Hybridization of cognitive computing for food services
Article
Zhang, X., Yang, S., Srivastava, G., Chen, M. and Cheng, X. 2020. Hybridization of cognitive computing for food services. Applied Soft Computing. 89. https://doi.org/10.1016/j.asoc.2019.106051
| Type | Article |
|---|---|
| Title | Hybridization of cognitive computing for food services |
| Authors | Zhang, X., Yang, S., Srivastava, G., Chen, M. and Cheng, X. |
| Abstract | The application of data mining technology to food services and the restaurant industry has certain social value. By predicting customer traffic and needs, a restaurant can prepare a reasonable amount of meals for customers according to predicted needs which is conducive to improving the dining experience of customers and also improving the quality of food preparation and making the restaurant itself operate more efficiently. In recent years, we have seen the use of collaborative robots for use in the fast food industry. In Asia and more specifically in Japan, we have seen many fast-food chains implement the use of robots to better serve their customers. By studying the linear regression algorithm and the random forest algorithm, this paper proposes a new interwoven novel fusion approach of combining both algorithms and applies the new model to restaurant data to assist in the prediction of customer traffic in the restaurant industry. This predictive algorithm using cognitive techniques can assist these newly place robots in the food industry better serve their client base and in doing so make the industry more efficient. Experimental, comparison, and analysis are reported in the paper. The error rate of the fusion solution is reduced by approximately 5.503% compared with the linear regression algorithm and is approximately 3.719% lower than the error rate of the random forest algorithm. Results show that the new fusion algorithm can achieve better prediction results of customer traffic prediction for the restaurant industry. Furthermore, we also provide a new take on the application of data mining technology in the restaurant industry itself. |
| Keywords | Linear regression, random forest, cognitive computing, food service robots, data mining, restaurant industry, fusion |
| Publisher | Elsevier |
| Journal | Applied Soft Computing |
| ISSN | 1568-4946 |
| Publication dates | |
| Online | 02 Jan 2020 |
| 01 Apr 2020 | |
| Publication process dates | |
| Deposited | 13 Mar 2020 |
| Accepted | 25 Dec 2019 |
| Output status | Published |
| Accepted author manuscript | License |
| Copyright Statement | © 2020. This manuscript version is made available under the CC-BY-NC-ND 4.0 license. |
| Digital Object Identifier (DOI) | https://doi.org/10.1016/j.asoc.2019.106051 |
| Language | English |
Permalink -
https://repository.mdx.ac.uk/item/88x44
Download files
48
total views41
total downloads4
views this month1
downloads this month
Export as
Related outputs
Social and moral psychology of COVID-19 across 69 countries
Azevedo, F., Pavlović, T., Rêgo, G., Ay, F., Gjoneska, B., Etienne, T., Ross, R., Schönegger, P., Riaño-Moreno, J., Cichocka, A., Capraro, V., Cian, L., Longoni, C., Chan, H., Van Bavel, J., Sjåstad, H., Nezlek, J., Alfano, M., Gelfand, M., Birtel, M., Cislak, A., Lockwood, P., Abts, K., Agadullina, E., Aruta, J., Besharati, S., Bor, A., Choma, B., Crabtree, C., Cunningham, W., De, K., Ejaz, W., Elbaek, C., Findor, A., Flichtentrei, D., Franc, R., Gruber, J., Gualda, E., Horiuchi, Y., Huynh, T., Ibanez, A., Imran, M., Israelashvili, J., Jasko, K., Kantorowicz, J., Kantorowicz-Reznichenko, E., Krouwel, A., Laakasuo, M., Lamm, C., Leygue, C., Lin, M., Mansoor, M., Marie, A., Mayiwar, L., Mazepus, H., McHugh, C., Minda, J., Mitkidis, P., Olsson, A., Otterbring, T., Packer, D., Perry, A., Petersen, M., Puthillam, A., Rothmund, T., Santamaría-García, H., Schmid, P., Stoyanov, D., Tewari, S., Todosijević, B., Tsakiris, M., Tung, H., Umbres, R., Vanags, E., Vlasceanu, M., Vonasch, A., Yucel, M., Zhang, Y., Abad, M., Adler, E., Akrawi, N., Mdarhri, H., Amara, H., Amodio, D., Antazo, B., Apps, M., Ba, M., Barbosa, S., Bastian, B., Berg, A., Bernal-Zárate, M., Bernstein, M., Białek, M., Bilancini, E., Bogatyreva, N., Boncinelli, L., Booth, J., Borau, S., Buchel, O., Cameron, C., Carvalho, C., Celadin, T., Cerami, C., Chalise, H., Cheng, X., Cockcroft, K., Conway, J., Córdoba-Delgado, M., Crespi, C., Crouzevialle, M., Cutler, J., Cypryańska, M., Dabrowska, J., Daniels, M., Davis, V., Dayley, P., Delouvée, S., Denkovski, O., Dezecache, G., Dhaliwal, N., Diato, A., Di Paolo, R., Drosinou, M., Dulleck, U., Ekmanis, J., Ertan, A., Farhana, H., Farkhari, F., Farmer, H., Fenwick, A., Fidanovski, K., Flew, T., Fraser, S., Frempong, R., Fugelsang, J., Gale, J., Garcia-Navarro, E., Garladinne, P., Ghajjou, O., Gkinopoulos, T., Gray, K., Griffin, S., Gronfeldt, B., Gümren, M., Gurung, R., Halperin, E., Harris, E., Herzon, V., Hruška, M., Huang, G., Hudecek, M., Isler, O., Jangard, S., Jorgensen, F., Kachanoff, F., Kahn, J., Dangol, A., Keudel, O., Koppel, L., Koverola, M., Kubin, E., Kunnari, A., Kutiyski, Y., Laguna, O., Leota, J., Lermer, E., Levy, J., Levy, N., Li, C., Long, E., Maglić, M., McCashin, D., Metcalf, A., Mikloušić, I., El Mimouni, S., Miura, A., Molina-Paredes, J., Monroy-Fonseca, C., Morales-Marente, E., Moreau, D., Muda, R., Myer, A., Nash, K., Nesh-Nash, T., Nitschke, J., Nurse, M., Ohtsubo, Y., de Mello, V., O’Madagain, C., Onderco, M., Palacios-Galvez, M., Palomöki, J., Pan, Y., Papp, Z., Pärnamets, P., Paruzel-Czachura, M., Pavlović, Z., Payán-Gómez, C., Perander, S., Pitman, M., Prasad, R., Pyrkosz-Pacyna, J., Rathje, S., Raza, A., Rhee, K., Robertson, C., Rodríguez-Pascual, I., Saikkonen, T., Salvador-Ginez, O., Santi, G., Santiago-Tovar, N., Savage, D., Scheffer, J., Schultner, D., Schutte, E., Scott, A., Sharma, M., Sharma, P., Skali, A., Stadelmann, D., Stafford, C., Stanojević, D., Stefaniak, A., Sternisko, A., Stoica, A., Stoyanova, K., Strickland, B., Sundvall, J., Thomas, J., Tinghög, G., Torgler, B., Traast, I., Tucciarelli, R., Tyrala, M., Ungson, N., Uysal, M., Van Lange, P., van Prooijen, J., van Rooy, D., Västfjäll, D., Verkoeijen, P., Vieira, J., von Sikorski, C., Walker, A., Watermeyer, J., Wetter, E., Whillans, A., White, K., Habib, R., Willardt, R., Wohl, M., Wójcik, A., Wu, K., Yamada, Y., Yilmaz, O., Yogeeswaran, K., Ziemer, C., Zwaan, R., Boggio, P. and Sampaio, W. 2023. Social and moral psychology of COVID-19 across 69 countries. Scientific Data. 10 (1), p. 272. https://doi.org/10.1038/s41597-023-02080-8
IHWC: intelligent hidden web crawler for harvesting data in urban domains
Kaur, S., Singh, A., Geetha, G. and Cheng, X. 2021. IHWC: intelligent hidden web crawler for harvesting data in urban domains. Complex & Intelligent Systems. https://doi.org/10.1007/s40747-022-00839-xA tamper-resistant broadcasting scheme for secure communication in internet of autonomous vehicles
Sun, J., Tao, J., Zhang, H., Zhao, Y., Nie, L., Cheng, X. and Zhang, T. 2023. A tamper-resistant broadcasting scheme for secure communication in internet of autonomous vehicles. IEEE Transactions on Intelligent Transportation Systems. 25 (3), pp. 2837-2846. https://doi.org/10.1109/TITS.2023.3265403Privacy-preserving and fine-grained data sharing for resource-constrained healthcare CPS devices
Bao, Y., Qiu, W. and Cheng, X. 2023. Privacy-preserving and fine-grained data sharing for resource-constrained healthcare CPS devices. Expert Systems. 40 (6). https://doi.org/10.1111/exsy.13220A novel framework for melanoma lesion segmentation using multiparallel depthwise separable and dilated convolutions with swish activations
Bukhari, M., Yasmin, S., Habib, A., Cheng, X., Ullah, F., Yoo, J. and Lee, D. 2023. A novel framework for melanoma lesion segmentation using multiparallel depthwise separable and dilated convolutions with swish activations. Journal of Healthcare Engineering. 2023 (1). https://doi.org/10.1155/2023/1847115Fake news stance detection using selective features and FakeNET
Aljrees, T., Cheng, X., Ahmed, M., Umer, M., Majeed, R., Alnowaiser, K., Abuzinadah, N. and Ashraf, I. 2023. Fake news stance detection using selective features and FakeNET. PLoS ONE. 18 (7). https://doi.org/10.1371/journal.pone.0287298PIGNUS: a deep learning model for IDS in industrial internet-of-things
Jayalaxmi, P., Saha, R., Kumar, G., Alazab, M., Conti, M. and Cheng, X. 2023. PIGNUS: a deep learning model for IDS in industrial internet-of-things. Computers and Security. 132. https://doi.org/10.1016/j.cose.2023.103315Generative recorrupted-to-recorrupted: an unsupervised image denoising network for arbitrary noise distribution
Liu, Y., Wan, B., Shi, D. and Cheng, X. 2023. Generative recorrupted-to-recorrupted: an unsupervised image denoising network for arbitrary noise distribution. Remote Sensing. 15 (2). https://doi.org/10.3390/rs15020364Cooperative conflict detection and resolution and safety assessment for 6G enabled unmanned aerial vehicles
Li, S., Cheng, X., Huang, X., Otaibi, S. and Wang, H. 2023. Cooperative conflict detection and resolution and safety assessment for 6G enabled unmanned aerial vehicles. IEEE Transactions on Intelligent Transportation Systems. 24 (2), pp. 2183-2198. https://doi.org/10.1109/TITS.2021.3137458An efficient quality of services based wireless sensor network for anomaly detection using soft computing approaches
Mittal, M., Kobielnik, M., Gupta, S., Cheng, X. and Wozniak, M. 2022. An efficient quality of services based wireless sensor network for anomaly detection using soft computing approaches. Journal of Cloud Computing. 11 (1), pp. 1-21. https://doi.org/10.1186/s13677-022-00344-zEfficient, revocable, and privacy-preserving fine-grained data sharing with keyword search for the cloud-assisted medical IoT system
Bao, Y., Qiu, W., Tang, P. and Cheng, X. 2022. Efficient, revocable, and privacy-preserving fine-grained data sharing with keyword search for the cloud-assisted medical IoT system. IEEE Journal of Biomedical and Health Informatics. 26 (5), pp. 2041-2051. https://doi.org/10.1109/JBHI.2021.3100871Large-size data distribution in IoV based on 5G/6G compatible heterogeneous network
Yin, X., Liu, J., Cheng, X. and Xiong, X. 2022. Large-size data distribution in IoV based on 5G/6G compatible heterogeneous network. IEEE Transactions on Intelligent Transportation Systems. 25 (7), pp. 9840-9852. https://doi.org/10.1109/TITS.2021.3118701A differentiated learning environment in domain model for learning disabled learners
Thapliyal, M., Ahuja, N., Shankar, A., Cheng, X. and Kumar, M. 2022. A differentiated learning environment in domain model for learning disabled learners. Journal of Computing in Higher Education. 34, pp. 60-82. https://doi.org/10.1007/s12528-021-09278-yOffline signature verification using deep neural network with application to computer vision
Sharma, N., Gupta, S., Mehta, P., Cheng, X., Shankar, A., Singh, P. and Nayak, S. 2022. Offline signature verification using deep neural network with application to computer vision. Journal of Electronic Imaging (JEI). 31 (4). https://doi.org/10.1117/1.JEI.31.4.041210Intrusion detection and prevention system for an IoT environment
Kumar, A., Abhishek, K., Ghalib, M., Shankar, A. and Cheng, X. 2022. Intrusion detection and prevention system for an IoT environment. Digital Communications and Networks. 8 (4), pp. 540-551. https://doi.org/10.1016/j.dcan.2022.05.027An empirical study on Retinex methods for low-light image enhancement
Rasheed, M., Guo, G., Shi, D., Khan, H. and Cheng, X. 2022. An empirical study on Retinex methods for low-light image enhancement. Remote Sensing. 14 (18). https://doi.org/10.3390/rs14184608A tagging SNP set method based on network community partition of linkage disequilibrium and node centrality
Wan, Q., Cheng, X., Zhang, Y., Lu, G., Wang, S. and He, S. 2022. A tagging SNP set method based on network community partition of linkage disequilibrium and node centrality. Current Bioinformatics. 17 (9), pp. 825-834. https://doi.org/10.2174/1574893617666220324155813Cyber-threat detection system using a hybrid approach of transfer learning and multi-model image representation
Ullah, F., Ullah, S., Naeem, M., Mostarda, L., Rho, S. and Cheng, X. 2022. Cyber-threat detection system using a hybrid approach of transfer learning and multi-model image representation. Sensors. 22 (15), pp. 1-26. https://doi.org/10.3390/s22155883A deep convolutional neural network stacked ensemble for malware threat classification in internet of things
Naeem, H., Cheng, X., Ullah, F., Jabbar, S. and Dong, S. 2022. A deep convolutional neural network stacked ensemble for malware threat classification in internet of things. Journal of Circuits, Systems and Computers. 31 (17). https://doi.org/10.1142/s0218126622503029RFAP: a revocable fine-grained access control mechanism for autonomous vehicle platoon
Zhao, Y., Wang, Y., Cheng, X., Chen, H., Yu, H. and Ren, Y. 2022. RFAP: a revocable fine-grained access control mechanism for autonomous vehicle platoon. IEEE Transactions on Intelligent Transportation Systems. 23 (7), pp. 9668-9679. https://doi.org/10.1109/TITS.2021.3105458Predicting attitudinal and behavioral responses to COVID-19 pandemic using machine learning
Pavlović, T., Azevedo, F., De, K., Riaño-Moreno, J., Maglić, M., Gkinopoulos, T., Donnelly-Kehoe, P., Payán-Gómez, C., Huang, G., Kantorowicz, J., Birtel, M., Schönegger, P., Capraro, V., Santamaría-García, H., Yucel, M., Ibanez, A., Rathje, S., Wetter, E., Stanojević, D., van