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INDONESIA
Indonesian Journal of Electrical Engineering and Computer Science
Published by Institute of Advanced Engineering and Science
ISSN : 25024752     EISSN : 25024760     DOI : -
Core Subject :
Arjuna Subject : -
Articles 9,138 Documents
 834   835   836   837   838 
RecommendRift: a leap forward in user experience with transfer learning on netflix recommendations Anuradha, Surabhi; Jyothi, Pothabathula Naga; Sivakumar, Surabhi; Sheshikala, Martha
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 2: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i2.pp1218-1225

Abstract

In today’s fast-paced lifestyle, streaming movies and series on platforms like  Netflix is a valued recreational activity. However, users often spend considerable time searching for the right content and receive irrelevant recommendations, particularly when facing the “cold start problem” for new users. This challenge arises from existing recommender systems relying on factors like casting, title, and genre, using term frequency-inverse document frequency (TF-IDF) for vectorization, which prioritizes word frequency over semantic meaning. To address this, an innovative recommender system considering not only casting, title, and genre but also the short description of movies or shows is proposed in this study. Leveraging Word2Vec embedding for semantic relationships, this system offers recommendations aligning better with user preferences. Evaluation metrics including precision, mean average precision (MAP), discounted cumulative gain (DCG), and ideal cumulative gain (IDCG) demonstrate the system’s effectiveness, achieving a normalized DCG (NDCG)@10 of 0.956. A/B testing shows an improved click-through rate (CTR) of recommendations, showcasing enhanced streaming experience.
Deep learning based hybrid precoder for optimal power allocation to improve the performance of massive MIMO Bhairanatti, Shilpa; P., Rubini
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 1: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i1.pp570-582

Abstract

Hybrid precoding is a significant procedure for decreasing the hardware complexity and power usage in massive multiple-input multiple-output (MIMO) systems. However, the effectiveness of hybrid precoding is highly dependent on precise channel state info and designing of the beamforming matrix. In recent years, deep learning-based approaches have emerged as a promising solution to address these challenges. This research focuses on improving the performance of massive MIMO systems. However, several methods have been introduced to develop the hybrid precoding model, but these models suffer from several issues such as complexity, interference and quantization error. Currently, deep learning-based methods have gained huge attention in this domain where these methods learn from the data and try to overcome the challenges. Here, a deep learning-based model is presented where our main aim is to develop a hybrid precoder along with the deep learning-based optimal power allocation model. Therefore, the proposed model overcomes the issue of hybrid precoding and power distribution resulting in improving the overall performance of massive MIMO systems on the parameters such as spectral efficiency (SE) and the sum rate.
Impact of high-k insulators on electrical properties of junctionless double gate strained transistor Kaharudin, Khairil Ezwan; Salehuddin, Fauziyah; Mohd Zain, Anis Suhaila; Jalaludin, Nabilah Ahmad; Arith, Faiz; Mat Junos, Siti Aisah; Ahmad, Ibrahim
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1437-1447

Abstract

High-k dielectric insulators are required to reduce leakage and increase transistor performance. They are able to impact the mobility of carriers in transistors positively, leading to better device performance in advanced transistor architecture. Nevertheless, an in-depth analysis of how high-k dielectric insulators influence transistor characteristics must be carried out to determine their suitability. The objective of this study is to investigate the impact of high-k insulators towards electrical properties of junctionless double gate strained transistor. The simulation works is done using process/device simulator Silvaco Athena/Atlas. Based on the retrieved results, the magnitude of ION, on-off ratio, gm, and Cint for TiO2-based device are approximately 63%, 99%, 62%, and 89% respectively higher than the lowest permittivity material-based device. The TiO2-based device also exhibits the lowest magnitude in IOFF and SS compared to others. However, a significant degradation in fT magnitude have been observed for TiO2-based device significantly due to its large capacitances
Optimizing feature extraction for tampering image detection using deep learning approaches Muniappan, Ramaraj; Sabareeswaran, Dhendapani; Jothish, Chembath; Raja, Joe Arun; Selvaraj, Srividhya; Nainan, Thangarasu; Ilango, Bhaarathi; Sumbramanian, Dhinakaran
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1853-1864

