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29,939 Article Results

TechTrolley-enhancing the retail experience

10.11591/ijeecs.v37.i3.pp1476-1486
Dhananjay Rajendra Chavan , Roshan Mahadev Sherekar , Sarthak Praveen Khudbhaiye , Jaya Zalte
In the modern era, convenience and efficiency have become essential aspects of daily life, and grocery shopping is no exception. The traditional shopping experience, characterized by long queues and time-consuming checkout processes, can be frustrating and inefficient. To address these challenges, the TechTrolley has emerged as an innovative solution, leveraging Bluetooth and radio frequency identification (RFID) technology to revolutionize the grocery shopping experience. With the help of TechTrolley, customer can seamlessly complete the shopping by scanning and purchasing the products, controlling the trolley with the use of controller integrated in application, getting details of the products and price in the application and over LCD display embedded on the trolley, complete the checkout process at billing counter. With the need to implement, we need an RFID tag, ESP32, LCD display, L298N motor driver and battery to implement the motion features of a trolley, database for storing the user and product details, a bridge network through router to establish the network between admin, user and the trolley in order to invoke the real time updates.
Volume: 37
Issue: 3
Page: 1476-1486
Publish at: 2025-03-01

Enhanced skin cancer classification via Xception model

10.11591/ijaas.v14.i1.pp69-76
Qurban Ali Memon , Namya Musthafa , Muhammad Masud , Ghaya Al Ameri
Skin cancer is a prevalent and deadly cancer, and early detection is crucial for improving treatment success. Intelligent technologies are currently being used to classify skin lesions. The fundamental goal of this experimental research is to investigate biomedical skin cancer datasets to develop an effective approach for determining whether a cancer is malignant or benign. Well-known deep learning classification models (convolutional neural network (CNN) (sequential), ResNet50, InceptionV3, and Xception) are employed to train and categorize the dataset images. Two large and balanced datasets are collected and employed in this research. One is used to compare the performance of the employed model algorithms. Next, the selected model(s) are again trained on the second dataset for validation and generalization purposes. It turns out that the performance of the Xception model is superior and can be generalized. The performance results obtained from various simulations are tabulated and graphed. Comparative results are also presented.
Volume: 14
Issue: 1
Page: 69-76
Publish at: 2025-03-01

Authenticated image encryption using robust chaotic maps and enhanced advanced encryption standard

10.11591/ijeecs.v37.i3.pp1543-1554
Rupaliben V. Chothe , Sunita P. Ugale , Dinesh M. Chandwadkar , Shraddha V. Shelke
The ability of advanced encryption standard (AES) algorithm to protect information systems has given cryptography a new dimension. Recent encryption approaches to enhance randomness include the use of chaotic algorithms, which provide resistance to differential attacks. We have proposed the application of robust chaotic maps in the block cipher to design a secure authenticated encryption scheme to get advantages of both. The chaotic sequence is generated using hyperbolic tangent map and added to input image initially to increase randomness. The basic 256-bit AES key is generated using the robust Renyi modulo map. An additional 128-bit key enhances security. Instead of static values used in AES, dynamic initialization vector (IV), different for every image will be generated. The results are mathematically verified using various security parameters. The algorithm provides lower values of peak signal-to-noise ratio (PSNR) (7.81 to 9.10 dB) for encrypted images and higher dissimilarities between input and encrypted image histograms. Thus, it is highly resistant to statistical attacks. The experimental results and their comparison prove the superiority of our proposed cryptosystem against statistical, differential and brute-force attacks. Thus, the novel multi-chaotic AES-GCM (galois/counter mode) algorithm can be used for color image encryption in military and industrial applications demanding high data security and authentication.
Volume: 37
Issue: 3
Page: 1543-1554
Publish at: 2025-03-01

Machine learning model approach in cyber attack threat detection in security operation center

10.11591/csit.v6i1.p80-90
Muhammad Ajran Saputra , Deris Stiawan , Rahmat Budiarto
The evolution of technology roles attracted cyber security threats not only compromise stable technology but also cause significant financial loss for organizations and individuals. As a result, organizations must create and implement a comprehensive cybersecurity strategy to minimize further loss. The founding of a cybersecurity surveillance center is one of the optimal adopted strategies, known as security operation center (SOC). The strategy has become the forefront of digital systems protection. We propose strategy optimization to prevent or mitigate cyberattacks by analyzing and detecting log anomalies using machine learning models. This study employs two machine learning models: the naïve Bayes model with multinomial, Gaussian, and Bernoulli variants, and the support vector machine (SVM) model with radial basis function (RBF), linear, polynomial, and sigmoid kernel variants. The hyperparameters in both models are then optimized. The models with optimized hyperparameters are subsequently trained and tested. The experimental results indicate that the best performance is achieved by the RBF kernel SVM model, with an accuracy of 79.75%, precision of 80.8%, recall of 79.75%, and F1-score of 80.01%; and the Gaussian naïve Bayes model, with an accuracy of 70.0%, precision of 80.27%, recall of 70.0%, and F1-score of 70.66%. Overall, both models perform relatively well and are classified in the very good category (75%‒89%).
Volume: 6
Issue: 1
Page: 80-90
Publish at: 2025-03-01

