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31,456 Article Results

HepatoScan: Ensemble classification learning models for liver cancer disease detection

10.11591/csit.v6i2.p169-177
Tella Sumallika , Raavi Satya Prasad
Liver cancer is a dangerous disease that poses significant risks to human health. The complexity of early detection of liver cancer increases due to the unpredictable growth of cancer cells. This paper introduces HepatoScan, an ensemble classification to detect and diagnose liver cancer tumors from liver cancer datasets. The proposed HepatoScan is the integrated approach that classifies the three types of liver cancers: hepatocellular carcinoma, cholangiocarcinoma, and angiosarcoma. In the initial stage, liver cancer starts in the liver, while the second stage spreads from the liver to other parts of the body. Deep learning is an emerging domain that develops advanced learning models to detect and diagnose liver cancers in the early stages. We train the pre-trained model InceptionV3 on liver cancer datasets to identify advanced patterns associated with cancer tumors or cells. For accurate segmentation and classification of liver lesions in computed tomography (CT) scans, the ensemble multi-class classification (EMCC) combines U-Net and mask region-based convolutional network (R-CNN). In this context, researchers use the CT scan images from Kaggle to analyze the liver cancer tumors for experimental analysis. Finally, quantitative results show that the proposed approach obtained an improved disease detection rate with mean squared error (MSE)-11.34 and peak signal-to-noise ratio (PSNR)-10.34, which is high compared with existing models such as fuzzy C-means (FCM) and kernel fuzzy C-means (KFCM). The classification results obtained based on detection rate with accuracy-0.97%, specificity-0.99%, recall-0.99%, and F1S-0.97% are very high compared with other existing models.
Volume: 6
Issue: 2
Page: 169-177
Publish at: 2025-07-01

Classifying IoT firmware security threats using image analysis and deep learning

10.11591/ijres.v14.i2.pp546-557
Abdelkabir Rouagubi , Chaymae El Youssofi , Khalid Chougdali
As the internet of things (IoT) grows, its embedded devices face increasing vulnerability to firmware-based attacks. The lack of robust security mechanisms in IoT devices makes them susceptible to malicious firmware updates, potentially compromising entire networks. This study addresses the classification of IoT firmware security threats using deep learning and image-based analysis techniques. A publicly available dataset of 32×32 grayscale images, derived from IoT firmware samples and categorized as benignware, hackware, and malware, was utilized. The grayscale images were converted into three-channel RGB format to ensure compatibility with convolutional neural networks (CNNs). We tested multiple pre-trained CNN architectures, including SqueezeNet, ShuffleNet, MobileNet, Xception, and ResNet50, employing transfer learning to adapt the models for this classification task. Both ResNet50 and ShuffleNet achieved exceptional performance, with 100% accuracy, precision, recall, and F1-score. These results validate the effectiveness of our methodology in leveraging transfer learning for IoT firmware classification while maintaining computational efficiency, making it suitable for deployment in resource-constrained IoT environments. T
Volume: 14
Issue: 2
Page: 546-557
Publish at: 2025-07-01

Test and measurement automation of printed circuit board assembly using digital oscilloscope

10.11591/ijres.v14.i2.pp463-471
Sanjeev Kumar , Deepak Prasad , Manoj Pandey
The testing and measurement (TM) of electrical parameters of printed circuit board assembly (PCBA) plays an integral part in the manufacturing sectors. These industries work on embedded system which widely use digital oscilloscopes (DO) for such purposes, however, operate them manually. An exponential rise in the implementation of industry 4.0 with the increasing demand for industrial products makes manual TM cumbersome. The automation of oscilloscopes (AO) remains a viable alternative to these issues requiring further investigation. An accurate and automated TM block facilitates efficient design, development, and assembly of a fully functional system hence addressed here. The AO has been carried out using generalized software that can be configured based on industry requirements. It subsequently stores the data on the server for better traceability. The automated software is developed using VB.NET and installed on a personal computer. Experiments reveal the proposed approach saves approximately 60%-70% of the time required for each PCBA operation than that of the manual system. This can enhance the productivity of the industry in terms of manpower and Resource utilization with a reduction in operating costs.
Volume: 14
Issue: 2
Page: 463-471
Publish at: 2025-07-01

Design and evaluation of clock-gating-based approximate multiplier for error-tolerant applications

10.11591/ijres.v14.i2.pp398-411
Chowdam Venkata Sudhakar , Suresh Babu Potladurty , Prasad Reddy Karipireddy
The multiplier is an essential component in real-time applications. Even though approximation arithmetic affects output accuracy in multipliers, it offers a realistic avenue to constructing power area and speed-efficient digital circuits. The approximation computing technique is commonly used in error-tolerant applications such as signal, image, and video processing. In this paper, approximate multipliers (AMs) are designed using both conventional and approximate half adders (A-HA) and full adders (A-FA), which are strategically placed to add partial products at the most significant bit (MSB) positions, and OR gates are used to add partial products at the lower significant bit (LSB). In addition, this research article demonstrates unsigned and signed multipliers using the ripple carry adder (RCA), carry save adder (CSA), conditional sum adder (COSA), carry select adder (CSLA), and clock gating technique. The proposed multipliers are implemented in Verilog hardware description language (HDL) and simulated on the Xilinx VIVADO 2021.2 design tool with target platform Artix-7 AC701 FPGA. The simulation results found that unsigned and signed approximate multiplier power consumption was reduced by 13% and 18.18% respectively and enhanced accuracy.
Volume: 14
Issue: 2
Page: 398-411
Publish at: 2025-07-01

Design and development of multiband multi-mode frequency reconfigurable CPW-fed antenna for 5G wireless communication

10.11591/ijres.v14.i2.pp328-338
Annu Tiwari , Muhammed Yasir Yilmaz , Gaurav Kumar Soni , Dinesh Yadav
This research develops, simulates, fabricates and measured a coplanar waveguide (CPW)-fed multiband multi-mode frequency reconfigurable antenna for 5G wireless communication. The antenna is design on Rogers RT5880 substrate with a dielectric constant of 2.2, a thickness of 0.508 mm, and a loss tangent (tanδ) of 0.0009 and the dimension is 30×28×0.508 mm3. The presented antenna has shown good impedance matching with reflection coefficients ranging from -14.82 to -50.36 dB at different frequencies between 6 GHz to 24 GHz. The presented frequency reconfigurable antenna design includes four PIN diodes, resistors, and inductors, enabling 16 different configurations. The simulated outcomes showed varied S parameter values and gains, demonstrating the antenna's flexibility. Measurements were taken using vector network analyzer (VNA) and anechoic chamber to assess reflection coefficient (|S11|) and gain, confirming the antenna's performance. The antenna's ability to reconfigure dynamically without losing signal integrity makes it suitable for 5G wireless applications. It meets and exceeds the requirements for multiband operation, validated by comprehensive simulations and measurements, showing its potential for wide use.
Volume: 14
Issue: 2
Page: 328-338
Publish at: 2025-07-01

Systematic review of a lightweight convolutional neural network architectures on edge devices

10.11591/ijres.v14.i2.pp339-352
Muhammad Abbas Abu Talib , Samsul Setumin , Siti Juliana Abu Bakar , Adi Izhar Che Ani , Denis Eka Cahyani
A lightweight convolutional neural network (CNN) has become one of the major studies in machine learning field to optimize its potential for employing it on the resource-constrained devices. However, a benchmark for fair comparison is still missing and thus, this paper aims to identify the recent studies regarding the lightweight CNN architectures including the types of CNN, its applications, edge devices usage, evaluation types and matrices, and performance comparison. The preferred reporting items for systematic reviews and meta-analysis (PRISMA) framework was used as the main approach to collect and interpret the literature. In the process, 37 papers were identified as meeting the criteria for lightweight CNNs aimed at image classification or regression tasks. Of these, only 20 studies explored the use of these models on edge devices. To conclude, MobileNet appeared as the most used architecture, while the types of CNN focused on image classification for the general-purpose application. Following that, the NVIDIA Jetson Nano was the most utilized edge device in recent research. Additionally, performance evaluation commonly included measures like accuracy and time, along with metrics such as recall, precision, F1-Score, and other similar indicators. Finally, the average accuracy for performance comparison can serve as threshold value for future research in this scope of study.
Volume: 14
Issue: 2
Page: 339-352
Publish at: 2025-07-01

On time audio alert automated intelligent system for visually impaired people

10.11591/ijres.v14.i2.pp428-439
Cyriloose Pretty Diana Cyril , Sumathy Vethanayagam , Sundararajan Geetha , Brahmadesam Viswanathan Krishna
India is the home of the world’s blind population. The country has around 12 million individuals with visual impairment against a global total of 39 million according to a report published by the National Programme for Control of Blindness. Visually impaired people cannot live independently. They have to depend on others for their daily activities. The major problem occurs when blind people walk on the road, they cannot detect any obstacle which puts them at risk. sometimes it causes major injuries and accidents. With the proposed system, visually impaired people can walk on the road independently. A device with internet of things (IoT) technology where smart glass is embedded with an ultrasonic sensor, sunglasses, Raspberry Pi, voice module ISD1820, and Bluetooth. This device alerts the user with audio guidance. When people walk in front of an obstacle, the device detects the obstacle and tells the user at what distance the obstacle is there. They can walk on the road comfortably and fearlessly. It gives audio alerts about obstacles to the user. The key features are it is easy to wear, lightweight, and cost-effective. Wearing smart glasses, they can walk on the road confidently like a normal human being without any guilt.
Volume: 14
Issue: 2
Page: 428-439
Publish at: 2025-07-01

Effects of hyperparameter tuning on random forest regressor in the beef quality prediction model

10.11591/csit.v6i2.p159-168
Ridwan Raafi'udin , Yohanes Aris Purwanto , Imas Sukaesih Sitanggang , Dewi Apri Astuti
Prediction models for beef meat quality are necessary because production and consumption were significant and increasing yearly. This study aims to create a prediction model for beef freshness quality using the random forest regressor (RFR) algorithm and to improve the accuracy of the predictions using hyperparameter tuning. The use of near-infrared spectroscopy (NIRS) in predicting beef quality is an easy, cheap, and fast technique. This study used six meat quality parameters as prediction target variables for the test. The R² metric was used to evaluate the prediction results and compare the performance of the RFR with default parameters versus the RFR with hyperparameter tuning (RandomSearchCV). Using default parameters, the R-squared (R²) values for color (L*), drip loss (%), pH, storage time (hour), total plate colony (TPC in cfu/g), and water moisture (%) were 0.789, 0.839, 0.734, 0.909, 0.845, and 0.544, respectively. After applying hyperparameter tuning, these R² scores increased to 0.885, 0.931, 0.843, 0.957, 0.903, and 0.739, indicating an overall improvement in the model’s performance. The average performance increase for prediction results for all beef quality parameters is 0.0997 or 14% higher than the default parameters.
Volume: 6
Issue: 2
Page: 159-168
Publish at: 2025-07-01

IoT-based smart agriculture system using fuzzy logic: case study in Vietnam

10.11591/ijres.v14.i2.pp440-451
Le Phuong Truong , Le Nam Thoi
This paper presents an internet of things (IoT)-based smart agriculture system using fuzzy logic. This system automatically supervise and regulate pivotal parameters like temperature, humidity, pH, nutrients (NPK), and electrical conductivity (Ec) for vegetables. Data from the cultivation environment is gathered by sensors system and processed by fuzzy logic algorithms to make appropriate control decisions, ensuring optimal crop growth conditions. Additionally, a web application was developed to monitor temperature, humidity, Ec, pH, and NPK content. Moreover, when any of the NPK, Ec, pH, temperature or humidity indices fall outside allowed ranges, the system send warning notifications through the web application. Furthermore, an IP camera was installed to take images of the garden and send them to users via this web app. Experimental results demonstrate the system's reliability with a pH root mean square error (RMSE) of 0.22 and temperature RMSE of 0.93, corresponding to low errors of 0.034% and 0.056% respectively. Concurrently, this system optimizes resource utilization including water and electricity to assist in reducing production costs.
Volume: 14
Issue: 2
Page: 440-451
Publish at: 2025-07-01