Abstract

Tamper image detection approach using deep learning involves, creating a model that can accurately identify and localize instances of image tampering, by employing advanced feature extraction methods, object detection algorithms, and optimization techniques that could be manipulated on need basis. Enhance the integrity of visual content by automating the detection of unauthorized alterations, to ensure the reliability of digital images across various applications and domains. The problem addressing the optimization feature extraction techniques involves the detection of subtle manipulations, handling diverse tampering techniques, and achieving robust performance across different types of images and scenarios. The proliferation of sophisticated image editing tools makes it challenging to detect tampered regions within images, necessitating proposed techniques for automated tamper image detection. The research work will focus on four different feature extraction algorithms such as non-negative factorization (NNF), singular value decomposition (SVD), explicit semantic analysis (ESA), principal component analysis (PCA), which are outsourced. Detecting tampered images through deep learning necessitates the meaningful selection and adjustment of several parameters to enhance the model's effectiveness. Integrating the feature extraction algorithm with the suggested methods effectively identifies critical features within the dataset, thereby improving the detection capabilities and achieving higher accuracy.
Development of explainable machine intelligence models for heart sound abnormality detection Ramakrishna, Jeevakala Siva; Venkateswarlu, Sonagiri China; Kumar, Kommu Naveen; Shreya, Parikipandla
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 2: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i2.pp846-853

Abstract

Developing explainable machine intelligence (XAI) models for heart sound abnormality detection is a crucial area of research aimed at improving the interpretability and transparency of machine learning algorithms in medical diagnostics. In this study, we propose a framework for building XAI models that can effectively detect abnormalities in heart sounds while providing interpretable explanations for their predictions. We leverage techniques such as SHapley additive exPlanations (SHAP) and local interpretable model-agnostic explanations (LIME) to generate explanations for model predictions, enabling clinicians to understand the rationale behind the algorithm’s decisions. Our approach involves preprocessing heart sound data, training machine learning models, and integrating XAI techniques to enhance the interpretability of the models. We evaluate the performance of our XAI models using standard metrics and demonstrate their effectiveness in accurately detecting heart sound abnormalities while providing insightful explanations for their predictions. This research contributes to the advancement of XAI in medical applications, particularly in the domain of cardiac diagnostics, where interpretability is crucial for clinical decision-making.
High-resolution aerial monitoring using DL for identifying abnormal activity based on visual patterns in drone videos Tripathi, Mukesh Kumar; Moorthy, Chellapilla V. K. N. S. N.; Kadam, Sandeep; Shewale, Chaitali; Shelke, Priya; Futane, Pravin R.
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1827-1835

Abstract

Unmanned aerial vehicles (UAVs) and sophisticated deep learning (DL) models have made the application of artificial intelligence (AI) more popular. This has resulted in an increase in the number of attempts to improve high-resolution aerial monitoring using DL for identifying abnormal activity based on visual patterns in drone videos. The study introduces a one-class support vector machine (OC-SVM) oddity locator for low-altitude, limited-scope UAVs used for ethereal video surveillance. The primary goal is to improve UAV-based observation capabilities by identifying areas or things of interest without prior knowledge, hence improving tasks like queue control, vehicle following, and hazardous product identification. The framework makes use of OC-SVM because of its quick and lightweight setup, making it suitable for continuous operation on low-computational UAVs. It empowers the identification of several peculiarities necessary for low-elevation reconnaissance by using textural characteristics to recognise both large-scale and tiny structures. Examine the UAV mosaicking and change location (UMCD) dataset to demonstrate the effectiveness of the framework, which achieves excellent accuracy and outperforms traditional methods by about one fifth in a variety of metrics. The suggested model compares with current methods, demonstrating superior accuracy and performance in recognition of peculiarities. Evaluation metrics include F1-score, review, exactness, and accuracy. The model demonstrates that it always encounters an oddity with a review compromise of up to seven on ten, achieving complete accuracy.
Enhanced query performance for stored streaming data through structured streaming within spark SQL Jose, Benymol; N., Rajesh; Joseph, Lumy
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1744-1750

