Journal articles: 'MOBILedit'

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Bintang, Rauhulloh Noor, Rusydi Umar, and Anton Yudhana. "Assess of Forensic Tools on Android Based Facebook Lite with the NIST Method." Scientific Journal of Informatics 8, no. 1 (May 10, 2021): 1–9. http://dx.doi.org/10.15294/sji.v8i1.26744.

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The increase in social media use of Facebook lite by using Android-based smartphones is quite high. Activities when communicating through the social media network Facebook Lite Facebook lite can send a text message, image, or Video. Not a few users of Facebook lite social media abusing this app to commit fraud crimes, pornographic acts, or defamation actions from social media users Facebook lite. In such cases, it can be a digital forensic benchmark to get results from digital evidence from the Facebook lite application. In this investigation, National Institute of Standards and Technology NIST research methods with various stages, namely Collection, Examination, Analysis, and Reporting. While the forensic tools to be used are Magnet Axiom Forensic and MOBILedit Forensic Express Pro. Comparison and results of data conducted with forensic tools Magnet Axiom Forensic and MOBILedit Forensic Express Pro in the form of parameter data specified. Axiom Forensic Magnet data is 57.14% while MOBILedit Forensic Express Pro data is 85.71%. This data is the data of the performance results of both forensic tool applications in obtaining digital evidence on Facebook lite application.

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Imam Riadi, Sunardi, and Panggah Widiandana. "Investigating Cyberbullying on WhatsApp Using Digital Forensics Research Workshop." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 4, no. 4 (August 20, 2020): 730–35. http://dx.doi.org/10.29207/resti.v4i4.2161.

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Cyberbullying in group conversations in one of the instant messaging applications is one of the conflicts that occur due to social media, specifically WhatsApp. This study conducted digital forensics to find evidence of cyberbullying by obtaining work in the Digital Forensic Research Workshop (DFRWS). The evidence was investigated using the MOBILedit Forensic Express tool as an application for evidence submission and the Cosine Similarity method to approve the purchase of cyberbullying cases. This research has been able to conduct procurement to reveal digital evidence on the agreement in the Group's features using text using MOBILedit. Identification using the Cosine method. Similarities have supported actions that lead to cyberbullying with different levels Improved Sqrt-Cosine (ISC) value, the largest 0.05 and the lowest 0.02 based on conversations against requests.

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Imam Riadi, Rusydi Umar, and Muhammad Irwan Syahib. "Akuisisi Bukti Digital Viber Messenger Android Menggunakan Metode National Institute of Standards and Technology (NIST)." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 5, no. 1 (February 14, 2021): 45–54. http://dx.doi.org/10.29207/resti.v5i1.2626.

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Viber is one of the most popular social media in the Instant Messenger application category that can be used to send text messages, make voice calls, send picture messages and video messages to other users. As many as 260 million people around the world have used this application. Increasing the number of viber users certainly brings positive and negative impacts, one of the negative impacts of this application is the use of digital forensic crime. This research simulates and removes digital crime evidence from the viber application on Android smartphones using the National Institute of Standards Technology (NIST) method, which is a method that has work guidelines on forensic policy and process standards to ensure each investigator follows the workflow the same so that their work is documented and the results can be accounted for. This study uses three forensic tools, MOBILedit Forensic Express, Belkasoft and Autopsy. The results in this study show that MOBILedit Forensic Express gets digital evidence with a percentage of 100% in getting accounts, contacts, pictures and videos. While proof of digital chat is only 50%. Belkasoft gets digital evidence with a percentage of 100% in getting accounts, contacts, pictures and videos. While proof of digital chat is only 50%. For Autopsy does not give the expected results in the extraction process, in other words the Autopsy application gives zero results. It can be concluded that MOBILedit Forensic Express and Belkasoft have a good performance compared to Autopsy and thus this research has been completed and succeeded in accordance with the expected goals.

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Nasirudin, Nasirudin, Sunardi Sunardi, and Imam Riadi. "Analisis Forensik Smartphone Android Menggunakan Metode NIST dan Tool MOBILedit Forensic Express." Jurnal Informatika Universitas Pamulang 5, no. 1 (March 31, 2020): 89. http://dx.doi.org/10.32493/informatika.v5i1.4578.

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Technological advances are growing rapidly, including mobile device technology, one of which is an Android smartphone that is experiencing rapid progress with a variety of features so that it can spoil its users, with the rapid development of smartphone technology, many users benefit, but many are disadvantaged by the growing smartphone. technology, so that many perpetrators or persons who commit crimes and seek profits with smartphone facilities. Case simulation by securing Samsung Galaxy A8 brand android smartphone evidence using the MOBILedit forensic express forensic tool with the National Institute of Standards and Technology (NIST) method which consists of four stages of collection, examination, analysis and reporting. The results of testing the Samsung Galaxy A8 android smartphone are carried out with the NIST method and the MOBILedit Forensic Express tool obtained by data backup, extraction and analysis so that there are findings sought for investigation and evidence of crimes committed by persons using android smartphone facilities.

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Zuhriyanto, Ikhsan, Anton Yudhana, and Imam Riadi. "Comparative analysis of Forensic Tools on Twitter applications using the DFRWS method." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 4, no. 5 (October 30, 2020): 829–36. http://dx.doi.org/10.29207/resti.v4i5.2152.

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Current crime is increasing, one of which is the crime of using social media, although no crime does not leave digital evidence. Twitter application is a social media that is widely used by its users. Acts of crime such as fraud, insults, hate speech, and other crimes lately use many social media applications, especially Twitter. This research was conducted to find forensic evidence on the social media Twitter application that is accessed using a smartphone application using the Digital Forensics Research Workshop (DFRWS) method. These digital forensic stages include identification, preservation, collection, examination, analysis, and presentation in finding digital evidence of crime using the MOBILedit Forensic Express software and Belkasoft Evidence Center. Digital evidence sought on smartphones can be found using case scenarios and 16 variables that have been created so that digital proof in the form of smartphone specifications, Twitter accounts, application versions, conversations in the way of messages and status. This study's results indicate that MOBILedit Forensic Express digital forensic software is better with an accuracy rate of 85.75% while Belkasoft Evidence Center is 43.75%.

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Ardiningtias, Syifa Riski Ardiningtias, Sunardi Sunardi, and Herman Herman. "INVESTIGASI DIGITAL PADA FACEBOOK MESSENGER MENGGUNAKAN NATIONAL INSTITUTE OF JUSTICE." Jurnal Informatika Polinema 7, no. 4 (August 31, 2021): 19–26. http://dx.doi.org/10.33795/jip.v7i4.709.

