1 10 6 https://repository.horizon.ac.id/files/original/7b0d7fb30d105df9fff96c4bdad25d76.pdf a92c8a1814a83dc87abb32deaa62bc17 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL. 13 NO. 2 (2024) JUNE 2024 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Development of a Self Organizing Multipurpose Mobile Robot Subject The topic of the resource remote monitoring, obstacle avoidance, sensor integration, self-organizing multipurpose mobile robot. Description An account of the resource Development of a Self-Organizing Multipurpose Mobile Robot is aimed at creating a versatile robotic system capable of autonomously navigating through environments, monitoring surroundings, and streaming real-time data. The project focused on integrating advanced sensors and technology to achieve seamless obstacle avoidance, environmental data collection, and remote monitoring. It involved identifying the project requirements, selecting appropriate hardware and software components, assembling the chassis, mounting sensors, and coding for integration. The core hardware components included the ESP32-CAM module, flame sensor, PIR motion sensor, light detector sensor, rain sensor, and others. These components synergized to enable the robot to interact with its environment and make informed decisions. It further delved into the construction process, detailing the steps of assembling the chassis, mounting components, wiring sensors, and programming the microcontroller. The integration of various sensors was meticulously orchestrated to ensure accurate data collection and real-time video streaming. Subsequent testing and result analysis validated the project's success in achieving its goals. The robot demonstrated efficient obstacle avoidance, precise environmental data acquisition, and stable remote video streaming. The project's coding and system design facilitated seamless collaboration between hardware and software components, culminating in a functional and agile robotic system. The discussion and performance analysis section elaborated on the significance of sensor integration, coding efficiency, and seamless communication. In conclusion, the project's success underscored the potential of mobile robotics in diverse applications. The project's outcomes lay the groundwork for future innovations in robotic systems, emphasizing the importance of strategic integration, meticulous coding, and comprehensive testing in achieving functional and adaptable robots. Creator An entity primarily responsible for making the resource Afolabi Awodeyi, Ayoola E. Akindele, Eduediuyai Ekerette Dan Omolegho A. Ibok Source A related resource from which the described resource is derived www.ijcit.com Date A point or period of time associated with an event in the lifecycle of the resource June 2024 Contributor An entity responsible for making contributions to the resource PERI IRAWAN Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT https://repository.horizon.ac.id/files/original/a3034e2ca79103f7847c685aca1ce489.pdf 07d09e01f393ef7c3bd30abcb1612059 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL. 13 NO. 2 (2024) JUNE 2024 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource A Systematic Literature Review on Phishing Detection Model Subject The topic of the resource component; phishing, algorithms, detection, social media, attack Description An account of the resource This paper introduces a unique method using supervised learning techniques in a hybrid crime detection model to identify phishing attempts on social media sites. Effective detection systems are desperately needed given the rise in criminality on social media, especially phishing. The suggested model combines the best features of several supervised learning algorithms which comprises of random forest, decision tree, support vector machine which are frequently used in analyzing the phishing attacks, taking use of their capacity to extrapolate patterns from labeled datasets and spot questionable behavior suggestive of phishing efforts. The commonly used algorithm was Decision Tree (DT), with 14% of the total, followed by Random Forest (RF), Support Vector Machine (SVM), and Naïve Bayes (12%), with 8%. The least popular algorithms were LSTM, SCS, STARMA, AUC, and FURIA, with 2% each. Decision trees and Support Vector Machines (SVMs) are often used in phishing assault detection since they excel at classification tasks exactly what phishing detection entail. The reason for this is their ability to differentiate between trustworthy and malevolent websites or emails. Decision trees offer a clear and concise example of decision-making processes. Decision tree (DT) presents several gaps which need to be solved, should important characteristics associated with phishing offenses be omitted or misidentified, the efficacy of the model may be jeopardized. Overfitting and class imbalance is a common problem with decision trees, particularly when working with complicated datasets. This might result in poor generalization to fresh, untested data, which would make the model less effective at identifying unusual phishing scams. Phishing statistics on social media frequently exhibit a class imbalance, with a comparatively smaller number of phishing crimes than lawful activity. Creator An entity primarily responsible for making the resource Nicholas Muriuki Muriithi, Josphat Karani Source A related resource from which the described resource is derived www.ijcit.com Date A point or period of time associated with an event in the lifecycle of the resource June 2024 Contributor An entity responsible for making contributions to the resource PERI