AI Programs Research Articles

An integrated artificial intelligence framework for knowledge production and B2B marketing rational analysis for enhancing business performance

Artificial intelligence is a software-based program that helps companies to enhance their productivity of the companies. Therefore gaining more marketing knowledge and other organizational knowledge is very important for the development of the performance of business companies. Thus, the usage of artificial intelligence in several sectors of business companies is increasing day by day. Thereafter, the infrastructure of artificial intelligence is expensive but the usage of artificial intelligence can enhance the level of the performance of business organizations. Apart from that, the usage of artificial intelligence helps to enhance the knowledge production of companies. Along with that, the usage of AI helps the companies to gain knowledge about the B2B marketing process which helps the companies to enhance their goods and products. In addition to that, by using the facilities of AI programs the companies can gain knowledge about the demands and needs of the market easily.Therefore, the companies can improve their products according to the demands of the markets. Thus, improving the services according to the demands and needs of the market helps the companies to enhance the number of the customer. Therefore, enhancing the number of the customer leads to the enhancement of the sales rate of the companies. Furthermore, the purpose of this particular research study is to analyze the artificial intelligence framework and its impact on the business performance of the companies. The usage of AI impacts not only the international companies but also the companies of domestic markets are impacted by the software program. In addition to that, the researcher has used the secondary methods for gathering genuine, objective and accurate data about the research topic. Apart from that, the researcher has used the qualitative methods for analyzing all the collected data in this research study.

The benefits and dangers of using artificial intelligence in petrophysics

Artificial Intelligence, or AI, is a method of data analysis that learns from data, identify patterns and makes predictions with the minimal human intervention. AI is bringing many benefits to petrophysical evaluation. Using case studies, this paper describes several successful applications. The future of AI has even more potential. However, if used carelessly there are potentially grave consequences. A complex Middle East Carbonate field needed a bespoke shaly water saturation equation. AI was used to ‘evolve’ an ideal equation, together with field specific saturation and cementation exponents. One UKCS gas field had an ‘oil problem’. Here, AI was used to unlock the hidden fluid information in the NMR T1 and T2 spectra and successfully differentiate oil and gas zones in real time. A North Sea field with 30 wells had shear velocity data (Vs) in only 4 wells. Vs was required for reservoir modelling and well bore stability prediction. AI was used to predict Vs in all 30 wells. Incorporating high vertical resolution data, the Vs predictions were even better than the recorded logs. As it is not economic to take core data on every well, AI is used to discover the relationships between logs, core, litho-facies and permeability in multi-dimensional data space. As a consequence, all wells in a field were populated with these data to build a robust reservoir model. In addition, the AI predicted data upscaled correctly unlike many conventional techniques. AI gives impressive results when automatically log quality controlling (LQC) and repairing electrical logs for bad hole and sections of missing data. AI doesn’t require prior knowledge of the petrophysical response equations and is self-calibrating. There are no parameters to pick or cross-plots to make. There is very little user intervention and AI avoids the problem of ‘garbage in, garbage out’ (GIGO), by ignoring noise and outliers. AI programs work with an unlimited number of electrical logs, core and gas chromatography data; and don’t ‘fall-over’ if some of those inputs are missing. AI programs currently being developed include ones where their machine code evolves using similar rules used by life’s DNA code. These AI programs pose considerable dangers far beyond the oil industry as described in this paper. A ‘risk assessment’ is essential on all AI programs so that all hazards and risk factors, that could cause harm, are identified and mitigated.

Dating in the age of AI

This article discusses the use of AI, machine learning and data processing technology in online dating applications for matching people together. Methods include facial recognition technology, key personality markers based on objects/activities detected in pictures, AI enabled chatbots, Myers-Briggs personality types, and natural language processing to generate language synchronicity scores. Societal impacts are also alluded to such as: should you take advice from AI, should you let AI process your data and take control of you love life, does AI let you think how you usually would in the real world and is the experienced emotion the same. Ethical aspects are explored such as: propensity for bias, ethical AI programming and who is in control of the AI. It finishes with a discussion on the application of virtual reality and avatars in dating applications, and the impact of these AI technologies in online dating on society.

Slow flow or prolonged arterial phase in coronary arteries is the cause of ischemia or sudden death in patients with dilated cardiomyopathy

Abstract Background In patients with dilated cardiomyopathy (DCM), the patients could be symptomatic or presented with recurrent fluid overload, syncope or sudden cardiac death (SCD) while the coronary arteries were patent. The aim of our study was to use the coronary flow abnormalities to stratify the high risk symptomatic versus the low risk asymptomatic patient with DCM. Methods Consecutive patients with DCM were enrolled. Twenty patients with normal ejection fraction (EF) without coronary artery disease served as control. The study patients were checked for symptoms (fluid overload, syncope, SCD) and re-admission. All patients underwent a new coronary angiographic technique with injection of contrast until all the coronary arteries were completely filled. As the injection of contrast stopped, the blood in white color moved in and the blood movement could be clearly observed. The angiogram was recorded from the entry of blood flow until all the contrast was cleared. During the review, the investigators downloaded, selected the angiogram from the electronic medical record, tapped on the Key Image and used the Up and Down arrow to move the images, frame-by-frame. Each frame represented a 0.06-second recording. The duration of the arterial phase was calculated starting the time when the blood entered the ostium of the index artery until all the contrast disappeared from the distal vasculature. At the same time, an AI program was trained to measure the length of the arterial phase by Machine learning, supervised and unsupervised Deep Learning and Convoluted Neural Networks. (Figure 1) The AI programs compared the time when the arteries were full with contrast until there was no contrast left in the distal vasculature. (Figure 2) Results One hundred patients with DCM were consecutively enrolled. Twenty patients served as control. In the control group with normal flow and EF, the duration of the arterial phase was 24–30 frames (1.44 to 2 seconds). In the study group, seventy patients had extremely prolonged arterial phase (average of 120 frames or >8 seconds (p<0.05). These patients were very symptomatic and had recurrent hospitalizations. Thirty patients had normal arterial phase of <2 seconds. These patients had shorter length of stay (<3 days), became asymptomatic after only 2 days of treatment and had rare readmission. (p<0.05) The AI programs confirmed the results of the arterial phases calculated manually by junior investigators. Conclusions In patients with DCM, the extreme prolonged arterial phase caused ischemia in the myocardium even there was no coronary artery disease. This ischemic burden triggered recurrent ventricular dysfunction, arrhythmia, syncope and SCD. The patients with normal arterial phase became asymptomatic after optimal medical treatment. With these results, more effective prevention and management could be achieved for high risk symptomatic patients with high mortality and readmissions. Funding Acknowledgement Type of funding sources: None. U Net architectureArterial Phase

Artificial Intelligence: Mind, Computer and the Dance of the Wu Li Masters

In these days of exuberant fantasies about the future development of artificial intelligence—mostly written by people who have never in their lives developed an AI program—the GFFT (Society for the Promotion of Technology Transfer) has also unleashed a competition on future AI scenarios to honour Wolfgang Bibel. Because I was allowed to give the laudatory speech for Wolfgang, I was also asked to contribute something to the pen. And because, despite everything else, it is not reprehensible to think about the future, I could not refrain from doing so. Here is my somewhat expanded contribution.

Human+AI Crowd Task Assignment Considering Result Quality Requirements

This paper addresses the problem of dynamically assigning tasks to a crowd consisting of AI and human workers. Currently, crowdsourcing the creation of AI programs is a common practice. To apply such kinds of AI programs to the set of tasks, we often take the ``all-or-nothing'' approach that waits for the AI to be good enough. However, this approach may prevent us from exploiting the answers provided by the AI until the process is completed, and also prevents the exploration of different AI candidates. Therefore, integrating the created AI, both with other AIs and human computation, to obtain a more efficient human-AI team is not trivial. In this paper, we propose a method that addresses these issues by adopting a ``divide-and-conquer'' strategy for AI worker evaluation. Here, the assignment is optimal when the number of task assignments to humans is minimal, as long as the final results satisfy a given quality requirement. This paper presents some theoretical analyses of the proposed method and an extensive set of experiments conducted with open benchmarks and real-world datasets. The results show that the algorithm can assign many more tasks than the baselines to AI when it is difficult for AIs to satisfy the quality requirement for the whole set of tasks. They also show that it can flexibly change the number of tasks assigned to multiple AI workers in accordance with the performance of the available AI workers.