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Perkembangan teknologi memudahkan masyarakat dalam berbagi informasi dan berkomunikasi. Facebook Messenger merupakan salah satu instant messanger yang memiliki kelebihan multi-platform yang dapat digunakan oleh pengguna dalam pengiriman pesan teks, gambar, pesan suara, dan video. Selain digunakan sebagai hal untuk kegiatan positif, namun fasilitas dalam teknologi ini juga dapat digunakan untuk melakukan kegiatan negatif. Penelitian ini melakukan investigasi forensik pada simulasi adanya tindakan kejahatan dalam penyebaran konten pornografi menggunakan Facebook Messenger sebagai media komunikasi pada smartphone Android. Pelaku berkomunikasi dan mengirimkan konten pornografi berupa percakapan, audio, dan video kepada pengguna dan kemudian menghapusnya dengan tujuan menghilangkan jejak. Namun, setiap tindak kejahatan dapat meninggalkan barang bukti sehingga selama menyelesaikan masalah ini perlu melakukan investigasi forensik digital. Perangkat berupa smartphone yang dapat digunakan selama objek untuk menemukan bukti digital. Pengangkatan barang bukti dalam penelitian ini menggunakan tools forensik MOBILEdit Forensics dan Wondershare Dr. fone dengan menggunakan kerangka kerja National Institute of Justice (NIJ). Penelitian ini dengan hasilnya kemudian dapat digunakan sebagai bukti oleh investigator atau penyidik dalam menangani sebuah kasus kejahatan dengan hasil yang didapatkan berupa versi aplikasi, akun, email, percakapan, waktu kejadian, gambar, audio, dan video. MobilEdit Forensics memiliki kelebihan dalam mendapatkan barang bukti sebesar 85,71% dibanding Wondershare Dr. fone yang hanya mendapatkan barang bukti hanya 28,57%.

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Riadi, Imam, Sunardi Sunardi, and Sahiruddin Sahiruddin. "Analisis Forensik Recovery pada Smartphone Android Menggunakan Metode National Institute Of Justice (NIJ)." Jurnal Rekayasa Teknologi Informasi (JURTI) 3, no. 1 (June 28, 2019): 87. http://dx.doi.org/10.30872/jurti.v3i1.2292.

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keberadaan smartphone saat ini dianggap sangat membantu aktifitas manusia dalam melakukan pekerjaan sehari- hari. Berkembangnya fitur-fitur yang ada pada smartphone memudahkan para penggunanya beraktifitas seperti melakukan pekerjaan kantor, bisnis, e-banking, dan untuk berinteraksi dengan pengguna lain di media sosial. Perkembangan smartphone tidak hanya memberikan dampak positif tetapi bisa berdampak negatif ketika perkembangan tersebut dimanfaatkan untuk melakukan tindakan kejahatan. Saat ini terdapat banyak kasus penghapusan barang bukti kejahatan yang dilakukan oleh tersangka untuk mengilangkan bukti kejahatan yang dilakukan oleh seorang pelaku. Hal ini menjadi tantangan bagi forensika teknologi informasi dan penegak hukum melakukan penyelidikan secara forensik terhadap smartphone dari tersangka dalam sebuah kasus kejahatan untuk mendapatkan kembali bukti digital yang akan dijadikan sebagai barang bukti dalam sebuah persidangan. Penelitian ini menggunakan tools MOBILedit Forensic, Wondershare dr. Fone for Android, dan Belkasoft Evidence Center untuk memperoleh bukti digital serta menggunakan metode National Institute of Justice (NIJ) yaitu dengan mengidentifikasi, mengusulkan solusi, melakukan uji solusi yang ditawarkan, mengevaluasi dan melaporkan hasil. Dari hasil pengujian tool forensik yang peneliti gunakan, tool MOBILedit Forensic tidak bisa mengembalikan data yang sudah dihapus, tool Wondershare dr. Fone For Android berhasil mengembalikan data kontak, log panggilan,dan pesan yang sudah dihapus, sementara tool Belkasoft Evidence Center hanya bisa mengembalikan data kontak, dan log panggilan yang sudah dihapus.

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Mahendra, Kadek Dwi Oka, and I. Komang Ari Mogi. "Digital Forensic Analysis Of Michat Application On Android As Digital Proof In Handling Online Prostitution Cases." JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) 9, no. 3 (February 18, 2021): 381. http://dx.doi.org/10.24843/jlk.2021.v09.i03.p09.

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Smartphone technology and the Internet are very popular lately, especially with various features, one of which is instant messaging. But behind it all, instant messaging like MiChat is very vulnerable to becoming a crime, one of which is Online Prostitution. To follow up on Online Prostitution activities, it is necessary to carry out mobile forensics to find evidence which is then given to be given to the authorities. This study uses the MiChat application as an online prostitution media, and uses the National Institute of Justice (NIJ) method which has five basic stages is, preparation, collection, examination, analysis, and reporting. This research uses MOBILedit Forensic Express, and SysTools SQLite Viewer.

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Riadi, Imam, Anton Yudhana, and Mushab Al Barra. "Forensik Mobile pada Layanan Media Sosial LinkedIn." JISKA (Jurnal Informatika Sunan Kalijaga) 6, no. 1 (January 20, 2021): 9–20. http://dx.doi.org/10.14421/jiska.2021.61-02.

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The research explores mobile forensic on LinkedIn social media. Forensic mobile finds digital evidence of job hoax cases in LinkedIn, investigation using the NIST (National Institute of Standard and Technology) method. Data collection techniques using Andriller tools in investigations. Data examination using tools Root Browser, Autopsy in the forensic process. data analysis using tools MOBILedit in the forensic process. The investigation found digital evidence of log activity, a status update on LinkedIn. Other results found in the investigation are 17 WiFi password, 117 download history, 263 phone calls, 1 file deleted, 1 file hidden, and 1 file raised, the research has reached the expected target.

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Pawitradi, Gede, and I. Ketut Gede Suhartana. "Acquisition of LINE Digital Social Media Evidence Using the National Institute of Justice (NIJ) Method." JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) 8, no. 2 (January 8, 2020): 129. http://dx.doi.org/10.24843/jlk.2019.v08.i02.p04.

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Nowadays the use of social media has developed very rapidly over time. With very easy to use and also higher security than ordinary messaging services, making one of the factors of social media is very often used in today's world. But behind it all, social media such as LINE is very vulnerable to become one of the crime facilities, one of which is cyberbullying. To follow up on the cyberbullying activity, a forensic cellphone needs to be carried out to find evidence which is then useful to send to court. This study uses the LINE application as cyberbullying crime media, as well as using the National Institute of Justice (NIJ) method. The National Institute of Justice (NIJ) method has five basic stages namely, preparation, collection, examination, analysis, and reporting. In this study using the MOBILedit Forensic tool, and DB Browser for SQLite. It is hoped that the research carried out can help in solving cyberbullying on social media LINE on mobile forensics

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Putra, I. Putu Denny Indra, and I. Ketut Gede Suhartana. "Cyberbullying Analysis on WhatsApp Messenger Using the National Institute of Justice (NIJ) Method." JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) 9, no. 4 (May 29, 2021): 501. http://dx.doi.org/10.24843/jlk.2021.v09.i04.p07.

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Social media is no stranger to today's digital era. Many people prefer to use social media because of its simplicity and safety, This is what makes social media more popular than other services. However, because of its convenience and security, social media, especially WhatsApp Messenger, are vulnerable to crime, one of the most common is cyberbullying. For this reason, a mobile forensic investigation is required to find evidence related to cyberbullying. In this study, the National Institute of Justice (NIJ) method was used to investigate the WhatsApp Messenger platform used for cyberbullying. The NIJ method has 5 (five) stages to carry out the forensic process, namely Preparing, Collection, Examination, Analysis, and Reporting. This study also uses 3 assistance from software, namely MOBILedit Forensic, DB Browser for SQLite, and Odin3. This research is expected to be able to help solve the problem of cyberbullying and other crimes found on social media, especially WhatsApp Messenger.

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Wang, Wei, Yutao Li, Ting Zou, Xin Wang, Jieyu You, and Yanhong Luo. "A Novel Image Classification Approach via Dense-MobileNet Models." Mobile Information Systems 2020 (January 6, 2020): 1–8. http://dx.doi.org/10.1155/2020/7602384.

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As a lightweight deep neural network, MobileNet has fewer parameters and higher classification accuracy. In order to further reduce the number of network parameters and improve the classification accuracy, dense blocks that are proposed in DenseNets are introduced into MobileNet. In Dense-MobileNet models, convolution layers with the same size of input feature maps in MobileNet models are taken as dense blocks, and dense connections are carried out within the dense blocks. The new network structure can make full use of the output feature maps generated by the previous convolution layers in dense blocks, so as to generate a large number of feature maps with fewer convolution cores and repeatedly use the features. By setting a small growth rate, the network further reduces the parameters and the computation cost. Two Dense-MobileNet models, Dense1-MobileNet and Dense2-MobileNet, are designed. Experiments show that Dense2-MobileNet can achieve higher recognition accuracy than MobileNet, while only with fewer parameters and computation cost.

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Wang, Wei, Yiyang Hu, Ting Zou, Hongmei Liu, Jin Wang, and Xin Wang. "A New Image Classification Approach via Improved MobileNet Models with Local Receptive Field Expansion in Shallow Layers." Computational Intelligence and Neuroscience 2020 (August 1, 2020): 1–10. http://dx.doi.org/10.1155/2020/8817849.

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Because deep neural networks (DNNs) are both memory-intensive and computation-intensive, they are difficult to apply to embedded systems with limited hardware resources. Therefore, DNN models need to be compressed and accelerated. By applying depthwise separable convolutions, MobileNet can decrease the number of parameters and computational complexity with less loss of classification precision. Based on MobileNet, 3 improved MobileNet models with local receptive field expansion in shallow layers, also called Dilated-MobileNet (Dilated Convolution MobileNet) models, are proposed, in which dilated convolutions are introduced into a specific convolutional layer of the MobileNet model. Without increasing the number of parameters, dilated convolutions are used to increase the receptive field of the convolution filters to obtain better classification accuracy. The experiments were performed on the Caltech-101, Caltech-256, and Tubingen animals with attribute datasets, respectively. The results show that Dilated-MobileNets can obtain up to 2% higher classification accuracy than MobileNet.

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Xu, Xinzheng, Meng Du, Huanxiu Guo, Jianying Chang, and Xiaoyang Zhao. "Lightweight FaceNet Based on MobileNet." International Journal of Intelligence Science 11, no. 01 (2021): 1–16. http://dx.doi.org/10.4236/ijis.2021.111001.

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Caccese, Jaclyn B., Thomas A. Buckley, and Thomas W. Kaminski. "Sway Area and Velocity Correlated with MobileMat Balance Error Scoring System (BESS) Scores." Journal of Applied Biomechanics 32, no. 4 (August 2016): 329–34. http://dx.doi.org/10.1123/jab.2015-0273.

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The Balance Error Scoring System (BESS) is often used for sport-related concussion balance assessment. However, moderate intratester and intertester reliability may cause low initial sensitivity, suggesting that a more objective balance assessment method is needed. The MobileMat BESS was designed for objective BESS scoring, but the outcome measures must be validated with reliable balance measures. Thus, the purpose of this investigation was to compare MobileMat BESS scores to linear and nonlinear measures of balance. Eighty-eight healthy collegiate student-athletes (age: 20.0 ± 1.4 y, height: 177.7 ± 10.7 cm, mass: 74.8 ± 13.7 kg) completed the MobileMat BESS. MobileMat BESS scores were compared with 95% area, sway velocity, approximate entropy, and sample entropy. MobileMat BESS scores were significantly correlated with 95% area for single-leg (r = .332) and tandem firm (r = .474), and double-leg foam (r = .660); and with sway velocity for single-leg (r = .406) and tandem firm (r = .601), and double-leg (r = .575) and single-leg foam (r = .434). MobileMat BESS scores were not correlated with approximate or sample entropy. MobileMat BESS scores were low to moderately correlated with linear measures, suggesting the ability to identify changes in the center of mass–center of pressure relationship, but not higher-order processing associated with nonlinear measures. These results suggest that the MobileMat BESS may be a clinically-useful tool that provides objective linear balance measures.

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Yang, Jianfei, Han Zou, Shuxin Cao, Zhenghua Chen, and Lihua Xie. "MobileDA: Toward Edge-Domain Adaptation." IEEE Internet of Things Journal 7, no. 8 (August 2020): 6909–18. http://dx.doi.org/10.1109/jiot.2020.2976762.

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Pavate, Aruna, Jay Mistry, Rahul Palve, and Nirav Gami. "Diabetic Retinopathy Detection-MobileNet Binary Classifier." Acta Scientific Medical Sciences 4, no. 12 (November 30, 2020): 86–91. http://dx.doi.org/10.31080/asms.2020.04.0800.

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Lu, Xiaoling, Haifeng Wu, and Yu Zeng. "Classification of Alzheimer’s disease in MobileNet." Journal of Physics: Conference Series 1345 (November 2019): 042012. http://dx.doi.org/10.1088/1742-6596/1345/4/042012.

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Feng, Xiaoqin, Rong Xie, Junyang Sheng, and Shuo Zhang. "Population Statistics Algorithm Based on MobileNet." Journal of Physics: Conference Series 1237 (June 2019): 022045. http://dx.doi.org/10.1088/1742-6596/1237/2/022045.

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Sourav, Shivam, Shikhar Mattoo, and Chitra Nasa. "Face Mask Detection using Mobilenet Technique." International Journal of Computer Applications 183, no. 13 (July 19, 2021): 36–40. http://dx.doi.org/10.5120/ijca2021921445.

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Cho, Jaeseong, Suyeon Jeon, Siyoung Song, Seokyeong Kim, Dohyun Kim, Jongkil Jeong, Goya Choi, and Soongin Lee. "Identification of Toxic Herbs Using Deep Learning with Focus on the Sinomenium Acutum, Aristolochiae Manshuriensis Caulis, Akebiae Caulis." Applied Sciences 9, no. 24 (December 12, 2019): 5456. http://dx.doi.org/10.3390/app9245456.

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Toxic herbs are similar in appearance to those known to be safe, which can lead to medical accidents caused by identification errors. We aimed to study the deep learning models that can be used to distinguish the herb Aristolochiae Manshuriensis Caulis (AMC), which contains carcinogenic and nephrotoxic ingredients from Akebiae Caulis (AC) and Sinomenium acutum (SA). Five hundred images of each herb without backgrounds, captured with smartphones, and 100 images from the Internet were used as learning materials. The study employed the deep-learning models VGGNet16, ResNet50, and MobileNet for the identification. Two additional techniques were tried to enhance the accuracy of the models. One was extracting the edges from the images of the herbs using canny edge detection (CED) and the other was applying transfer learning (TL) to each model. In addition, the sensitivity and specificity of AMC, AC, and SA identification were assessed by experts with a Ph.D. degree in herbology, undergraduates and clinicians of oriental medicine, and the ability was compared with those of MobileNet-TL′s. The identification accuracies of VGGNet16, ResNet50, and MobileNet were 93.9%, 92.2%, and 95.6%, respectively. After adopting the CED technique, the accuracy was 95.0% for VGGNet16, 63.9% for ResNet50, and 80.0% for MobileNet. After using TL without the CED technique, the accuracy was 97.8% for VGGNet16-TL, 98.9% for ResNet50-TL, and 99.4% for MobileNet-TL. Finally, MobileNet-TL showed the highest accuracy among three models. MobileNet-TL had higher identification accuracy than experts with a Ph.D. degree in herbology in Korea. The result identifying AMC, AC, and SA in MobileNet-TL has demonstrated a great capability to distinguish those three herbs beyond human identification accuracy. This study indicates that the deep-learning model can be used for herb identification.

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Sarac, Mine, Mehmet Alper Ergin, Ahmetcan Erdogan, and Volkan Patoglu. "AssistOn-Mobile: a series elastic holonomic mobile platform for upper extremity rehabilitation." Robotica 32, no. 8 (September 16, 2014): 1433–59. http://dx.doi.org/10.1017/s0263574714002367.

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SUMMARYWe present the design, control, and human–machine interface of a series elastic holonomic mobile platform,AssistOn-Mobile, aimed to administer therapeutic table-top exercises to patients who have suffered injuries that affect the function of their upper extremities. The proposed mobile platform is a low-cost, portable, easy-to-use rehabilitation device targeted for home use. In particular,AssistOn-Mobileconsists of a holonomic mobile platform with four actuated Mecanum wheels and a compliant, low-cost, multi-degrees-of-freedom series elastic element acting as its force sensing unit. Thanks to its series elastic actuation,AssistOn-Mobileis highly backdriveable and can provide assistance/resistance to patients, while performing omni-directional movements on plane.AssistOn-Mobilealso features Passive Velocity Field Control (PVFC) to deliver human-in-the-loop contour tracking rehabilitation exercises. PVFC allows patients to complete the contour-tracking tasks at their preferred pace, while providing the proper amount of assistance as determined by the therapists. PVFC not only minimizes the contour error but also does so by rendering the closed-loop system passive with respect to externally applied forces; hence, ensures the coupled stability of the human-robot system. We evaluate the feasibility and effectiveness ofAssistOn-Mobilewith PVFC for rehabilitation and present experimental data collected during human subject experiments under three case studies. In particular, we utilizeAssistOn-Mobilewith PVFC (a) to administer contour following tasks where the pace of the tasks is left to the control of the patients, so that the patients can assume a natural and comfortable speed for the tasks, (b) to limit compensatory movements of the patients by integrating a RGB-D sensor to the system to continually monitor the movements of the patients and to modulate the task speeds to provide online feedback to the patients, and (c) to integrate a Brain–Computer Interface such that the brain activity of the patients is mapped to the robot speed along the contour following tasks, rendering an assist-as-needed protocol for the patients with severe disabilities. The feasibility studies indicate thatAssistOn-Mobileholds promise in improving the accuracy and effectiveness of repetitive movement therapies, while also providing quantitative measures of patient progress.

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Chen, Dong, and Hao Shen. "MAOD: An Efficient Anchor-Free Object Detector Based on MobileDet." IEEE Access 8 (2020): 86564–72. http://dx.doi.org/10.1109/access.2020.2992516.

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Mitschke, Norbert, Michael Heizmann, and Klaus-Henning Noffz. "Ein evolutionärer Ansatz für aufgabenspezifische MobileNet-Topologien." tm - Technisches Messen 86, no. 7-8 (July 26, 2019): 413–21. http://dx.doi.org/10.1515/teme-2019-0020.

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ZusammenfassungInfolge sinkender Hardwarepreise und der zunehmenden Automatisierung wird maschinelles Lernen für industrielle Anwendungen wie klassische Sichtprüfungsaufgaben immer interessanter. In diesem Artikel wird ein metaheuristischer Ansatz für die Suche nach einer allgemeinen MobileNet-Topologie nach Howard et al. [7] vorgestellt, der auf differentieller Evolution beruht. Dieser ist in der Lage, anhand eines gegebenen Datensatzes und ohne zusätzliches Vorwissen einen geeigneten Klassifikator zu entwerfen. Gleichzeitig wird durch die Wahl einer geeigneten Fitnessfunktion der Ressourcenbedarf der Inferenz begrenzt. Für typische industrielle Datensätze können neuronale Netze mit Genauigkeiten von über 99\hspace{0.1667em}\% gefunden werden, während die Rechendauer dafür relativ kurz bleibt.

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So, Min-Hyeok, Cheol-Su Han, and Hack-Yoon Kim. "Defect Classification Algorithm of Fruits Using Modified MobileNet." Journal of Korean Institute of Information Technology 18, no. 7 (July 30, 2020): 81–89. http://dx.doi.org/10.14801/jkiit.2020.18.7.81.

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Bialkowski, M. E., S. T. Jellett, and R. S. Varnes. "Electronically steered antenna system for the australian mobilesat." IEE Proceedings - Microwaves, Antennas and Propagation 143, no. 4 (1996): 347. http://dx.doi.org/10.1049/ip-map:19960396.

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Ghosh, Tapotosh, Md Min-Ha-Zul Abedin, Shayer Mahmud Chowdhury, Zarin Tasnim, Tajbia Karim, S. M. Salim Reza, Sabrina Saika, and Mohammad Abu Yousuf. "Bangla handwritten character recognition using MobileNet V1 architecture." Bulletin of Electrical Engineering and Informatics 9, no. 6 (December 1, 2020): 2547–54. http://dx.doi.org/10.11591/eei.v9i6.2234.

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Handwritten character recognition is a very tough task in case of complex shaped alphabet set like Bangla script. As optical character recognition (OCR) has a huge application in mobile devices, model needs to be suitable for mobile applications. Many researches have been performed in this arena but none of them achieved satisfactory accuracy or could not detect more than 200 characters. MobileNet is a state of art (convolutional neural network) CNN architecture which is designed for mobile devices as it requires less computing power. In this paper, we used MobileNet for handwritten character recognition. It has achieved 96.46% accuracy in recognizing 231 classes (171 compound, 50 basic and 10 numerals), 96.17% accuracy in 171 compound character classes, 98.37% accuracy in 50 basic character classes and 99.56% accuracy in 10 numeral character classes.

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Zaki, Siti Zulaikha Muhammad, Mohd Asyraf Zulkifley, Marzuraikah Mohd Stofa, Nor Azwan Mohammed Kamari, and Nur Ayuni Mohamed. "Classification of tomato leaf diseases using MobileNet v2." IAES International Journal of Artificial Intelligence (IJ-AI) 9, no. 2 (June 1, 2020): 290. http://dx.doi.org/10.11591/ijai.v9.i2.pp290-296.

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<span lang="EN-US">Tomato is a red-colored edible fruit originated from the American continent. There are a lot of plant diseases associated with tomatoes such as leaf mold, late blight, and mosaic virus. Tomato is an important vegetable crop that contributes to the world economically. Despite tremendous efforts in plant management, viral diseases are notoriously difficult to control and eradicate completely. Thus, accurate and faster detection of plant diseases is needed to mitigate the problem at the early stage. A computer vision approach is proposed to identify the disease by capturing the leaf images and detect the possibility of the diseases. A deep learning classifier is utilized to make a robust decision that covers a wide variety of leaf appearances. Compact deep learning architecture, which is MobileNet V2 has been fine-tuned to detect three types of tomato diseases. The algorithm is tested on 4,671 images from PlantVillage dataset. The results show that MobileNet V2 is able to detect the disease up to more than 90% accuracy.</span>

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Wei, Liu, and Lv Peng. "An Efficient OpenCL-Based FPGA Accelerator for MobileNet." Journal of Physics: Conference Series 1883, no. 1 (April 1, 2021): 012086. http://dx.doi.org/10.1088/1742-6596/1883/1/012086.

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Hossain, Syed Mohammad Minhaz, Kaushik Deb, Pranab Kumar Dhar, and Takeshi Koshiba. "Plant Leaf Disease Recognition Using Depth-Wise Separable Convolution-Based Models." Symmetry 13, no. 3 (March 21, 2021): 511. http://dx.doi.org/10.3390/sym13030511.

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Proper plant leaf disease (PLD) detection is challenging in complex backgrounds and under different capture conditions. For this reason, initially, modified adaptive centroid-based segmentation (ACS) is used to trace the proper region of interest (ROI). Automatic initialization of the number of clusters (K) using modified ACS before recognition increases tracing ROI’s scalability even for symmetrical features in various plants. Besides, convolutional neural network (CNN)-based PLD recognition models achieve adequate accuracy to some extent. However, memory requirements (large-scaled parameters) and the high computational cost of CNN-based PLD models are burning issues for the memory restricted mobile and IoT-based devices. Therefore, after tracing ROIs, three proposed depth-wise separable convolutional PLD (DSCPLD) models, such as segmented modified DSCPLD (S-modified MobileNet), segmented reduced DSCPLD (S-reduced MobileNet), and segmented extended DSCPLD (S-extended MobileNet), are utilized to represent the constructive trade-off among accuracy, model size, and computational latency. Moreover, we have compared our proposed DSCPLD recognition models with state-of-the-art models, such as MobileNet, VGG16, VGG19, and AlexNet. Among segmented-based DSCPLD models, S-modified MobileNet achieves the best accuracy of 99.55% and F1-sore of 97.07%. Besides, we have simulated our DSCPLD models using both full plant leaf images and segmented plant leaf images and conclude that, after using modified ACS, all models increase their accuracy and F1-score. Furthermore, a new plant leaf dataset containing 6580 images of eight plants was used to experiment with several depth-wise separable convolution models.

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Husein, Amir Mahmud, Christopher Christopher, Andy Gracia, Rio Brandlee, and Muhammad Haris Hasibuan. "Deep Neural Networks Approach for Monitoring Vehicles on the Highway." SinkrOn 4, no. 2 (April 14, 2020): 163. http://dx.doi.org/10.33395/sinkron.v4i2.10553.

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Vehicle classification and detection aims to extract certain types of vehicle information from images or videos containing vehicles and is one of the important things in a smart transportation system. However, due to the different size of the vehicle, it became a challenge that directly and interested many researchers . In this paper, we compare YOLOv3's one-stage detection method with MobileNet-SSD for direct vehicle detection on a highway vehicle video dataset specifically recorded using two cellular devices on highway activities in Medan City, producing 42 videos, both methods evaluated based on Mean Average Precision (mAP) where YOLOv3 produces better accuracy of 81.9% compared to MobileNet-SSD at 67.9%, but the size of the resulting video file detection is greater. Mobilenet-SSD performs faster with smaller video output sizes, but it is difficult to detect small objects.

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Caccese, Jaclyn B., and Thomas W. Kaminski. "Comparing Computer-Derived and Human-Observed Scores for the Balance Error Scoring System." Journal of Sport Rehabilitation 25, no. 2 (May 2016): 133–36. http://dx.doi.org/10.1123/jsr.2014-0281.

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Context:The Balance Error Scoring System (BESS) is the current standard for assessing postural stability in concussed athletes on the sideline. However, research has questioned the objectivity and validity of the BESS, suggesting that while certain subcategories of the BESS have sufficient reliability to be used in evaluation of postural stability, the total score is not reliable, demonstrating limited interrater and intrarater reliability. Recently, a computerized BESS test was developed to automate scoring.Objective:To compare computerderived BESS scores with those taken from 3 trained human scorers.Design:Interrater reliability study.Setting:Athletic training room.Patients:NCAA Division I student athletes (53 male, 58 female; 19 ± 2 y, 168 ± 41 cm, 69 ± 4 kg).Interventions:Subjects were asked to perform the BESS while standing on the Tekscan (Boston, MA) MobileMat® BESS. The MobileMat BESS software displayed an error score at the end of each trial. Simultaneously, errors were recorded by 3 separate examiners. Errors were counted using the standard BESS scoring criteria.Main Outcome Measures:The number of BESS errors was computed for the 6 stances from the software and each of the 3 human scorers. Interclass correlation coefficients (ICCs) were used to compare errors for each stance scored by the MobileMat BESS software with each of 3 raters individually. The ICC values were converted to Fisher Z scores, averaged, and converted back into ICC values.Results:The double-leg, single-leg, and tandem-firm stances resulted in good agreement with human scorers (ICC = .999, .731, and .648). All foam stances resulted in fair agreement.Conclusions:Our results suggest that the MobileMat BESS is suitable for identifying BESS errors involving each of the 6 stances of the BESS protocol. Because the MobileMat BESS scores consistently and reliably, this system can be used with confidence by clinicians as an effective alternative to scoring the BESS.

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郭, 奕君. "Multi-National Face Classification and Recognition Based on MobileNet Network." Journal of Image and Signal Processing 09, no. 03 (2020): 146–55. http://dx.doi.org/10.12677/jisp.2020.93018.

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Li, Yiting, Haisong Huang, Qingsheng Xie, Liguo Yao, and Qipeng Chen. "Research on a Surface Defect Detection Algorithm Based on MobileNet-SSD." Applied Sciences 8, no. 9 (September 17, 2018): 1678. http://dx.doi.org/10.3390/app8091678.

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This paper aims to achieve real-time and accurate detection of surface defects by using a deep learning method. For this purpose, the Single Shot MultiBox Detector (SSD) network was adopted as the meta structure and combined with the base convolution neural network (CNN) MobileNet into the MobileNet-SSD. Then, a detection method for surface defects was proposed based on the MobileNet-SSD. Specifically, the structure of the SSD was optimized without sacrificing its accuracy, and the network structure and parameters were adjusted to streamline the detection model. The proposed method was applied to the detection of typical defects like breaches, dents, burrs and abrasions on the sealing surface of a container in the filling line. The results show that our method can automatically detect surface defects more accurately and rapidly than lightweight network methods and traditional machine learning methods. The research results shed new light on defect detection in actual industrial scenarios.

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Kosankar, Swati, and Dr Vasima Khan. "Flower Classification using MobileNet: An Optimized Deep Learning Model." IJARCCE 8, no. 2 (February 28, 2019): 186–92. http://dx.doi.org/10.17148/ijarcce.2019.8233.

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Chen, Junde, Defu Zhang, Md Suzauddola, and Adnan Zeb. "Identifying crop diseases using attention embedded MobileNet-V2 model." Applied Soft Computing 113 (December 2021): 107901. http://dx.doi.org/10.1016/j.asoc.2021.107901.

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Boyinbode, Olutayo, Dick Ng’ambi, and Antoine Bagula. "An Interactive Mobile Lecturing Model." International Journal of Mobile and Blended Learning 5, no. 2 (April 2013): 1–21. http://dx.doi.org/10.4018/jmbl.2013040101.

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Although use of podcasts and vodcasts are increasingly becoming popular in higher education, their use is usually unidirectional and therefore replicates the transmission mode of traditional face-to-face lectures. In this paper, the authors propose a tool, MOBILect, a mobile lecturing tool that enables users to comment on lecture vodcasts using mobile devices, and aggregated comments become an educational resource. The vodcasts are generated through Opencast Matterhorn and YouTube. The tool was evaluated at the University of Cape Town with students’ own devices. The paper reports on the architecture of the MOBILect, its framework for student-vodcast interaction, and evaluation results. The paper concludes that the MOBILect has potential for use as a supplement to the traditional face-to-face lectures especially in scenarios of large classes, or where the medium of instruction is not the students’ mother tongue.

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Scidŕ, Giuseppe. "La mobilitŕ spaziale di entitŕ tangibili nella societŕ globale." SOCIOLOGIA URBANA E RURALE, no. 86 (April 2009): 41–64. http://dx.doi.org/10.3280/sur2008-086003.

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- The spatial mobility of tangible entities in a global society, The paper handles some tangible consequences of the mobiletic revolution, as a necessary but not exhaustive catalyst of the evolutionary process of globalization whose effects have deep repercussions on the social, economic and territorial organization of the social system both at a national and an international level. For the social scientists coining the formula "mobiletic revolution" by the middle of the Sixties, the overall results seem to be expressed by a new global society benefiting a sharp drop of space friction. Today, the related consequences of it find their evidence in the people, goods and information mobility, respectively through public and private networks, through the transport system and finally through the communication structure development. In turn, such changes produce a number of interactions and synergies caused by the growth of each of the three mobility carriers, which gradually brought the human beings to an ambiguous cultural adjustment as regards the new shaped space-time dimensions. Key words: mobiletic revolution, social change, social relations, mobility carriers.

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Bouguezzi, Safa, Hana Ben Fredj, Tarek Belabed, Carlos Valderrama, Hassene Faiedh, and Chokri Souani. "An Efficient FPGA-Based Convolutional Neural Network for Classification: Ad-MobileNet." Electronics 10, no. 18 (September 16, 2021): 2272. http://dx.doi.org/10.3390/electronics10182272.

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Convolutional Neural Networks (CNN) continue to dominate research in the area of hardware acceleration using Field Programmable Gate Arrays (FPGA), proving its effectiveness in a variety of computer vision applications such as object segmentation, image classification, face detection, and traffic signs recognition, among others. However, there are numerous constraints for deploying CNNs on FPGA, including limited on-chip memory, CNN size, and configuration parameters. This paper introduces Ad-MobileNet, an advanced CNN model inspired by the baseline MobileNet model. The proposed model uses an Ad-depth engine, which is an improved version of the depth-wise separable convolution unit. Moreover, we propose an FPGA-based implementation model that supports the Mish, TanhExp, and ReLU activation functions. The experimental results using the CIFAR-10 dataset show that our Ad-MobileNet has a classification accuracy of 88.76% while requiring little computational hardware resources. Compared to state-of-the-art methods, our proposed method has a fairly high recognition rate while using fewer computational hardware resources. Indeed, the proposed model helps to reduce hardware resources by more than 41% compared to that of the baseline model.

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Arifin, Fatchul, Herjuna Artanto, Nurhasanah, and Teddy Surya Gunawan. "Fast COVID-19 Detection of Chest X-Ray Images Using Single Shot Detection MobileNet Convolutional Neural Networks." Journal of Southwest Jiaotong University 56, no. 2 (April 30, 2021): 235–48. http://dx.doi.org/10.35741/issn.0258-2724.56.2.19.

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COVID-19 is a new disease with a very rapid and tremendous spread. The most important thing needed now is a COVID-19 early detection system that is fast, easy to use, portable, and affordable. Various studies on desktop-based detection using Convolutional Neural Networks have been successfully conducted. However, no research has yet applied mobile-based detection, which requires low computational cost. Therefore, this research aims to produce a COVID-19 early detection system based on chest X-ray images using Convolutional Neural Network models to be deployed in mobile applications. It is expected that the proposed Convolutional Neural Network models can detect COVID-19 quickly, economically, and accurately. The used architecture is MobileNet's Single Shot Detection. The advantage of the Single Shot Detection MobileNet models is that they are lightweight to be applied to mobile-based devices. Therefore, these two versions will also be tested, which one is better. Both models have successfully detected COVID-19, normal, and viral pneumonia conditions with an average overall accuracy of 93.24% based on the test results. The Single Shot Detection MobileNet V1 model can detect COVID-19 with an average accuracy of 83.7%, while the Single Shot Detection MobileNet V2 Single Shot Detection model can detect COVID-19 with an average accuracy of 87.5%. Based on the research conducted, it can be concluded that the approach to detecting chest X-rays of COVID-19 can be detected using the MobileNet Single Shot Detection model. Besides, the V2 model shows better performance than the V1. Therefore, this model can be applied to increase the speed and affordability of COVID-19 detection.

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Ramalingam, Balakrishnan, Vega-Heredia Manuel, Mohan Rajesh Elara, Ayyalusami Vengadesh, Anirudh Krishna Lakshmanan, Muhammad Ilyas, and Tan Jun Yuan James. "Visual Inspection of the Aircraft Surface Using a Teleoperated Reconfigurable Climbing Robot and Enhanced Deep Learning Technique." International Journal of Aerospace Engineering 2019 (September 12, 2019): 1–14. http://dx.doi.org/10.1155/2019/5137139.

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Aircraft surface inspection includes detecting surface defects caused by corrosion and cracks and stains from the oil spill, grease, dirt sediments, etc. In the conventional aircraft surface inspection process, human visual inspection is performed which is time-consuming and inefficient whereas robots with onboard vision systems can inspect the aircraft skin safely, quickly, and accurately. This work proposes an aircraft surface defect and stain detection model using a reconfigurable climbing robot and an enhanced deep learning algorithm. A reconfigurable, teleoperated robot, named as “Kiropter,” is designed to capture the aircraft surface images with an onboard RGB camera. An enhanced SSD MobileNet framework is proposed for stain and defect detection from these images. A Self-filtering-based periodic pattern detection filter has been included in the SSD MobileNet deep learning framework to achieve the enhanced detection of the stains and defects on the aircraft skin images. The model has been tested with real aircraft surface images acquired from a Boeing 737 and a compact aircraft’s surface using the teleoperated robot. The experimental results prove that the enhanced SSD MobileNet framework achieves improved detection accuracy of aircraft surface defects and stains as compared to the conventional models.

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Adytia, Nico Ricky, and Gede Putra Kusuma. "Indonesian License Plate Detection and Identification Using Deep Learning." International Journal of Emerging Technology and Advanced Engineering 11, no. 7 (July 26, 2021): 1–7. http://dx.doi.org/10.46338/ijetae0721_01.

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Abstract— License plate is the unique identity of the vehicle, which serves as proof of the legitimacy of the operation of the vehicle in the form of a plate or other material with certain specifications issued by the police and contains the area code, registration number and validity period and installed on the vehicle. License plates are often used in automated parking systems and vehicle identification in case of traffic violations. So, it is necessary to build a system for detection and identification of license plates. The proposed license plate detection and identification system is divided into three main processes, namely license plate detection, character segmentation, and character recognition. The detection process uses transfer learning techniques using Faster R-CNN Inception V2. The segmentation process uses traditional computer vision with morphological operations and contours extraction. Then the character recognition process uses the MobileNet V2 transfer learning technique as an architecture for character classification. The recognition accuracy compared between MobileNet V2 and TesseractOCR shows that MobileNet V2 is superior with an accuracy rate of 96%, while Tesseract-OCR has a poor accuracy of 59%. Keywords— Deep Learning, Convolutional Neural Network, License Plate Detection, Character Segmentation, Character Recognition

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Buddhikot, Milind, Adiseshu Hari, Kundan Singh, and Scott Miller. "MobileNAT: A New Technique for Mobility Across Heterogeneous Address Spaces." Mobile Networks and Applications 10, no. 3 (June 2005): 289–302. http://dx.doi.org/10.1007/s11036-005-6423-3.

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S, Dhivya, and Usha Devi G. "TAMIZHİ: Historical Tamil-Brahmi Script Recognition Using CNN and MobileNet." ACM Transactions on Asian and Low-Resource Language Information Processing 20, no. 3 (July 3, 2021): 1–26. http://dx.doi.org/10.1145/3402891.