Prooijen, J., Hesse, E., Elbaek, C., Franc, R., Pavlović, Z., Mitkidis, P., Cichocka, A., Gelfand, M., Alfano, M., Ross, R., Sjåstad, H., Nezlek, J., Cislak, A., Lockwood, P., Abts, K., Agadullina, E., Amodio, D., Apps, M., Aruta, J., Besharati, S., Bor, A., Choma, B., Cunningham, W., Ejaz, W., Farmer, H., Findor, A., Gjoneska, B., Gualda, E., Huynh, T., Imran, M., Israelashvili, J., Kantorowicz-Reznichenko, E., Krouwel, A., Kutiyski, Y., Laakasuo, M., Lamm, C., Levy, J., Leygue, C., Lin, M., Mansoor, M., Marie, A., Mayiwar, L., Mazepus, H., McHugh, C., Olsson, A., Otterbring, T., Packer, D., Palomäki, J., Perry, A., Petersen, M., Puthillam, A., Rothmund, T., Schmid, P., Stadelmann, D., Stoica, A., Stoyanov, D., Stoyanova, K., Tewari, S., Todosijević, B., Torgler, B., Tsakiris, M., Tung, H., Umbreș, R., Vanags, E., Vlasceanu, M., Vonasch, A., Zhang, Y., Abad, M., Adler, E., Mdarhri, H., Antazo, B., Ay, F., Ba, M., Barbosa, S., Bastian, B., Berg, A., Białek, M., Bilancini, E., Bogatyreva, N., Boncinelli, L., Booth, J., Borau, S., Buchel, O., de Carvalho, C., Celadin, T., Cerami, C., Chalise, H., Cheng, X., Cian, L., Cockcroft, K., Conway, J., Córdoba-Delgado, M., Crespi, C., Crouzevialle, M., Cutler, J., Cypryańska, M., Dabrowska, J., Davis, V., Minda, J., Dayley, P., Delouvée, S., Denkovski, O., Dezecache, G., Dhaliwal, N., Diato, A., Di Paolo, R., Dulleck, U., Ekmanis, J., Etienne, T., Farhana, H., Farkhari, F., Fidanovski, K., Flew, T., Fraser, S., Frempong, R., Fugelsang, J., Gale, J., García-Navarro, E., Garladinne, P., Gray, K., Griffin, S., Gronfeldt, B., Gruber, J., Halperin, E., Herzon, V., Hruška, M., Hudecek, M., Isler, O., Jangard, S., Jørgensen, F., Keudel, O., Koppel, L., Koverola, M., Kunnari, A., Leota, J., Lermer, E., Li, C., Longoni, C., McCashin, D., Mikloušić, I., Molina-Paredes, J., Monroy-Fonseca, C., Morales-Marente, E., Moreau, D., Muda, R., Myer, A., Nash, K., Nitschke, J., Nurse, M., de Mello, V., Palacios-Galvez, M., Pan, Y., Papp, Z., Pärnamets, P., Paruzel-Czachura, M., Perander, S., Pitman, M., Raza, A., Rêgo, G., Robertson, C., Rodríguez-Pascual, I., Saikkonen, T., Salvador-Ginez, O., Sampaio, W., Santi, G., Schultner, D., Schutte, E., Scott, A., Skali, A., Stefaniak, A., Sternisko, A., Strickland, B., Thomas, J., Tinghög, G., Traast, I., Tucciarelli, R., Tyrala, M., Ungson, N., Uysal, M., Van Rooy, D., Västfjäll, D., Vieira, J., von Sikorski, C., Walker, A., Watermeyer, J., Willardt, R., Wohl, M., Wójcik, A., Wu, K., Yamada, Y., Yilmaz, O., Yogeeswaran, K., Ziemer, C., Zwaan, R., Boggio, P., Whillans, A., Van Lange, P., Prasad, R., Onderco, M., O'Madagain, C., Nesh-Nash, T., Laguna, O., Kubin, E., Gümren, M., Fenwick, A., Ertan, A., Bernstein, M., Amara, H. and Van Bavel, J. 2022. Predicting attitudinal and behavioral responses to COVID-19 pandemic using machine learning. PNAS Nexus. 1 (3), pp. 1-15. https://doi.org/10.1093/pnasnexus/pgac093Adaptive weighted dynamic differential evolution algorithm for emergency material allocation and scheduling
Wang, T., Wu, K., Du, T. and Cheng, X. 2022. Adaptive weighted dynamic differential evolution algorithm for emergency material allocation and scheduling. Computational Intelligence. 38 (3), pp. 714-730. https://doi.org/10.1111/coin.12389National identity predicts public health support during a global pandemic