IRAWAN Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT https://repository.horizon.ac.id/files/original/bd5b00125b057f54d8e06f3158a6eea0.pdf 62487d67c9e1f9219ea0874a3eb03a53 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL. 13 NO. 2 (2024) JUNE 2024 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Trends in Water Consumption Patterns Amongst Various Utility Users in Fort Portal City Subject The topic of the resource Water consumption patterns, Seasonal variations, Factors influencing water consumption, Infrastructure development Description An account of the resource This study investigates trends in water consumption patterns among diverse utility users in Fort Portal City, Uganda, by analyzing data obtained from the National Water and Sewerage Corporation (NWSC) spanning the period from January 2009 to December 2015. The objectives of the study are threefold: 1) to analyze long-term water consumption trends, 2) to identify seasonal variations in water consumption, and 3) to assess the factors influencing water consumption. The analysis of NWSC data reveals a consistent increase in overall water demand over the study period. This growth is attributed to factors such as population expansion, urbanization, and economic development within the city. Furthermore, we observe variations in water usage patterns among different user categories, with residential users showing steady growth and industrial users displaying fluctuations in demand. Seasonal variations in water consumption are pronounced, with dry seasons witnessing heightened water use, particularly by residential and commercial users. These findings highlight the necessity for adaptive water management strategies to address peak demands during dry periods and advocate responsible water use practices. Multiple factors influence water consumption, including population dynamics, economic activities, and NWSC policies and pricing structures. This necessitates a multifaceted approach to water resource management, tailored to the specific needs of diverse user categories. The implications of this study underscore the importance of infrastructure development, water storage solutions, and resource allocation to meet the growing water demand in Fort Portal City. Public awareness campaigns, infrastructure maintenance, and collaborative efforts among stakeholders are recommended to promote responsible water use and equitable access to water services. Creator An entity primarily responsible for making the resource Samuel OCEN, Derrick Mwanjje, Peter Muyambyomu Source A related resource from which the described resource is derived www.ijcit.com Date A point or period of time associated with an event in the lifecycle of the resource June 2024 Contributor An entity responsible for making contributions to the resource PERI IRAWAN Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT https://repository.horizon.ac.id/files/original/f62e19a9079d3647a1998ee8a32198b1.pdf 23ac6442a5f8b113034d68041efe4666 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL. 13 NO. 2 (2024) JUNE 2024 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Fake Clothing Detection Using Deep Learning Method Subject The topic of the resource Fake clothing, cloth quality authentication, neural network, autoencoder, counterfeiting detection Description An account of the resource Manufacture and distribution of fake clothing material which can be inferred to be criminal in nature has become a rapidly growing online shopping concern. It can be seen as a way of disguising false information as legitimate one. Indeed, many fashion industries face challenging times to meet market sales and expected profits once fake clothing products are sold on street corners. The consequences of clothing counterfeiting also range from huge losses to buyers and sellers of original products to health hazards, loss of image, and slow growth. More so, while IT has been beneficial, the introduction of IT has also provided a global platform for elusive counterfeiters and traders. The need for efficient/effective techniques for identifying or differentiating original clothing materials from fake ones is consequently on a geometric rise as well. This study developed and evaluated a fake cloth detection model using Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Autoencoder using Python programming. The goal was to improve the capacity for discerning genuine and fake fabric items through image analysis. Dataset acquired from Kaggle was used for the training, testing, and validation phases in the ratio of 70:20:10 respectively. The processes includes resizing the images to a uniform size, converting them to grayscale or applying color normalization, and removing any irrelevant information. Data augmentation methods were applied to enhance the dataset's diversity. Results obtained from the implementation of the model shows that the CNN model achieved perfect precision and accuracy, indicating that it performed well on the dataset. The RNN model achieved 97% precision while the Autoencoder model had a lower precision and accuracy compared to the CNN and RNN models. It correctly identified 63% of the positive instances, but its overall accuracy was 56%, indicating that it struggled with correct classification. These results also highlight the importance of selecting appropriate algorithms that align with the specific task requirements, especially as it found the autoencoder may excel in unsupervised learning scenarios, but its limitations become apparent in supervised classification tasks like fake cloth detection. Creator An entity primarily responsible for making the resource Olufunke Janet Ehineni, Gabriel