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Computational epigraphy is the study of an ancient script where the computer science and mathematical model is relatively built for epigraphy. The Tamil-Brahmi inscriptions are the most ancient of the extant written of the Tamil. The inscriptions furnish valuable information on many aspects of life in the ancient Tamil country from a period anterior to the literary age of Sangam. The recognition of the script and systematic analysis of the script is required. The recognition of this script is complex, containing various curves for a single character and the style of writing overlap with curves and lines. Generating corpus of the script is necessary, since it is the initial step for computational epigraphy. The archaeological department has supported the raw data that helped to develop a corpus of Tamizhi. In this article, we have implemented a convolution neural network in various ways, i.e., (i) Training the CNN model from scratch a Softmax classifier in a sequential model (ii) using MobileNet: Transfer learning paradigm from a pre-trained model on a Tamizhi dataset (iii) Building Model with CNN and SVM (iv) SVM for evaluation of best accuracy to recognize handwritten Brahmi characters. To train the CNN Model an extensive TAMIZHİ handwritten Brahmi Dataset of 1lakh and 90,000 isolated samples for the character has been created and deployed. The designed dataset consists of 9 vowels and 18 consonants and 209 class so researchers can use machine learning. MobileNet outperformed among all the models implemented with the accuracy of 68.3%, whereas other algorithm ranges from 58% to 67% with respect to the Tamizhi dataset. MobileNet model is trained and tested for the dataset of vowels (8 class), consonants (18 class), and consonants vowels (26 class) with the accuracy of 98.1%, 97.7%, 97.5%, respectively.

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Lin, Zhe, and Wenxuan Guo. "Cotton Stand Counting from Unmanned Aerial System Imagery Using MobileNet and CenterNet Deep Learning Models." Remote Sensing 13, no. 14 (July 18, 2021): 2822. http://dx.doi.org/10.3390/rs13142822.

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An accurate stand count is a prerequisite to determining the emergence rate, assessing seedling vigor, and facilitating site-specific management for optimal crop production. Traditional manual counting methods in stand assessment are labor intensive and time consuming for large-scale breeding programs or production field operations. This study aimed to apply two deep learning models, the MobileNet and CenterNet, to detect and count cotton plants at the seedling stage with unmanned aerial system (UAS) images. These models were trained with two datasets containing 400 and 900 images with variations in plant size and soil background brightness. The performance of these models was assessed with two testing datasets of different dimensions, testing dataset 1 with 300 by 400 pixels and testing dataset 2 with 250 by 1200 pixels. The model validation results showed that the mean average precision (mAP) and average recall (AR) were 79% and 73% for the CenterNet model, and 86% and 72% for the MobileNet model with 900 training images. The accuracy of cotton plant detection and counting was higher with testing dataset 1 for both CenterNet and MobileNet models. The results showed that the CenterNet model had a better overall performance for cotton plant detection and counting with 900 training images. The results also indicated that more training images are required when applying object detection models on images with different dimensions from training datasets. The mean absolute percentage error (MAPE), coefficient of determination (R2), and the root mean squared error (RMSE) values of the cotton plant counting were 0.07%, 0.98 and 0.37, respectively, with testing dataset 1 for the CenterNet model with 900 training images. Both MobileNet and CenterNet models have the potential to accurately and timely detect and count cotton plants based on high-resolution UAS images at the seedling stage. This study provides valuable information for selecting the right deep learning tools and the appropriate number of training images for object detection projects in agricultural applications.

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Boyinbode, Olutayo, Dick Ng', and N. A. ambi. "MOBILect: an interactive mobile lecturing tool for fostering deep learning." International Journal of Mobile Learning and Organisation 9, no. 2 (2015): 182. http://dx.doi.org/10.1504/ijmlo.2015.070706.

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Wibowo, Adi, Cahyo Adhi Hartanto, and Panji Wisnu Wirawan. "Android skin cancer detection and classification based on MobileNet v2 model." International Journal of Advances in Intelligent Informatics 6, no. 2 (July 12, 2020): 135. http://dx.doi.org/10.26555/ijain.v6i2.492.

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The latest developments in the smartphone-based skin cancer diagnosis application allow simple ways for portable melanoma risk assessment and diagnosis for early skin cancer detection. Due to the trade-off problem (time complexity and error rate) on using a smartphone to run a machine learning algorithm for image analysis, most of the skin cancer diagnosis apps execute the image analysis on the server. In this study, we investigate the performance of skin cancer images detection and classification on android devices using the MobileNet v2 deep learning model. We compare the performance of several aspects; object detection and classification method, computer and android based image analysis, image acquisition method, and setting parameter. Skin cancer actinic Keratosis and Melanoma are used to test the performance of the proposed method. Accuracy, sensitivity, specificity, and running time of the testing methods are used for the measurement. Based on the experiment results, the best parameter for the MobileNet v2 model on android using images from the smartphone camera produces 95% accuracy for object detection and 70% accuracy for classification. The performance of the android app for object detection and classification model was feasible for the skin cancer analysis. Android-based image analysis remains within the threshold of computing time that denotes convenience for the user and has the same performance accuracy with the computer for the high-quality images. These findings motivated the development of disease detection processing on android using a smartphone camera, which aims to achieve real-time detection and classification with high accuracy.

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Michele, Aurelia, Vincent Colin, and Diaz D. Santika. "MobileNet Convolutional Neural Networks and Support Vector Machines for Palmprint Recognition." Procedia Computer Science 157 (2019): 110–17. http://dx.doi.org/10.1016/j.procs.2019.08.147.

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Jellett, S. T., M. E. Bialkowski, and A. P. Dimitrios. "An experimental investigation into microstrip antenna elements suitable for mobilesat applications." Microwave and Optical Technology Letters 6, no. 4 (March 20, 1993): 240–45. http://dx.doi.org/10.1002/mop.4650060409.

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Khasoggi, Barlian, Ermatita Ermatita, and Samsuryadi Samsuryadi. "Efficient mobilenet architecture as image recognition on mobile and embedded devices." Indonesian Journal of Electrical Engineering and Computer Science 16, no. 1 (October 1, 2019): 389. http://dx.doi.org/10.11591/ijeecs.v16.i1.pp389-394.

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The introduction of a modern image recognition that has millions of parameters and requires a lot of training data as well as high computing power that is hungry for energy consumption so it becomes inefficient in everyday use. Machine Learning has changed the computing paradigm, from complex calculations that require high computational power to environmentally friendly technologies that can efficiently meet daily needs. To get the best training model, many studies use large numbers of datasets. However, the complexity of large datasets requires large devices and requires high computing power. Therefore large computational resources do not have high flexibility towards the tendency of human interaction which prioritizes the efficiency and effectiveness of computer vision. This study uses the Convolutional Neural Networks (CNN) method with MobileNet architecture for image recognition on mobile devices and embedded devices with limited resources with ARM-based CPUs and works with a moderate amount of training data (thousands of labeled images). As a result, the MobileNet v1 architecture on the ms8pro device can classify the caltech101 dataset with an accuracy rate 92.4% and 2.1 Watt power draw. With the level of accuracy and efficiency of the resources used, it is expected that MobileNet's architecture can change the machine learning paradigm so that it has a high degree of flexibility towards the tendency of human interaction that prioritizes the efficiency and effectiveness of computer vision.

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Journal articles on the topic 'MOBILedit'

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