Van Bavel, J., Cichocka, A., Capraro, V., Sjåstad, H., Nezlek, J., Pavlović, T., Alfano, M., Gelfand, M., Azevedo, F., Birtel, M., Cislak, A., Lockwood, P., Ross, R., Abts, K., Agadullina, E., Aruta, J., Besharati, S., Bor, A., Choma, B., Crabtree, C., Cunningham, W., De, K., Ejaz, W., Elbaek, C., Findor, A., Flichtentrei, D., Franc, R., Gjoneska, B., Gruber, J., Gualda, E., Horiuchi, Y., Huynh, T., Ibanez, A., Imran, M., Israelashvili, J., Jasko, K., Kantorowicz, J., Kantorowicz-Reznichenko, E., Krouwel, A., Laakasuo, M., Lamm, C., Leygue, C., Lin, M., Mansoor, M., Marie, A., Mayiwar, L., Mazepus, H., McHugh, C., Minda, J., Mitkidis, P., Olsson, A., Otterbring, T., Packer, D., Perry, A., Petersen, M., Puthillam, A., Riaño-Moreno, J., Rothmund, T., Santamaría-García, H., Schmid, P., Stoyanov, D., Tewari, S., Todosijević, B., Tsakiris, M., Tung, H., Umbreș, R., Vanags, E., Vlasceanu, M., Vonasch, A., Yucel, M., Zhang, Y., Abad, M., Adler, E., Akrawi, N., Mdarhri, H., Amara, H., Amodio, D., Antazo, B., Apps, M., Ay, F., Ba, M., Barbosa, S., Bastian, B., Berg, A., Bernal-Zárate, M., Bernstein, M., Białek, M., Bilancini, E., Bogatyreva, N., Boncinelli, L., Booth, J., Borau, S., Buchel, O., Cameron, C., Carvalho, C., Celadin, T., Cerami, C., Chalise, H., Cheng, X., Cian, L., Cockcroft, K., Conway, J., Córdoba-Delgado, M., Crespi, C., Crouzevialle, M., Cutler, J., Cypryańska, M., Dabrowska, J., Daniels, M., Davis, V., Dayley, P., Delouvee, S., Denkovski, O., Dezecache, G., Dhaliwal, N., Diato, A., Di Paolo, R., Drosinou, M., Dulleck, U., Ekmanis, J., Ertan, A., Etienne, T., Farhana, H., Farkhari, F., Farmer, H., Fenwick, A., Fidanovski, K., Flew, T., Fraser, S., Frempong, R., Fugelsang, J., Gale, J., Garcia-Navarro, E., Garladinne, P., Ghajjou, O., Gkinopoulos, T., Gray, K., Griffin, S., Gronfeldt, B., Gümren, M., Gurung, R., Halperin, E., Harris, E., Herzon, V., Hruška, M., Huang, G., Hudecek, M., Isler, O., Jangard, S., Jørgensen, F., Kachanoff, F., Kahn, J., Dangol, A., Keudel, O., Koppel, L., Koverola, M., Kubin, E., Kunnari, A., Kutiyski, Y., Laguna, O., Leota, J., Lermer, E., Levy, J., Levy, N., Li, C., Long, E., Longoni, C., Maglić, M., McCashin, D., Metcalf, A., Mikloušić, I., El Mimouni, S., Miura, A., Molina-Paredes, J., Monroy-Fonseca, C., Morales-Marente, E., Moreau, D., Muda, R., Myer, A., Nash, K., Nesh-Nash, T., Nitschke, J., Nurse, M., Ohtsubo, Y., Oldemburgo de Mello, V., O’Madagain, C., Onderco, M., Palacios-Galvez, M., Palomäki, J., Pan, Y., Papp, Z., Pärnamets, P., Paruzel-Czachura, M., Pavlović, Z., Payán-Gómez, C., Perander, S., Pitman, M., Prasad, R., Pyrkosz-Pacyna, J., Rathje, S., Raza, A., Rêgo, G., Rhee, K., Robertson, C., Rodríguez-Pascual, I., Saikkonen, T., Salvador-Ginez, O., Sampaio, W., Santi, G., Santiago-Tovar, N., Savage, D., Scheffer, J., Schönegger, P., Schultner, D., Schutte, E., Scott, A., Sharma, M., Sharma, P., Skali, A., Stadelmann, D., Stafford, C., Stanojević, D., Stefaniak, A., Sternisko, A., Stoica, A., Stoyanova, K., Strickland, B., Sundvall, J., Thomas, J., Tinghög, G., Torgler, B., Traast, I., Tucciarelli, R., Tyrala, M., Ungson, N., Uysal, M., Van Lange, P., van Prooijen, J., van Rooy, D., Västfjäll, D., Verkoeijen, P., Vieira, J., von Sikorski, C., Walker, A., Watermeyer, J., Wetter, E., Whillans, A., Willardt, R., Wohl, M., Wójcik, A., Wu, K., Yamada, Y., Yilmaz, O., Yogeeswaran, K., Ziemer, C., Zwaan, R. and Boggio, P. 2022. National identity predicts public health support during a global pandemic. Nature Communications. 13 (1), pp. 1-14. https://doi.org/10.1038/s41467-021-27668-9CroLSSim: Cross‐language software similarity detector using hybrid approach of LSA‐based AST‐MDrep features and CNN‐LSTM model
Ullah, F., Naeem, M., Naeem, H., Cheng, X. and Alazab, M. 2022. CroLSSim: Cross‐language software similarity detector using hybrid approach of LSA‐based AST‐MDrep features and CNN‐LSTM model. International Journal of Intelligent Systems. 37 (9), pp. 5768-5795. https://doi.org/10.1002/int.22813MobiScan: an enhanced invisible screen‐camera communication system for IoT applications
Zhang, X., Liu, J., Ba, Z., Tao, Y. and Cheng, X. 2022. MobiScan: an enhanced invisible screen‐camera communication system for IoT applications. Transactions on Emerging Telecommunications Technologies. 33 (4). https://doi.org/10.1002/ett.4151Secure smart contracts for cloud-based manufacturing using Ethereum blockchain
Kumar, A., Abhishek, K., Nerurkar, P., Ghalib, M., Shankar, A. and Cheng, X. 2022. Secure smart contracts for cloud-based manufacturing using Ethereum blockchain. Transactions on Emerging Telecommunications Technologies. 33 (4). https://doi.org/10.1002/ett.4129Editorial: Security of cloud service for the manufacturing industry
Cheng, X., Liu, Z. and Ning, Y. 2022. Editorial: Security of cloud service for the manufacturing industry. Transactions on Emerging Telecommunications Technologies. 33 (4). https://doi.org/10.1002/ett.4369Power grid-oriented cascading failure vulnerability identifying method based on wireless sensors
Li, S., Chen, Y., Wu, X., Cheng, X. and Tian, Z. 2021. Power grid-oriented cascading failure vulnerability identifying method based on wireless sensors. Journal of Sensors. 2021, pp. 1-12. https://doi.org/10.1155/2021/8820413Secure and energy-efficient smart building architecture with emerging technology IoT
Kumar, A., Sharma, S., Goyal, N., Singh, A., Cheng, X. and Singh, P. 2021. Secure and energy-efficient smart building architecture with emerging technology IoT. Computer Communications. 176, pp. 207-217. https://doi.org/10.1016/j.comcom.2021.06.003Fuzzy decision trees embedded with evolutionary fuzzy clustering for locating users using wireless signal strength in an indoor environment
Narayanan, S., Baby, C., Perumal, B., Bhatt, R., Cheng, X., Ghalib, M. and Shankar, A. 2021. Fuzzy decision trees embedded with evolutionary fuzzy clustering for locating users using wireless signal strength in an indoor environment. International Journal of Intelligent Systems. 36 (8), pp. 4280-4267. https://doi.org/10.1002/int.22459PSSPNN: PatchShuffle Stochastic Pooling Neural Network for an explainable diagnosis of COVID-19 with multiple-way data augmentation
Wang, S., Zhang, Y., Cheng, X., Zhang, X. and Zhang, Y. 2021. PSSPNN: PatchShuffle Stochastic Pooling Neural Network for an explainable diagnosis of COVID-19 with multiple-way data augmentation. Computational and Mathematical Methods in Medicine. 2021, pp. 1-18. https://doi.org/10.1155/2021/6633755Task bundling in worker-centric mobile crowdsensing
Zhao, T., Yang, Y., Wang, E., Mumtaz, S. and Cheng, X. 2021. Task bundling in worker-centric mobile crowdsensing. International Journal of Intelligent Systems. 36 (9), pp. 4936-4961. https://doi.org/10.1002/int.22497ShadowFPE: new encrypted web application solution based on shadow DOM
Guo, X., Huang, Y., Ye, J., Yin, S., Li, M., Li, Z., Yiu, S. and Cheng, X. 2021. ShadowFPE: new encrypted web application solution based on shadow DOM. Mobile Networks and Applications. 26 (4), pp. 1733-1746. https://doi.org/10.1007/s11036-019-01509-yLearning context-aware outfit recommendation
Abugabah, A., Cheng, X. and Wang, J. 2020. Learning context-aware outfit recommendation. Symmetry. 12 (6), pp. 1-13. https://doi.org/10.3390/sym12060873