Babatunde Iwasokun, Arome Junior Gabriel, Samuel Olutayo Ogunlana, David Bamidele Adewole, Ibraheem Temitope Jimoh Source A related resource from which the described resource is derived www.ijcit.com Date A point or period of time associated with an event in the lifecycle of the resource June 2024 Contributor An entity responsible for making contributions to the resource PERI IRAWAN Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT https://repository.horizon.ac.id/files/original/80615f3faf6440920f470786181d4dad.pdf a00771519d8a6f214a605961435f14d7 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL. 13 NO. 2 (2024) JUNE 2024 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource The Development of the RSU U2 Net+ Architecture for Brain Tumor Segmentation in 3D Images Subject The topic of the resource component; Computer Vision, Deep Learning, RSU U2 Net+, Brain Tumor Description An account of the resource Segmenting brain tumors in medical images plays a crucial role in diagnosis and monitoring of medical conditions. However, the segmentation process is still performed manually, consuming time and exhibiting variability among assessors. This research aims to develop the RSU U2-Net+ architecture for brain tumor multilabel segmentation in 3D images. The RSU U2-Net+ architecture consists of 9 interconnected blocks, employing broader connectivity in each block. The architecture is reinforced with the use of Residual U-blocks (RSU) to enhance image understanding across various scales without significantly increasing computational load. Testing on data reveals that the RSU U2-Net+ architecture performs well, as indicated by a dice coefficient score of 0.779, IoU of 0.6439, recall of 0.7541, and specificity of 0.9911. Evaluation is also conducted for each tumor label. Recall and specificity for edema are 0.8690 and 0.9851, for enhancing tumor are 0.7991 and 0.9956, and for non-enhancing tumor are 0.5942 and 0.9927. This research makes a significant contribution to the development of advanced medical image analysis technology. The achieved results have tangible benefits for medical practitioners and patients, with the potential to enhance the speed and consistency of brain tumor segmentation in 3D medical images. Creator An entity primarily responsible for making the resource Elvaret, Habibullah Akbar Source A related resource from which the described resource is derived www.ijcit.com Date A point or period of time associated with an event in the lifecycle of the resource June 2024 Contributor An entity responsible for making contributions to the resource PERI IRAWAN Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT https://repository.horizon.ac.id/files/original/14b1d3b879ae0335fcfb16e8f1c1453c.pdf a4b6a95468e653e2fe89514d3d87c4da Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL. 13 NO. 2 (2024) JUNE 2024 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Overcoming the Challenge of Non-Optimal AP Selection in Wi-Fi Roaming Subject The topic of the resource OFDMA (Orthogonal Frequency Division Multiple Access), TWT (Target Wake Time), BSS Coloring, Beamforming, MU-MIMO (Multi-User, Multiple Input, Multiple Output), AP (Access Point), IoT (Internet of Things), STA (station) Description An account of the resource This paper addresses the critical issue of non-optimal Access Point (AP) selection in wireless networks, which significantly impacts network efficiency and user satisfaction. In dynamic wireless environments, particularly those integrating Internet of Things (IoT) applications, clients often face challenges in maintaining optimal connectivity due to suboptimal AP selection and inefficient roaming. This results in degraded network performance, characterized by reduced data transfer speeds, increased latency, and higher likelihood of connection drops. The paper delves into the intricacies of client-AP connectivity, exploring the reasons behind non-optimal AP selection and the lack of efficient roaming. It employs simulation tools and analytical methodologies to offer insights into identifying, analyzing, and resolving these issues. The goal is to enhance the client-AP connectivity experience in wireless networks, ensuring that they are robust, capable, and intelligently adaptive to the dynamic movements and requirements of users. Key strategies discussed include advanced algorithms for AP selection that go beyond mere signal strength, implementation of fast roaming technologies like IEEE 802.11r, 802.11k, and 802.11v, and smart AP management techniques such as load balancing and dynamic channel assignment. The paper also highlights the significant improvements brought by Wi-Fi 6 (802.11ax) in enhancing the Wi-Fi roaming experience through features such as Orthogonal Frequency Division Multiple Access (OFDMA), Target Wake Time (TWT), and BSS Coloring. The paper underscores the need for a comprehensive approach to address the challenge of non-optimal AP selection and efficient roaming in wireless networks. By leveraging advanced selection algorithms, implementing smart AP management strategies, and adopting fast roaming technologies, it is possible to significantly enhance the performance and user experience of wireless networks in our increasingly connected world. Creator An entity primarily responsible for making the resource Nuwan Kuruwitaarachchi Source A related resource from which the described resource is derived www.ijcit.com Date A point or period of time associated with an event in the lifecycle of the resource June 2024 Contributor An entity responsible for making contributions to the resource PERI IRAWAN Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT