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International CARDS Journal

Volume 2, Issue 8 2021 Q2



Digital forensics and machine learning



Frameworks may support preparedness and procedure in digital forensics investigations (Casey & Souvignet, 2020).  Casey and Souvignet (2020) discuss the importance of preparedness in risk management for digital forensics to identify malicious activity on computer systems.  Fullár, Kutnyánszky, and Leiner (2020) advance digital forensics in crime scene investigation by exploring additional areas for which to apply DNA residue testing.  Digital forensics may also improve the procedures of a digital forensics investigation (Ieong, 2006).  Ieong (2006) develop a digital forensic framework that incorporates legal presentation, jurisdiction, and procedures.

Image analysis may present obstacles in forensic medicine because of lack of adequate datasets for referencing decayed organs (Peña-Solórzano, Albrecht, Bassed, Burke, & Dimmock, 2020).  Peña-Solórzano et al. (2020) suggest that machine learning can address some of the challenges of image analysis in forensic medicine.  Machine learning may also support other areas of a digital forensics investigation such as the analysis of video, audio, or textual data (Saha, Rathee, & Koshiba, 2020).

The complexity of cloud computing can add to the preparation necessary for digital forensics applications (Irfan, Abbas, Sun, Sajid, & Pasha, 2016).  Irfan et al. (2016) develop a digital forensic framework that combines cloud computing with security incident event management.  Ko, Chambers, and Barrett (2020) propose tools for mitigating distributed denial of service attacks by installing mechanisms in attack paths.

 

Casey, E., & Souvignet, T. R. (2020). Digital transformation risk management in forensic science laboratories. Forensic Science International, 316, 110486. doi:https://doi.org/10.1016/j.forsciint.2020.110486

Fullár, A., Kutnyánszky, V., & Leiner, N. (2020). Identification of burglars using foil impressioning based on tool marks and DNA evidence. Forensic Science International, 316, 110524. doi:https://doi.org/10.1016/j.forsciint.2020.110524

Ieong, R. S. C. (2006). FORZA – Digital forensics investigation framework that incorporate legal issues. Digital Investigation, 3, 29-36. doi:https://doi.org/10.1016/j.diin.2006.06.004

Irfan, M., Abbas, H., Sun, Y., Sajid, A., & Pasha, M. (2016). A framework for cloud forensics evidence collection and analysis using security information and event management. Security and Communication Networks, 9(16), 3790-3807. doi:https://doi.org/10.1002/sec.1538

Ko, I., Chambers, D., & Barrett, E. (2020). Adaptable feature-selecting and threshold-moving complete autoencoder for DDoS flood attack mitigation. Journal of Information Security and Applications, 55, 102647. doi:https://doi.org/10.1016/j.jisa.2020.102647

Peña-Solórzano, C. A., Albrecht, D. W., Bassed, R. B., Burke, M. D., & Dimmock, M. R. (2020). Findings from machine learning in clinical medical imaging applications – Lessons for translation to the forensic setting. Forensic Science International, 316, 110538. doi:https://doi.org/10.1016/j.forsciint.2020.110538

Saha, T. K., Rathee, D., & Koshiba, T. (2020). Efficient protocols for private wildcards pattern matching. Journal of Information Security and Applications, 55, 102609. doi:https://doi.org/10.1016/j.jisa.2020.102609



Improvements to machine learning algorithms and their potential to improve health care



Improvements to machine learning algorithms may improve the capabilities and accuracy of artificial intelligence (Wang, Qian, & Li, 2020).  Wang et al. (2020) develop a technique to increase the accuracy of machine learning algorithms based on consistency.  Villar, Merino, Posadas, Henia, and Rioux (2020) explain how model driven engineering can navigate the complexity of cyber physical systems.  Lyu, Needell, and Balzano (2020) introduce a framework for improving network analysis with Monte Carlo Markov Chain algorithms.