Blockchain-based decentralized voting system with SHA-256 algorithm and facial recognition

10.11591/ijres.v14.i2.pp481-489
BJD Kalyani , Jaya Krishna Modadugu , Sarabu Neelima
Blockchain technology has completely changed the way data is stored and transactions are verified. It provides a dependable, transparent, and safe medium for communication and transaction validation. In order to solve the drawbacks of conventional electronic voting systems, the goal of this research project is to design a decentralized voting system based on blockchain technology. The suggested method offers an immutable and safe way to record and validate votes by utilizing the security and transparency capabilities of blockchain technology. The suggested approach provides an immutable and safe way to record and validate votes by utilizing the security and transparency capabilities of blockchain technology. This paper aims to provide a comprehensive process for digital identity authentication, create a voter interface that is compatible with Ethereum wallets, and apply smart contracts on the Ethereum network to speed up voter registration, ballot preparation, voting, and result tabulation. Additionally, this paper proposes to build up a multi-factor authentication system for election managers and validators to offer them safe and approved power over the voting process. By carefully examining the existing methods, this research highlights the flaws and weaknesses of traditional electronic voting systems and stresses the need for more trustworthy and secure voting technology. The proposed blockchain-based voting system offers an innovative solution to problems with voter fraud and election manipulation because of its irreversible blockchain record, which gives a high degree of transparency and integrity.
Volume: 14
Issue: 2
Page: 481-489
Publish at: 2025-07-01

Advanced generalized integrator based phase lock loop under complex grid condition: a comparative analysis

10.11591/ijeecs.v39.i1.pp23-32
Poonam Tripathy , Banishree Misra , Byamakesh Nayak
Integration of renewable energy systems (RESs) to the grid leads to various power quality issues. A proper control approach for the interfaced inverter is required to mitigate the uncertainties caused in the grid due to the RESs association to maintain the grid stability. The presence of harmonics and DC offset in the input grid voltage of a phase lock loop (PLL) leads to inaccurate phase estimation due to fundamental frequency oscillations. Though many advanced generalized integrator (GI) based PLLs have been developed still there is a need for a robust PLL for synchronization with faster dynamic response, both the harmonics and DC offset rejection ability with precise estimation. This paper proposes some simple yet effective advanced PLLs employing low pass filters (LPFs) in the existing GI based PLLs for faster and accurate phase angle estimation for seamless synchronization under complex grid circumstances. These advanced generalized integrators with LPFs (GI-LPF) based PLLs will provide enhanced and robust synchronization for the grid integrated RESs thereby addressing multiple power quality issues like voltage unbalance, harmonics and DC offsets. The simulation based comparative analysis of the proposed controllers confirm their effective disturbance rejection capability under complex grid conditions by providing advanced and precise response.
Volume: 39
Issue: 1
Page: 23-32
Publish at: 2025-07-01

Predictive modeling for equity trading using sentiment analysis

10.11591/ijeecs.v39.i1.pp575-584
Chetan Gondaliya , Abhishek Parikh
Warren Buffett’s investment philosophy highlights the importance of generating wealth through available capital, but investors require more advanced tools for informed decision-making. Current research is focused on developing a modeling technique that leverages computer algorithms, including sentiment analysis. This method evaluates public sentiment about companies through social media, aiding investors in identifying promising stocks and safeguarding their wealth against unfavorable market conditions. In India, the banking, real estate, and pharmaceutical sectors are among the most robust and rapidly growing industries; however, deciding to invest in these sectors remains debatable. To address this, the proposed study aims to develop a hybrid prediction model that combines sentiment and technical analysis to uncover short-term trading opportunities. This model utilizes a two-layer ensemble stacking technique, training three distinct machine learning algorithms in the first layer and aggregating their outputs in the second layer. The proposed model significantly outperforms traditional methods in terms of accuracy, enabling investors to make more confident and profitable decisions in the Indian stock market.
Volume: 39
Issue: 1
Page: 575-584
Publish at: 2025-07-01