Field programmable gate array simulation and study on different multiplexer hardware for electronics and communication

10.11591/csit.v6i1.p28-39
Arvind Kumar , Adesh Kumar , Anurag Vijay Agrawal
Multiplexing is the technique of transmitting two or more separate signals concurrently using a single communication channel. Multiplexing enables the augmentation of communication channels and consequently the volume of data that may be transmitted. Communication networks utilize diverse multiplexing techniques. An input multiplexer amalgamates various network signals into a singular composite signal before transmission over a shared medium. The composite signal is broken back into its component signals by a demultiplexer, when it reaches its destination, allowing further operations to utilize them separately. The design of the hardware chip depends on the configuration of the multiplexer and demultiplexer in the communication system. The work is presented as a study of the digital logic design and simulation of the different configurations of the multiplexer hardware. The performance evaluation is carried out on the different series of Xilinx field programmable gate array (FPGA) such as Spartan-6, Spartan-3E, Virtex-5, and Virtex-6 with logically checked in Xilinx ISE waveform simulator software. The current analysis of the design and simulation of different configurations of the multiplexer design helps the designers to estimate the chip performance. The novelty of the work lies in its scalable and programmable architecture fitted for specific communication systems that assess performance based on latency, frequency, and power consumption that can be further linked with communication protocols.
Volume: 6
Issue: 1
Page: 28-39
Publish at: 2025-03-01

Power of analytic tools in Oxygen Forensic® Detective based on NIST cybersecurity framework

10.11591/csit.v6i1.p8-19
Tole Sutikno , Iqbal Busthomi
The National Institute of Standards and Technology (NIST) cybersecurity framework is a systematic approach for assessing and improving cybersecurity procedures in digital investigations. Oxygen Forensic® Detective is a digital forensic software that integrates multiple analytic tools to assist investigators in extracting valuable insights from digital evidence. The analytic tools, including timeline, social graph, image categorization, facial categorization, maps, data search, key evidence, optical character recognition, statistics, and translation, assist investigators in thoroughly analyzing digital artifacts, establishing connections, and accurately classifying images with precision and effectiveness. By incorporating these analytical resources into Oxygen Forensic® Detective, a comprehensive strategy is established to effectively combat cyber threats. The NIST cybersecurity framework is incorporated into the tool, offering a methodical approach to identifying and reducing cybersecurity risks. Law enforcement agencies can enhance the productivity and effectiveness of their forensic methodologies by implementing these advanced technologies. This can result in successful prosecutions and improved cybersecurity practices.  Overall, the utilization of analytical tools in criminological inquiries has experienced a substantial rise in the contemporary digital era.
Volume: 6
Issue: 1
Page: 8-19
Publish at: 2025-03-01

Analysis of telehealth acceptance for basic life support training in sudden cardiac arrest in Pontianak

10.11591/csit.v6i1.p48-57
Ruhil Iswara , Sri Kusumadewi , Rahadian Kurniawan
Sudden cardiac arrest (SDA), which is one of the most prevalent causes of mortality, can be prevented by quickly conducting basic life support (BLS). In Pontianak City, the challenges associated with obtaining emergency health training, such as BLS, remain high. This study aims to evaluate user acceptance of telehealth as well as its effectiveness in BLS training. We will also discuss its impact on community knowledge and skills in managing cardiac arrest. We used the HOT-Fit method to analyze the level of acceptance of telehealth in BLS training. We collected data from 60 respondents who underwent telehealth-based BLS training. The results showed that participants' understanding and readiness in dealing with heart attack emergencies had increased significantly, by 90% and 92%, respectively. Analysis of the level of acceptance with HOT-Fit showed that system quality had the greatest influence on system use (0.611). Service quality exerted the most significant impact on user satisfaction (0.568). The net benefit was influenced by system use, user satisfaction, and organizational support, with user satisfaction having the greatest influence (0.600). Further research will be conducted on the utilization of augmented reality (AR) or virtual reality (VR) technology to implement telehealth for BLS training.
Volume: 6
Issue: 1
Page: 48-57
Publish at: 2025-03-01