Machine learning may also improve diagnosis capabilities in radiology for novel coronavirus detection (Garg, Garg, Mahela, & Garg, 2020).  Garg et al. (2020) explain how medical image analysis and convolutional neural networks can assist in diagnosing the novel coronavirus.  Narayanan, Hardie, Krishnaraja, Karam, and Davuluru (2020) also explore machine learning in radiology in novel coronavirus detection.  Rigby (2019) expects artificial intelligence to revolutionize the field of medicine.

 

 

Garg, T., Garg, M., Mahela, O. P., &Garg, A. R. (2020). Convolutional neural networks with transfer learning for recognition of COVID-19: A comparative study of different approaches. AI, 1(4). doi:10.3390/ai1040034

Lyu, H., Needell, D., & Balzano, L. (2020). Online matrix factorization for Markovian data and applications to Network Dictionary Learning. Journal of Machine Learning Research, 21, 1-49.

Narayanan, B. N., Hardie, R. C., Krishnaraja, V., Karam, C., & Davuluru, V. S. (2020). Transfer-to-transfer learning approach for computer aided detection of COVID-19 in chest radiographs. AI, 1(4). doi:10.3390/ai1040032

Rigby, M. J. (2019). Ethical dimensions of using artificial intelligence in health care. AMA Journal of Ethics, 21(2).

Villar, E., Merino, J., Posadas, H., Henia, R., & Rioux, L. (2020). Mega-modeling of complex, distributed, heterogeneous CPS systems. Microprocessors and Microsystems, 78, 103244. doi:https://doi.org/10.1016/j.micpro.2020.103244

Wang, J., Qian, Y., & Li, F. (2020). Learning with mitigating random consistency from the accuracy measure. Machine Learning, 109(12), 2247-2281. doi:10.1007/s10994-020-05914-3



Applications of artificial intelligence



With the increase to the capabilities of artificial intelligence, emerging applications may support new initiatives for technology (Gebru et al., 2017).  Gebru et al. (2017) apply convolutional neural networks in the prediction of demographic data based on image analysis of automobile images.  Ramenahalli (2020) describe combining audio and visual sensory data with artificial intelligence for more efficient scene recognition.  Tuggener et al. (2020) evaluate deep learning models in automated audio processing.

Artificial intelligence may implement machine learning to complete tasks with greater efficiency (Bard et al., 2020).  Bard et al. (2020) describe the increase in capabilities of artificial intelligence in game playing performance.  Han, Zhu, and Ji (2019) develop a three dimensional model for studying the airflow and heat transfer compensation for turbulence in refrigerated transport vehicles.  Yazdanian, West, and Dillenbourg (2020) provide an analysis of the platforms available in the software engineering domain.

 

 

 

Bard, N., Foerster, J. N., Chandar, S., Burch, N., Lanctot, M., Song, H. F., . . . Bowling, M. (2020). The Hanabi challenge: A new frontier for AI research. Artificial Intelligence, 280, 103216. doi:https://doi.org/10.1016/j.artint.2019.103216

Gebru, T., Krause, J., Wang, Y., Chen, D., Deng, J., Aiden, E. L., & Fei-Fei, L. (2017). Using deep learning and Google Street View to estimate the demographic makeup of neighborhoods across the United States. Proceedings of the National Academy of Sciences, 114(50), 13108. doi:10.1073/pnas.1700035114

Han, J.-W., Zhu, W.-Y., & Ji, Z.-T. (2019). Comparison of veracity and application of different CFD turbulence models for refrigerated transport. Artificial Intelligence in Agriculture, 3, 11-17. doi:https://doi.org/10.1016/j.aiia.2019.10.001

Ramenahalli, S. (2020). A Biologically Motivated, Proto-Object-Based Audiovisual Saliency Model. AI, 1(4), 487-509.

Tuggener, L., Amirian, M., Benites, F., von Däniken, P., Gupta, P., Schilling, F.-P., & Stadelmann, T. (2020). Design Patterns for Resource-Constrained Automated Deep-Learning Methods. AI, 1(4), 510-538.

