TELKOMNIKA (Telecommunication Computing Electronics and Control)
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Factors influencing blockchain adoption intention in Philippine small and medium enterprises
Full text linkAs an emerging technology, blockchain has huge potential for transforming various industries, such as small and medium enterprises (SMEs). Despite its promising impact, its application in the supply chains of SMEs in developing countries is still in its infancy. This study analyzes the key factors of blockchain adoption intention in Philippine SMEs through an integrated technology-organization-environment (TOE) and technology acceptance model (TAM) with external variables. The data were obtained through a survey of 465 SME practitioners in the national capital region (NCR), Philippines, and analyzed using partial least squares and structural equation modeling (PLS-SEM). In terms of technology dimensions, relative advantage (RLA) had a positive influence on perceived usefulness (PUS) while compatibility (COM) had a positive influence on perceived ease of use (PEU), which both subsequently led to blockchain adoption intention. As regards organization, top management support (TMS) had a significant influence on the adoption intention of blockchain among SMEs. In terms of environment, only competitive pressure (CMP) had significant influence on blockchain adoption intention. In general, most of the hypothesized relationships are significant; thus, SMEs have a positive interest in adopting blockchain technology. Finally, the study serves as baseline evidence of blockchain adoption intention among SMEs in the Philippines
EdgeShield: a robust and agile cybersecurity architecture for the internet of medical things
Full text linkWe present EdgeShield, a lightweight pipeline that streamlines internet of medical things (IoMT) traffic analysis by pairing aggressive dimensionality-reduction with federated model aggregation. It employs systematic preprocessing, advanced feature selection, and robust sampling to reduce computational overhead while enhancing performance. Through feature engineering techniques such as principal component analysis (PCA), targeted feature selection, and embedding methods, EdgeShield reduces dataset dimensionality by 96%, enabling near real-time detection and prevention of cyber attacks on resource-constrained edge devices. To harden the IoMT perimeter, EdgeShield trains ten lightweight edge models in just 54s and merges their parameters into a single global clas sifier with negligible extra delay. This method requires no additional training or predictions, thus accelerating deployment. Additionally, by using a compact dataset with five top-performing features and PCA with two components, EdgeShield consistently achieves accuracy levels exceeding 99.2% for individual edge models and the consolidated global model. With a built-in continuous improvement loop, EdgeShield dynamically adapts to emerging data patterns and operational conditions, driving substantial advancements in IoMT ecosystem management. This approach delivers both rapid machine learning model deployment and robust cyber attack detection, illustrating its potential to revolutionize IoMT security and elevate healthcare data integrity
Perceived ease of use, usefulness, and task interdependence: impacts on employee performance in higher education
Full text linkThis research aimed to investigate the relationship between perceived ease of use (PEU), perceived usefulness (PU), and task interdependence (TASKINT) on employees’ work performance. Technology acceptance model (TAM) was used as a theoretical perspective to explore technology adoption in the context of employees in higher education using electronic asset management (EAM). Moreover, a quantitative method was used to explain the causality of the relationship between the variables, and a total of 380 respondents were determined as the sample. The results showed that PEU and usefulness had a significant effect on TASKINT. Even though PEU and TASKINT had a significant effect on employees’ work performance, PU did not have a significant effect. In addition, the results showed TASKINT significantly mediated the relationship between perceived ease of use, usefulness, and employees’ work performance. These findings imply that enhancing the ease of use and fostering TASKINT can lead to improved employee performance when adopting new technologies. For higher education institutions (HEI), focusing on user-friendly systems and promoting collaborative tasks can maximize the benefits of technology implementation on work performance
Identification of working memory status in children from EEG signal features using discrete wavelet transform
Full text linkThe conventional method for assessing the working memory performance of children is time-consuming and potentially inaccurate, especially when dealing with many samples. Therefore, an automated system that can produce swift and accurate results is required. Electroencephalograms (EEG) can be used to analyse the working memory status of children by extracting specific features from the EEG signal, which can be incorporated into an automatic system to reduce manpower and processing time for analysis. This project used EEG recording to identify children’s working memory status while they were performing working memory tasks. EEG signals were acquired from both children and adults using an automated computer-based working memory assessment tool, processed, and analyzed. The discrete wavelet transform (DWT) was then employed to identify five distinct working memory statuses: distracted, confused, daydreaming, losing focus, and active. DWT was also used to extract features that demonstrate these various statuses. The results showed that DWT could accurately identify the working memory status of both children and adults from their EEGs. This work has thus provided a more efficient method for extracting features from EEG signals to identify working memory statuses in both children and adults
Adversarial-robust steganalysis system leveraging adversarial training and EfficientNet
Full text linkSteganalysis aims to detect hidden messages within digital media, presenting a significant challenge in the field of information security. This paper introduces an adversarial-robust steganalysis system leveraging adversarial training and the powerful feature extraction capabilities of EfficientNet. We utilize EfficientNet to extract robust features from images, which are subsequently classified by a dense neural network to distinguish between steganographic and non-steganographic content. To enhance the system’s resilience against adversarial attacks, we implement a custom adversarial training loop that generates adversarial examples using the fast gradient sign method (FGSM) and integrates these examples into the training process. Our results demonstrate that the proposed system not only achieves high accuracy in detecting steganographic content but also maintains robustness against adversarial perturbations. This dual approach of leveraging state-of-the-art deep learning architectures and adversarial training provides a significant advancement in the field of steganalysis, ensuring more reliable detection of hidden messages in digital images
Customer segmentation in e-commerce: K-means vs hierarchical clustering