Secure e-voting system using Schorr's zero-knowledge identification protocol

10.11591/csit.v6i1.p20-27
Indah Octaviani Laleb , Daniel M.D.U. Kasse
In today's era of technological progress, the electoral system has changed significantly with the introduction of electronic voting (e-voting). The traditional voting system poses many vulnerabilities to manipulation, potential human error, and problems with voter privacy. These limitations can lead to reduced trust and participation in elections. E-voting has emerged to address this issue, aiming to improve the convenience, security, and privacy of voters. E-voting systems are evaluated on accuracy, security, privacy, and transparency; however, ensuring voter privacy while maintaining these principles remains a significant challenge. A potential solution to improving privacy in e-voting is Schorr's zero-knowledge identification protocol. This protocol allows voters to confirm their identity without revealing personal information, maintaining voter privacy throughout the process. By implementing these protocols, the e-voting system can strengthen security and privacy, making elections more transparent and trustworthy. As technology evolves, adopting solutions like Schorr's zero-knowledge identification protocol can help e-voting systems meet the growing demand for safe, fair, and private elections.
Volume: 6
Issue: 1
Page: 20-27
Publish at: 2025-03-01

Detection of android malware with deep learning method using convolutional neural network model

10.11591/csit.v6i1.p68-79
Reza Maulana , Deris Stiawan , Rahmat Budiarto
Android malware is an application that targets Android devices to steal crucial data, including money or confidential information from Android users. Recent years have seen a surge in research on Android malware, as its types continue to evolve, and cybersecurity requires periodic improvements. This research focuses on detecting Android malware attack patterns using deep learning and convolutional neural network (CNN) models, which classify and detect malware attack patterns on Android devices into two categories: malware and non-malware. This research contributes to understanding how effective the CNN models are by comparing the ratio of data used with several epochs. We effectively use CNN models to detect malware attack patterns. The results show that the deep learning method with the CNN model can manage unstructured data. The research results indicate that the CNN model demonstrates a minimal error rate during evaluation. The comparison of accuracy, precision, recall, F1 Score, and area under the curve (AUC) values demonstrates the recognition of malware attack patterns, reaching an average of 92% accuracy in data testing. This provides a holistic understanding of the model's performance and its practical utility in detecting Android malware.
Volume: 6
Issue: 1
Page: 68-79
Publish at: 2025-03-01

Geoinformation system for monitoring forest fires and data encryption for low-orbit vehicles

10.11591/csit.v6i1.p58-67
Khuralay Moldamurat , Makhabbat Bakyt , Dastan Yergaliyev , Dinara Kalmanova , Anuar Galymzhan , Abylaikhan Sapabekov
This article discusses two important aspects of unmanned aerial vehicles (UAVs): forest fire monitoring and data security for low-orbit vehicles. The first part of the article is devoted to the development of a geographic information system (GIS) for monitoring and forecasting the spread of forest fires. The system uses intelligent processing of aerospace data obtained from UAVs to timely detect fires, determine their characteristics and forecast the dynamics of development. The second part of the article focuses on the problem of high-speed encryption of data transmitted from low-orbit aircraft. An effective encryption algorithm is proposed that ensures high data processing speed and reliable protection of information from unauthorized access. The article presents the results of modeling and analysis of the effectiveness of the proposed solutions.
Volume: 6
Issue: 1
Page: 58-67
Publish at: 2025-03-01

Matrix inversion using multiple-input multiple-output adaptive filtering

10.11591/csit.v6i1.p1-7
Muhammad Yasir Siddique Anjum , Javed Iqbal
A new approach for matrix inversion is introduced. The approach is based on vector representation of multiple-input multiple-output (MIMO) channel matrix, in which the channel matrix is described by a linear combination of channel vectors weighted by their respective system inputs. The MIMO system output is then fed into a bank of adaptive filters, wherein the response of a given adaptive filter is iteratively minimized to match its output to the given system input. In doing so, adaptive filters equalize the impact of respective channel vectors on the MIMO channel output, while simultaneously orthogonalizing themselves from all other channel vectors, forming the channel matrix inverse. The method demonstrates satisfactory convergence and accuracy in Monte Carlo simulations conducted with varying signal-to-noise ratios (SNRs) and matrix conditioning scenarios. The suggested approach, by virtue of its adaptable characteristics, can also be employed for time-varying linear equation systems.
Volume: 6
Issue: 1
Page: 1-7
Publish at: 2025-03-01

