims to create a model that detects the existence of Diabetic Retinopathy and not only classifying them into mild, moderate, severe and proliferative depending on the severity level but also providing the output in a descriptive manner describing the conditions of fundus image. This paper employs a mixture of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). The hybrid CNN-RNN model contains benefits of transfer learning as well as Long Short-Term Memory (LSTM). The Diabetic Retinopathy is detected by using the CNN and LSTM is used for generating a descriptive sentence based on the condition of the image. The objective is to extend the state of the art of CNN-LSTM for three-dimensional dataset formation for the accurate prediction of diabetic retinopathy from the retinal images. The model is evaluated using BLEU scores The BLEU-1 score is nearly 0.86. Comparative analysis against existing approaches demonstrates superior performance in accuracy as well as generating sentences based on the severity of the disease. The model is able to generate a descriptive sentence about the condition of the retinal fundus image based on any severity level. The model has been evaluated on BLEU scores and has a BLEU score accuracy of about 86.17%.The outcomes prove that the automatic system not only reaches expert-level accuracy but also improves diagnostic efficiency, presenting a feasible solution for large-scale retinal screening in clinics and remote areas.

Sequence Classification is one of the on-demand research projects in the field of Natural Language Processing (NLP). Classifying a set of images or text into an appropriate category or class is a complex task that a lot of Machine Learning (ML) models fail to accomplish accurately and end up under-fitting the given dataset. Some of the ML algorithms used in text classification are KNN, Naïve Bayes, Support Vector Machines, Convolutional Neural Networks (CNNs), Recursive CNNs, Recurrent Neural Networks (RNNs), Long Short Term Memory (LSTM), etc. For this experimental study, LSTM and a few other algorithms were chosen for a more comparative study. The dataset used is the SMS Spam Collection Dataset from Kaggle and 150 more entries were additionally added from different sources. Two possible class labels for the data points are spam and ham. Each entry consists of the class label, a few sentences of text followed by a few useless features that are eliminated. After converting the text to the required format, the models are run and then evaluated using various metrics. In experimental studies, the LSTM gives much better classification accuracy than the other machine learning models. F1-Scores in the high nineties were achieved using LSTM for classifying the text. The other models showed very low F1-Scores and Cosine Similarities indicating that they had underperformed on the dataset. Another interesting observation is that the LSTM had reduced the number of false positives and false negatives than any other model.

Online leakage current classification using convolutional neural network long short-term memory for high voltage insulators on web-based service

Long short-term memory - Fully connected (LSTM-FC) neural network for PM2.5 concentration prediction

Accurate real-time bus arrival information is essential for an efficient public transport network, as it significantly impacts passenger experience, system reliability, reduced waiting times, dwell times, and operational efficiency. In Sri Lanka's public transport system, current bus arrival time computations primarily rely on static data, neglecting real-time information and critical factors influencing travel times. This highlights the need to identify the unique variables affecting bus arrival times within the Sri Lankan context and to develop robust prediction models that account for these influences. While traditional methods such as Historical Average Models, Regression, Time Series Analysis, and Kalman Filtering have been used in previous research for short-term travel time predictions, Machine Learning (ML) approaches have proven to deliver superior accuracy. ML models are regarded as the most effective for heterogeneous, lane-less traffic conditions with varying traffic volumes, such as those found in Sri Lanka. ML techniques excel in processing large, high-quality datasets and provide accurate predictions by accounting for all relevant variables influencing travel times. Although research has been conducted on developing various basic ML models for travel time prediction, there is a noticeable gap in studies comparing these models to determine the most suitable one for the Sri Lankan context. A Long- Short Term Memory (LSTM) neural network is a deep learning model that is capable of handling long-term dependencies. In the context of bus travel time prediction, LSTMs can leverage historical traffic and travel data to capture temporal patterns and fluctuations that influence travel times. By evaluating LSTMs against basic machine learning models, this study seeks to explore the advantages of applying deep learning techniques to transportation forecasting, ultimately contributing to more accurate and efficient predictive systems in transit planning. The ML models selected in this study include two basic traditional models K- Nearest Neighbours (KNN) and Support Vector Regression (SVR) and four advanced models that utilize ensemble techniques and advanced optimization as Random Forest Regression (RFR), Ada Boost, XG Boost and Gradient Boosting Machine (GBM). The performance of these models was compared with the LSTM model to identify the gap in their accuracies.