Abstract

Traditional database systems like relational databases can store data which are structured with predefined schema, but in the case of bigdata, the data comes in different formats or are collected from diverse sources. The distributed databases like not only spark querying language (NoSQL) repositories are often used in relation to bigdata analytics, but a continual updating is required in business because of the streaming data that comes from stock trading, online activities of website visitors, and from the mobile applications in real time. It will not have to delay, for some report to show up, to assess and analyse the current situation, to move forward with the next business choice. Apache Spark’s structured streaming offer capabilities for handling streaming data in a batch processing mode with faster responses compared to MongoDB which is a document-based NoSQL database. This study completes similar queries to evaluate Spark SQL and NoSQL database performance, focusing on the upsides of Spark SQL over NoSQL databases in streaming data exploration. The queries are completed with streaming data stored in a batch mode.
Inset-fed microstrip patch antenna optimization for 2.4 GHz using surrogate model assisted differential evolution machine learning algorithm Alaba, Magnoudewa; Onyango Konditi, Dominic Bernard; Oduol, Vitalice Kalecha
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 2: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i2.pp901-912

Abstract

In this work, we have used the surrogate model assisted differential evolution (SADEA) to model a one and two-element inset-fed patch antenna array to optimize its parameters for efficiency and usability. The microstrip patch antennas operates in a frequency band of 2.4 GHz. The optimization process focused on fine-tuning the patch length, patch width, and notch width to enhance key performance metrics directivity, return loss, and bandwidth. The design is made in CST software with an FR-4 substrate and simulated in the ADE1.0 software a MATLAB toolbox. Significant enhancements were achieved including a directivity gain of 3.04 dB, and 5.58 dB a return loss of -19 dB, -16 dB, and an expanded impedance bandwidth from 0.0798 GHz, 0.0588 GHz to 0.0951 GHz, 0.0824 GHz respectively. The antenna was constructed and then measured. The findings showed that the measurements and the fabrication process closely matched, especially in terms of return loss.
The research on the signal source number estimation algorithm Peizhi, Wang; Mohamed, Raihani; Mustapha, Norwati; Manshor, Noridayu
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 1: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i1.pp188-196

Abstract

In array signal processing, Estimating the quantity of signal sources represents a crucial area of investigation. In this paper, a comprehensive introduction and analysis of the estimation methods for determining the number of signal sources are presented, including the background and significance, and the significance of precise estimation of the quantity of signal sources. The influence of factors such as signal-to-noise ratio (SNR), noise background, and number of snapshots on the estimation algorithm is discussed in detail. At the same time, common array models are introduced. Then, different signal source number estimation algorithms are analyzed in detail, and their respective advantages and applicable conditions are pointed out. Finally, the performance of each algorithm in different situations is evaluated by comparing the performance of the algorithms under different SNRs, snapshot numbers, and array elements. The experimental results show that with the increase of the SNR and the number of array elements, the correct estimation probability of the algorithm also increases correspondingly, which provides a reliable experimental basis and performance evaluation for the estimation.
Aqua-stream: an IoT based smart water management system for sustainable living Siraparapu, Sri Ramya; Azad, S. M. A. K.
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1460-1469

Abstract

Aqua-stream, an innovative internet of things (IoT) enabled water management system, utilizes the power of long short-term memory (LSTM) networks, a sophisticated time-series forecasting machine learning technique with Kafka. Aqua-stream seamlessly integrates LSTM within the Kafka streaming architecture for efficient real-time data processing, ensuring quick responses to emerging water management needs. LSTM is employed for real-time anomaly detection, dynamically analyzing streaming data to prevent leaks through automated shut-off valves. The system’s comprehensive dashboard utilizes LSTM insights for live water quality analysis; adaptive scheduling based on individual preferences and personalized recommendations, enhancing cost-effective water management. This streamlined approach extends to the smart gardening system, where LSTM guides automation for optimal plant care incorporating sensors to monitor soil moisture, temperature, and sunlight levels. This system automatically adjusts watering and lighting to ensure optimal conditions for plant growth. Users can control and monitor their garden remotely via a smartphone, facilitating plant care while saving water and energy. Aqua-stream redefines home water management, offering a holistic solution that combines intelligent water conservation with smart gardening for a sustainable and connected living experience. Aqua-stream represents a seamless integration of LSTM-based machine learning and IoT technologies, offering an intelligent, yet simplified, solution for sustainable and connected living.

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