Yazdanian, R., West, R., & Dillenbourg, P. (2020). Keeping Up with the Trends: Analyzing the Dynamics of Online Learning and Hiring Platforms in the Software Programming Domain. International Journal of Artificial Intelligence in Education. doi:10.1007/s40593-020-00231-1



Big data analytics and natural language processing



Techniques in big data analytics may improve natural language processing capabilities (Pääkkönen & Pakkala, 2015).  Pääkkönen and Pakkala (2015) design and develop a big data reference architecture that consists of data components, stores, and flows.  Hillebrand, Biesner, Bauckhage, and Sifa (2021) develop an algorithm for topic clustering that can maintain more thematic granularity than simple clustering.  Berger, Chiodini, and Zenga (2020) implement Monte Carlo simulations to validate a method of sampling by empirical bounds.

Emerging techniques may extend improvements to natural language processing engines (Ucak, Kang, Ko, & Lee, 2021).  Ucak et al. (2021) demonstrate how natural language processing can assist with classification of elements in chemistry.  Elghadyry, Ouardi, and Verel (2021)suggest that intermediate languages can facilitate natural language processing engines to translate between two different languages.

Bender, Gebru, and McMillan-Major (2021) describe the environmental risks of computing technology deployed to develop large language models for artificial intelligence.  Bender et al. (2021) refer to the lack of renewable energy sources that cloud computing companies depend on to store and process large datasets.  Lettiah (2021) explains the importance of preserving indigenous languages and semantic terms.  Chrisomalis (2021) describes the effect that language can have on cognitive though and social structure.

Heterogeneous data sources may present challenges for natural language processing engines (Migliorini, Belussi, Quintarelli, & Carra, 2021).  Migliorini et al. (2021) propose partitioning techniques to address challenges for heterogeneous data sources.  Padillo, Luna, and Ventura (2019) explain how a combination of association rule mining and classification can perform associative classification.

 

 

Bender, E., Gebru, T., & McMillan-Major, A. (2021). On the dangers of stochastic parrots: Can language models be too big. Proceedings of FAccT.

Berger, Y. G., Chiodini, P. M., & Zenga, M. (2020). Bounds for monetary-unit sampling in auditing: An adjusted empirical likelihood approach. Statistical Papers. doi:10.1007/s00362-020-01209-w

Chrisomalis, S. (2021). The scope of linguistic relativity in graphic and lexical numeration. Language & Communication, 76, 1-12. doi:https://doi.org/10.1016/j.langcom.2020.09.006

Elghadyry, B., Ouardi, F., & Verel, S. (2021). Composition of weighted finite transducers in MapReduce. Journal of Big Data, 8(1), 22. doi:10.1186/s40537-020-00397-4

Hillebrand, L., Biesner, D., Bauckhage, C., & Sifa, R. (2021). Interpretable topic extraction and word embedding learning using non-negative tensor DEDICOM. Machine Learning and Knowledge Extraction, 3(1), 123-167.

Lettiah, G. (2021). Countering the cumbersome: Rethinking the Shona compounding term-creation strategy. Language & Communication, 76, 13-22. doi:https://doi.org/10.1016/j.langcom.2020.09.008

Migliorini, S., Belussi, A., Quintarelli, E., & Carra, D. (2021). CoPart: a context-based partitioning technique for big data. Journal of Big Data, 8(1), 21. doi:10.1186/s40537-021-00410-4

Pääkkönen, P., & Pakkala, D. (2015). Reference architecture and classification of technologies, products and services for big data systems. Big Data Research, 2(4), 166-186. doi:https://doi.org/10.1016/j.bdr.2015.01.001

Padillo, F., Luna, J. M., & Ventura, S. (2019). Evaluating associative classification algorithms for Big Data. Big Data Analytics, 4(1), 2. doi:10.1186/s41044-018-0039-7

Ucak, U. V., Kang, T., Ko, J., & Lee, J. (2021). Substructure-based neural machine translation for retrosynthetic prediction. Journal of Cheminformatics, 13(1), 4. doi:10.1186/s13321-020-00482-z