Enhancing scalability and efficiency in technological transaction utilizing dual-layer blockchain approach

10.11591/ijres.v14.i2.pp452-462
T. Kanimozhi , M. Inbavalli
The leather industry encounters significant challenges in integrating blockchain technology and smart contracts into its complex supply networks. Despite technological advancements, existing supply chain management systems suffer from inefficiencies, opacity, and vulnerabilities to fraud. Blockchain offers promising solutions such as immutable ledgers, decentralized governance, and smart contract automation. However, scalability limitations hinder the efficient handling of high transaction volumes, impacting procurement, production, inventory management, and distribution processes, leading to delays and increased costs. This research aims to address these challenges by exploring innovative approaches, including dual-layer blockchain architectures incorporating sharding and state channels, tailored to the unique needs of the leather industry. By overcoming scalability barriers, the research seeks to unlock the transformative potential of blockchain technology and smart contracts, enhancing transparency, traceability, and efficiency in leather supply chains while ensuring global interoperability and regulatory compliance. Through empirical validation and comparative analysis, this study provides understandings into the practical implementation of blockchain solutions within the leather industry, offering strategic guidance for sustainable supply chain management practices.
Volume: 14
Issue: 2
Page: 452-462
Publish at: 2025-07-01

Pipelined reconfigurable architecture for 5G software-defined radio systems

10.11591/ijres.v14.i2.pp320-327
Vijaya Bhaskar Chalampalem , Munaswamy Pidugu , Sanacarapu Nagaraju
The filters are used to allow a specific band of frequencies. In a wireless communication, the filter is used to select the frequency of operation with a narrow or broad band. As the generations increase the amount of data handled increases drastically. 5G data rate can be significantly deliver up to 20 Gigabits per second while 4G communication data rate is handled in the order of 100 Megabits per second. Now the challenge becomes processing data at such a speed with low power and low area specifications. The filters that can configure themselves as per the data received are reconfigurable filters so that the bandwidth is saved. Also, when the pipelining is introduced, the reconfigurable filter improves the performance of the design. This paper details about the pipelined reconfigurable finite impulse response (RFIR) filter with the simplest algorithm with auto updating capability. The design is modelled in Verilog hardware description language (HDL) language, synthesized for Cyclone III field-programmable gate array (FPGA). The results prove that the proposed filter increases only slightly with respect to delay and power dissipation with a trade off in area and maximum possible clock frequency.
Volume: 14
Issue: 2
Page: 320-327
Publish at: 2025-07-01

Optimizing social media analytics with the data quality enhancement and analytics framework for superior data quality

10.11591/ijres.v14.i2.pp472-480
B. Karthick , T. Meyyappan
his paper introduces the data quality enhancement and analytics (DQEA) framework to enhance data quality in social media analytics through machine learning (ML) algorithms. The efficacy of the framework is validated through features tested against human coders on Amazon Mechanical Turk, achieving an inter-coder reliability score of 0.85, indicating high agreement. Furthermore, two case studies with a large social media dataset from Tumblr were conducted to demonstrate the effectiveness of the proposed content features. In the first case study, the DQEA framework reduced data noise by 30% and bias by 25%, while increasing completeness by 20%. In the second case study, the framework improved data consistency by 35% and overall data quality score by 28%. Comparative analysis with state-of-the-art models, including random forest and support vector machines (SVM), showed significant improvements in data reliability and decision-making accuracy. Specifically, the DQEA framework outperformed the random forest model by 15% in accuracy and 20% in true positive rate, and the SVM model by 10% in error rate reduction and 18% in reliability. The results underscore the potential of advanced data analytics tools in transforming social media data into a valuable asset for organizations, highlighting the practical implications and future research directions in this domain.
Volume: 14
Issue: 2
Page: 472-480
Publish at: 2025-07-01
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