Artificial Intelligence is proving its prominence in every industry out there and the healthcare industry is no different. From patient care to Administrative processes AI has huge potential in the healthcare industry. There are many research studies suggesting that AI can perform as well as or better than humans at key healthcare tasks. We have seen robots performing surgeries or assisting doctors with more precision and flexibility. Algorithms are outperforming radiologists in detecting dangerous tumors and advising researchers on how to build cohorts for expensive clinical trials.

New building regulations aimed at improving energy efficiency are set to increase the price of new homes, as well as those of extensions and loft conversions on existing ones. The rules, which came into effect on Wednesday in England, are part of government plans to reduce the UK's carbon emissions to net zero by 2050. They set new standards for ventilation, energy efficiency and heating, and state that new residential buildings must have charging points for electric vehicles. The moves are the most significant change to building regulations in years, and industry experts say they will inevitably lead to higher prices at a time when a shortage of materials and high labour costs is already driving up bills. Brian Berry, chief executive of the Federation of Master Builders, a trade group for small and medium-sized builders, says the measures will require new materials, testing methods, products and systems to be installed.

We model the problem of optimizing the schedule of courses a student at the American College of Greece will need to take to complete their studies. We model all constraints set forth by the institution and the department, so that we guarantee the validity of all produced schedules. We formulate several different objectives to optimize in the resulting schedule, including fastest completion time, course difficulty balance, and so on, with a very important objective our model is capable of capturing being the maximization of the expected student GPA given their performance on passed courses using Machine Learning and Data Mining techniques. All resulting problems are Mixed Integer Linear Programming problems with a number of binary variables that is in the order of the maximum number of terms times the number of courses available for the student to take. The resulting Mathematical Programming problem is always solvable by the GUROBI solver in less than 10 seconds on a modern commercial off-the-self PC, whereas the manual process that was installed before used to take department heads that are designated as student advisors more than one hour of their time for every student and was resulting in sub-optimal schedules as measured by the objectives set forth.

Rule sets are highly interpretable logical models in which the predicates for decision are expressed in disjunctive normal form (DNF, OR-of-ANDs), or, equivalently, the overall model comprises an unordered collection of if-then decision rules. In this paper, we consider a submodular optimization based approach for learning rule sets. The learning problem is framed as a subset selection task in which a subset of all possible rules needs to be selected to form an accurate and interpretable rule set. We employ an objective function that exhibits submodularity and thus is amenable to submodular optimization techniques. To overcome the difficulty arose from dealing with the exponential-sized ground set of rules, the subproblem of searching a rule is casted as another subset selection task that asks for a subset of features. We show it is possible to write the induced objective function for the subproblem as a difference of two submodular (DS) functions to make it approximately solvable by DS optimization algorithms. Overall, the proposed approach is simple, scalable, and likely to be benefited from further research on submodular optimization. Experiments on real datasets demonstrate the effectiveness of our method.

Black-box nature of Artificial Intelligence (AI) models do not allow users to comprehend and sometimes trust the output created by such model. In AI applications, where not only the results but also the decision paths to the results are critical, such black-box AI models are not sufficient. Explainable Artificial Intelligence (XAI) addresses this problem and defines a set of AI models that are interpretable by the users. Recently, several number of XAI models have been to address the issues surrounding by lack of interpretability and explainability of black-box models in various application areas such as healthcare, military, energy, financial and industrial domains. Although the concept of XAI has gained great deal of attention recently, its integration into the IoT domain has not yet been fully defined. In this paper, we provide an in-depth and systematic review of recent studies using XAI models in the scope of IoT domain. We categorize the studies according to their methodology and applications areas. In addition, we aim to focus on the challenging problems and open issues and give future directions to guide the developers and researchers for prospective future investigations.

Background. In recent years, libraries and archives led important digitisation campaigns that opened the access to vast collections of historical documents. While such documents are often available as XML ALTO documents, they lack information about their logical structure. In this paper, we address the problem of Logical Layout Analysis applied to historical documents in French. We propose a rule-based method, that we evaluate and compare with two Machine-Learning models, namely RIPPER and Gradient Boosting. Our data set contains French newspapers, periodicals and magazines, published in the first half of the twentieth century in the Franche-Comt\'e Region. Results. Our rule-based system outperforms the two other models in nearly all evaluations. It has especially better Recall results, indicating that our system covers more types of every logical label than the other two models. When comparing RIPPER with Gradient Boosting, we can observe that Gradient Boosting has better Precision scores but RIPPER has better Recall scores. Conclusions. The evaluation shows that our system outperforms the two Machine Learning models, and provides significantly higher Recall. It also confirms that our system can be used to produce annotated data sets that are large enough to envisage Machine Learning or Deep Learning approaches for the task of Logical Layout Analysis. Combining rules and Machine Learning models into hybrid systems could potentially provide even better performances. Furthermore, as the layout in historical documents evolves rapidly, one possible solution to overcome this problem would be to apply Rule Learning algorithms to bootstrap rule sets adapted to different publication periods.

Predicting the future is a possibility. So when working with AI, we should be aware of these distributions. That is why I think we need multiple AI for different parts of the market and then do an explainable rule-base module for decision making. As a human, our decision-making module should be dynamic and try to maximize profitability based on changing strategies. Here, AI can help clarify the details behind the scenes, which a human can not do most of the time rapidly.

Natural Language Processing (NLP) has long played a significant role in the compliance processes for major banks around the world. By implementing the different NLP techniques into the production processes, compliance departments can maintain detailed checks and keep up with regulator demands. All of these areas can benefit from document processing and the use of NLP techniques to get through the process more effectively. Certain verification tasks fall beyond the realm of using traditional, rules-based NLP systems. This is where deep learning can help fill these gaps, providing smoother and more efficient compliance checks. There are several challenges that make the rules-based system more complicated to use when undergoing check routines.

The automatic assignment of species information to the corresponding genes in a research article is a critically important step in the gene normalization task, whereby a gene mention is normalized and linked to a database record or identifier by a text-mining algorithm. Existing methods typically rely on heuristic rules based on gene and species co-occurrence in the article, but their accuracy is suboptimal. We therefore developed a high-performance method, using a novel deep learning-based framework, to classify whether there is a relation between a gene and a species. Instead of the traditional binary classification framework in which all possible pairs of genes and species in the same article are evaluated, we treat the problem as a sequence-labeling task such that only a fraction of the pairs needs to be considered. Our benchmarking results show that our approach obtains significantly higher performance compared to that of the rule-based baseline method for the species assignment task (from 65.8% to 81.3% in accuracy).

Prime Minister Fumio Kishida and Indonesian President Joko "Jokowi" Widodo on Friday confirmed they will strengthen cooperation toward realizing a "Free and Open Indo-Pacific" amid China's growing assertiveness in the region and Russia's invasion of Ukraine. Widodo said Indonesia and the Association of Southeast Asian Nations stand ready to build cooperation with their partners, and Kishida, who is on the first stop of his trip to Southeast Asia and Europe, underscored the importance of upholding the rules-based international order. "We are facing many challenges, including the situations in Ukraine, the East and South China seas and North Korea, and maintaining and strengthening the rules-based, free and open international order has become more important," Kishida said during a joint news conference after the summit. Kishida said that based on such understanding, the two sides confirmed they will strengthen cooperation toward realizing a Free and Open Indo-Pacific, an initiative that Japan has been pushing, and the ASEAN Outlook spearheaded by Indonesia. Japan views Indonesia, this year's host of the Group of 20 summit to be held in November and a key economy in Southeast Asia, as a strategic partner sharing universal values such as democracy and the rule of law.