Air pollution prediction using LSTM deep learning and metaheuristics algorithms

Abstract: This research is driven by the challenges faced by Universitas Lancang Kuning (UNILAK) in attracting applicants amidst intense competition, especially after the government's policy opened independent pathways to State Universities (PTN) from 2022-2023, which impacted private university applicant numbers. To address this and support strategic planning, this study aims to predict the trend of prospective students applying to all study programs at UNILAK for the period 2025-2027. Two time series models were employed: ARIMA (AutoRegressive Integrated Moving Average) and LSTM (Long Short-Term Memory). Applicant data from 2019 to 2024 was used to build the model. The Augmented Dickey-Fuller (ADF) test confirmed the data's stationarity with a p-value of 0.0. ACF and PACF analyses determined the ARIMA parameters as p=1, d=1, q=1. The LSTM model was trained to capture more complex data patterns. ARIMA predictions for 2025, 2026, and 2027 are 3298.66, 3362.33, and 3371.30, respectively. LSTM predictions for the same years are 3335.64, 3476.52, and 3518.42. Evaluation using Root Mean Squared Error (RMSE) showed ARIMA (RMSE=588.72) to be more accurate than LSTM (RMSE=653.96). Nevertheless, LSTM provided a more optimistic prediction. This study concludes that ARIMA is better suited for short-term planning, while LSTM can be used for more ambitious long-term strategies. Keywords: arima; LSTM; applicants; prediction; university Abstrak: Penelitian ini didorong oleh tantangan Universitas Lancang Kuning (UNILAK) dalam menarik pendaftar di tengah persaingan ketat, khususnya setelah kebijakan pemerintah membuka jalur mandiri ke Perguruan Tinggi Negeri (PTN) sejak 2022-2023, yang menyebabkan penurunan jumlah pendaftar di universitas swasta. Untuk mendukung perencanaan strategis, studi ini bertujuan memprediksi tren jumlah calon mahasiswa yang mendaftar ke seluruh program studi di UNILAK untuk periode 2025-2027.Dua model deret waktu digunakan: ARIMA (AutoRegressive Integrated Moving Average) dan LSTM (Long Short-Term Memory). Data jumlah pendaftar dari 2019 hingga 2024 digunakan untuk membangun model. Uji Augmented Dickey-Fuller (ADF) menunjukkan data stasioner dengan p-value 0,0. Analisis ACF dan PACF menentukan parameter ARIMA sebagai p=1, d=1, q=1. Model LSTM dilatih untuk menangkap pola data yang lebih kompleks.Prediksi ARIMA untuk 2025, 2026, dan 2027 adalah 3298.66, 3362.33, dan 3371.30. Prediksi LSTM untuk tahun yang sama adalah 3335.64, 3476.52, dan 3518.42. Evaluasi menggunakan Root Mean Squared Error (RMSE) menunjukkan ARIMA (RMSE=588.72) lebih akurat daripada LSTM (RMSE=653.96). Meskipun demikian, LSTM memberikan prediksi yang lebih optimis. Studi ini menyimpulkan ARIMA lebih cocok untuk perencanaan jangka pendek, sementara LSTM dapat digunakan untuk strategi jangka panjang yang ambisius. Kata kunci: arima; LSTM; pendaftar; prediksi; universitas

Hybrid blockchain-based spectrum sharing algorithm for dynamic channel selection in cognitive radio

The objective of this research is to develop a Deep Learning model to forecast the stock price, by using the variant of Long Short-Term Memory. This model predicts the close price of the stock for the future selected date, choosing as inputs the following data: open, high, low, adj close and close prices. This model shows a comparative analysis between three different LSTM networks: Long Short-Term Memory (LSTM), Stacked Long Short-Term Memory (Stacked LSTM), and Stacked Bi-directional Long Short-Term Memory (Stacked Bidirectional LSTM) concluding which one is the best and implementing the model using that variant. We have used the historical stock prices data from yahoo’s financial website over 5 years, by choosing multiple datasets: Apple, Amazon, Google, Meta, Microsoft and Tesla (daily values). In order to get effective in the forecasting model, we have tested the network with different iterations and epochs. The model represents Multiple Graphs for data visualization in different comparisons. We have estimated the effectiveness of our proposed model by using the following performance indicators: the Mean Square Error (MSE), the Root Mean Square Error (RMSE), and the R-Squared of the model. The experimental results clearly show that our Stacked Bi-LSTM model has the highest accuracy values when comparing with the LSTM and Stacked LSTM Models. Hence, we can conclude that our Stacked Bi-LSTM Model is suitable for accurate prediction of the stock market time series. Key Words: stock price prediction, Machine Learning, stacked LSTM, Bi-directional LSTM, Deep Learning, Data pre-processing techniques, Data normalization, Data Visualization, Training and Testing Set, Financial Time Series, Future prediction

The accurate prediction of environmental parameters inside the polyhouse is prerequisite for reducing the potential yield and financial losses which might occur due to variations in weather conditions. A precise predictive model to forecast the internal climate of the polyhouse several hours beforehand on the basis of present and past weather conditions is needed for an hour. In this study, a new hybrid approach has been proposed which coupled artificial intelligence based deep learning model (Long-Short Term Memory (LSTM)) with Maximum Overlap Discrete Wavelet Transform (MODWT) to forecast the polyhouse environment. The wavelet transform method improved 10 to 20% accuracy of deep leaning model (LSTM) by filtering input data and extracting significant information. A comparative performance analysis of three machine learning models (MLP, SVM, GPR) with deep learning model (Long Short Term Memory (LSTM)) have been evaluated for predicting the temperature and relative humidity of polyhouse. This study also explores the ability of newly proposed hybrid model MODWT-LSTM and compares accuracy with LSTM. The findings of this study shows that MODWT-LSTM performs better than other models for predicting temperature (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> =0.95, RMSE=0.64°C) and relative humidity (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> =0.97, RMSE=2.43%) of polyhouse. The outcome of proposed work also states that deep learning can be applied more precisely for monitoring and controlling the environment of polyhouse.

SummaryFor the past few years, Bitcoin plays a vital role in both the economical and financial industries. In order to gain a huge return on investment, the investors are eager to forecast the future value of Bitcoin. However, Bitcoin price variation is quite nonlinear and chaotic in nature, so it creates more difficulty in forecasting future value. Researchers found that the multiplicative long short term memory (LSTM) model will be more efficient for predicting those complex variations. So, target mission is about to develop an optimized multiplicative LSTM with an Attention mechanism using Technical Indicators derived from historical data. A modified cuckoo search optimization model is proposed to tune the hyperparameter of the Deep Learning model. This novel optimization algorithm eliminates the local optimum and slower convergence problem of the cuckoo search optimization algorithm. Deibold Mariano test is performed to statistically evaluate the proposed model and it is inferred that the recommended methodology is statistically fit. Regression metrics such as root mean square error, mean square error and mean absolute error has been used for comparative evaluation with related benchmark techniques such as genetic algorithm optimized LSTM (GA–LSTM), particle swarm optimized LSTM (PSO–LSTM) and cuckoo search optimized LSTM (CSO–LSTM). The empirical result shows that the recommended methodology outperforms the taken benchmark models and provides better accuracy.

Research on sentiment analysis in recent years has increased. However, in sentiment analysis research there are still few ideas about the handling of negation, one of which is in the Indonesian sentence. This results in sentences that contain elements of the word negation have not found the exact polarity.The purpose of this research is to analyze the effect of the negation word in Indonesian. Based on positive, neutral and negative classes, using attention-based Long Short Term Memory and word2vec feature extraction method with continuous bag-of-word (CBOW) architecture. The dataset used is data from Twitter. Model performance is seen in the accuracy value.The use of word2vec with CBOW architecture and the addition of layer attention to the Long Short Term Memory (LSTM) and Bidirectional Long Short Term Memory (BiLSTM) methods obtained an accuracy of 78.16% and for BiLSTM resulted in an accuracy of 79.68%. whereas in the FSW algorithm is 73.50% and FWL 73.79%. It can be concluded that attention based BiLSTM has the highest accuracy, but the addition of layer attention in the Long Short Term Memory method is not too significant for negation handling. because the addition of the attention layer cannot determine the words that you want to pay attention to.