Full text linkCustomer segmentation is important for e-commerce companies to understand and target different customers. The primary focus of this work is the application and comparison of K-means clustering and hierarchical clustering, unsupervised machine learning techniques, in customer segmentation for e-commerce platforms. Clustering leverages customer search behavior, reflecting brand preferences, and identifying distinct customer segments. The proposed work explores the K-means algorithm and hierarchical clustering. It uses them to classify customers in a standard e-commerce customer dataset, mainly focused on frequently searched brands. Both techniques are compared based on silhouette scores and cluster visualizations. K-means clustering yielded well-separated segments compared to hierarchical clustering. Then, using the K-means algorithm, customers are classified into different segments based on brand search patterns. Further, targeted marketing strategies are discussed for each segment. Results show three customer segments: high searchers-low buyers, loyal customers, and moderate engagers. The proposed work provides valuable insights into customers that could be used for developing targeted marketing campaigns, product recommendations, and customer engagement strategies to enhance the conversion rate, customer satisfaction, and, in turn, the growth of an e-commerce platform
Prototype of alternate wetting and drying rice cultivation using internet of things for precision agriculture
Full text linkThis study introduces a semi-automatic system for alternating wet and dry rice cultivation using internet of things (IoT) technology to enhance precision agriculture and address critical challenges in water resource management. The prototype consists of node and master devices powered by ESP32 microcontrollers integrated with sensors to monitor air temperature, humidity, and water levels. Communication between the devices is achieved through the low-latency, low-power encrypted secure protocol-network over wireless (ESP-NOW) protocol, enabling real-time monitoring and remote control of water pumps. Data collected by the system is displayed on ThinkSpeak servers and Nextion touch screens, aiding efficient irrigation and environmental management for farmers. Performance testing demonstrates that the system achieves reliable communication up to 115 meters with efficient energy consumption, operating for approximately two hours with a 3,000 mAh battery. By optimizing irrigation practices, the system reduces water waste while ensuring adequate crop hydration, promoting sustainable farming practices. This scalable IoT solution not only enhances productivity and resource efficiency but also contributes to broader efforts in agricultural sustainability by supporting precise environmental control and minimizing dependency on manual labor
Human–robot collaboration with mixed reality for interactive and safe workspaces
Full text linkRealizing seamless collaboration between humans and robots in shared workspaces requires advanced systems that can ensure safety and efficiency while considering the inherent unpredictability of human movement. This paper proposes a system that integrates mixed reality (MR) and robotics through a unified coordinate system to facilitate real-time interaction and collaboration. By leveraging a MR interface, human collaborators can visualize and interact with the projected paths of the robotic arms, thereby enhancing both spatial awareness and task coordination. The proposed system adapts the robot’s movement path dynamically using the Voronoi diagram algorithm to modify trajectories in response to the detection of a human hand within a predefined caution zone. This mechanism reduces the risk of collisions, which ensures safer collaborative environments. The proposed system’s ability to exchange motion information between the operator and the robot supports real-time adjustments and promotes an intuitive and efficient collaborative experience. Our findings suggest that integrating MR technology in human–robot collaboration systems can improve safety protocols and operational fluidity dramatically, thereby representing a significant step forward in the development of safe, efficient, and effective interactive robot systems
Analyzing temporal properties of speech trajectory using graph structures towards speech recognition
Full text linkSpeech signal analysis aims to identify patterns within data to develop effec tive recognition algorithms. This process primarily utilizes feature extraction techniques such as linear predictive coding (LPC), linear predictive cepstral co efficients (LPCCs), and Mel-frequency cepstral coefficients (MFCCs). These features are crucial for constructing recognition algorithms that leverage both statistical and deep learning methods. While deep learning models require ex tensive datasets, they often prove unsuitable for low-resource languages. The Hidden Markov model (HMM)is the most widely adopted statistical framework in speech processing. However, HMMs are characterized by state-dependent models, where each state interacts only with its neighboring states. This limita tion restricts HMMs from capturing long-term signal properties, highlighting the need for addressing these constraints at the feature extraction stage. Most feature extraction methods rely on short-term signal processing, which further limits the comprehension of speech utterances. To overcome these limitations, alter native methods are necessary to capture more comprehensive patterns. This pa per presents a graph-based approach for analyzing speech trajectories and their temporal properties, which are subsequently validated using HMMs in speech recognition tasks. Graph-based representations on a low-resource Telugu dataset improve recognition accuracy by 13% while reducing processing time compared to traditional LPC
Escalating QoS by firefly optimization of CGSTEB routing protocol with subordinate energy alert gateways
Full text linkWireless sensor networks (WSNs) comprise large numbers of sensor nodes that are highly constrained by limited battery power, making energy-efficient routing essential for sustaining network lifetime and service quality. Among existing solutions, the general self-organized tree-based energy balancing (GSTEB) pro tocol with clustering has been widely adopted for energy-aware communication. However, GSTEB and its clustered variant often suffer from energy imbalance, high packet loss, and reduced quality of service (QoS) due to excessive load on cluster heads (CHs). To address these challenges, this paper introduces an enhanced routing framework that integrates firefly optimization with clustered GSTEB(CGSTEB)andintroduces subordinate energy alert gateways (SEAGs). The firefly algorithm is applied to optimize CH selection through a fitness func tion that balances residual energy and node proximity, ensuring efficient cluster formation and adaptive load distribution. Meanwhile, SEAGs establish a two hop communication model between CHs and the base station (BS), reducing CH energy consumption and preventing premature node failures. Simulation exper iments conducted in NS2 demonstrate that the proposed firefly-CGSTEB with SEAG significantly improves QoS metrics, including network lifetime, energy utilization, throughput, and packet loss rate, compared with conventional CG STEB. These results confirm the effectiveness of combining metaheuristic opti mization with gateway-assisted routing for resilient and energy-efficient WSNs