Technical and cost analysis of an electric hand plow tractor for specific land in Java, Indonesia

10.11591/ijpeds.v16.i1.pp175-184
Cuk Supriyadi Ali Nandar , Setyo Margo Utomo , Endra Dwi Purnomo , Amiruddin Aziz , Lia Amelia , Achmad Ridho Mubarak , Marsalyna Marsalyna , Sherly Octavia Saraswati , Fandy Septian Nugroho
This study focuses on the technical and cost analysis of an electric hand plow tractor, especially in the East Java region. The manufacturing cost of electric tractors increases significantly in line with the battery capacity. Although the manufacturing cost of an electric tractor is 3–5 times higher than that of a fuel tractor, the operational cost of an electric tractor is about 79% that of a fuel tractor. Based on the investment analysis, it is feasible to assembly electric tractors with a power of 5.5 HP or 4.1 kW using NMC18650 and NMC21700 batteries with an energy capacity of 14 kWh in case for rural residents with access to an electricity network available in their paddy fields. The price of electricity and the unit cost of a battery pack have a large impact on operational costs. The manufacture of electric tractors will be more attractive to get better economic returns when the price of electricity does not increase, and the unit cost of battery packs falls due to the battery technology trend. Nevertheless, certain challenges to the utilization of electric tractors are the farmers' preferences and habits, market demands, the environment, and the regulations of the tractor component manufacturers.
Volume: 16
Issue: 1
Page: 175-184
Publish at: 2025-03-01

Fuzzy analytic hierarchy process for analysis of barriers to halal supply chain adoption in Indonesia

10.11591/ijaas.v14.i1.pp268-275
Dana Marsetiya Utama , Ardi Agustia Bahtiar
The increasing awareness of the importance of halal certification has prompted companies to evaluate the barriers to adopting the halal supply chain. While this adoption has the potential for significant benefits, various barriers must be investigated. This study examines the barriers to adopting halal supply chains in small and medium-sized food enterprises (SMEs). The fuzzy analytical hierarchy process (fuzzy AHP) assesses and weighs the 30 identified barriers. The results showed that the main barriers to adopting a halal supply chain include understanding and awareness of the importance of halal certification, support from the government and related institutions, and companies' internal readiness to implement halal standards. In addition, other significant barriers were high certification costs, lack of funds to promote the halal industry, lack of willingness to adopt and implement halal in the supply chain, and lack of technology costs to manage supply chain processes by halal standards. The implications of this study suggest the need for better support strategies from the government and relevant agencies, as well as awareness and understanding-raising efforts among SMEs to overcome these barriers and facilitate the adoption of halal supply chains.
Volume: 14
Issue: 1
Page: 268-275
Publish at: 2025-03-01

Digital twin software provider: key players, rankings and trends

10.11591/ijaas.v14.i1.pp1-10
Lutz Sommer
Current studies about digital twins (DT) generally provide little support to interested parties in selecting suitable providers. The aim of the present study is, on the one hand, to ask whether existing rankings could be used to derive a quick, cost-efficient decision. On the other hand, it is to be questioned how the provider market is developing. Therefore, the study based on the one hand on 10 provider rankings of DT and the other hand on detailed research of n=153 providers was included for a detailed analysis of trends. The following findings were obtained: i) The selected top 10 rankings can serve as a fast, cost-efficient selection approach for providers; ii) the DT market is dominated by established, large top providers with North American locations; and iii) Since 2010 there has been a trend in the form of a disproportionately large number of new providers.
Volume: 14
Issue: 1
Page: 1-10
Publish at: 2025-03-01

Evolving strategies in anti-phishing: an in-depth analysis of detection techniques and future research directions

10.11591/ijeecs.v37.i3.pp1726-1733
Preeti Preeti , Priti Sharma
Phishing attacks are a major digital threat, impacting individuals and organizations globally. This review paper examines evolving anti-phishing strategies by analyzing five key techniques: URL blacklists, visual similarity detection, heuristic methods, machine learning models, and deep learning techniques. Each technique is evaluated for its mechanisms, unique features, and challenges. A systematic literature survey (SLR) is conducted to compare these methods; effectiveness. The paper highlights significant research challenges and suggests future directions, emphasizing the integration of artificial intelligence and behavioral analytics to combat evolving phishing tactics, this study aims to advance understanding and inspire more effective anti-phishing solutions.
Volume: 37
Issue: 3
Page: 1726-1733
Publish at: 2025-03-01
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