RISIKO RELATIF FAKTOR-FAKTOR PEMELIHARAAN YANG MEMENGARUHI INFERTILITAS HASIL INSEMINASI BUATAN PADA SAPI BALI DI LOMBOK TIMUR

The aim of this study was to evaluate odds ratio (OR) of management factors on infertility of artificial insemination results. Three traditional farmer groups were purposively choosed, based on percentage of calf born of AI program. Group I, with calves born of 61.22%, Group II with calves born of 38,89 %, and group III with calves born of 30,0%. The management factors evaluated were 1. ownerships of cattle, 2. raise cattle in separate pen with other, 3. cattle raised in pen dirty pen, 4. not enough feed given 5. body condition score. The data was obtained by dichotomy questioner and observation then evaluated using odds ratio (OR), when OR more than 1 indicated the high risk of factor to infertility. The results of OR in Group I showed that ownership (1,18), separated pen (0,54), dirty pen (2,11), less feed (3,20) and body condition (0,22). The results in Group II showed that ownership (0,83) , separated pen (1,12), dirty pen (0,14), less feed (2,08) and body condition (2,00). The results of odds ratios in Group III showed that ownership (1,63), separated pen (3,83), dirty pen (0,88), less feed (1,00) and body condition (0,12). These data indicated that ownerships, dirty pen, less feed and body condition were factors which may confounding with other factors. However less OR of separate pen were, in Group I (0,54), Group II (1,12) and Group III (3,83) consistently with number of calves produced. It can be concluded that separate pen was a factor which influence calves produced in using artificial insemination.

Conversational AI

As we know a business has many customers and these customers may have a lot of queries. It is hardly possible for a customer representative to attend every customer personally. Hence a conversational AI is used to provide a simple and efficient solution and provide a human like experience to the user. The project aims to develop a Conversation AI program that can help the business handle different types of queries the customers may have. Even people who know a little about a computer or a smart phone can easily use this AI. This product in saves the resources and time of the business and also eases the customers.

Constructionism and AI: A history and possible futures

AbstractConstructionism, long before it had a name, was intimately tied to the field of Artificial Intelligence. Soon after the birth of Logo at BBN, Seymour Papert set up the Logo Group as part of the MIT AI Lab. Logo was based upon Lisp, the first prominent AI programming language. Many early Logo activities involved natural language processing, robotics, artificial game players, and generating poetry, art, and music. In the 1970s researchers explored enhancements to Logo to support AI programming by children. In the 1980s the Prolog community, inspired by Logo's successes, began exploring how to adapt logic programming for use by school children. While there have been over 40 years of active AI research in creating intelligent tutoring systems, there was little AI‐flavoured constructionism after the 1980s until about 2017 when suddenly a great deal of activity started. Amongst those activities were attempts to enhance Scratch, Snap!, and MIT App Inventor with new blocks for speech synthesis, speech recognition, image recognition, and the use of pre‐trained deep learning models. The Snap! enhancements also include support for word embeddings, as well as blocks to enable learners to create, train, and use deep neural networks. Student and teacher project‐oriented resources highlighting these new AI programming components appeared at the same time. In this paper, we review this history, providing a unique perspective on AI developments—both social and technical—from a constructionist perspective. Reflecting on these, we close with speculations about possible futures for AI and constructionism. Practitioner notesWhat is already known about this topic There exist excellent broad surveys of the current status of teaching machine learning in schools, for example Marques et al. (2020). There are historical collections of AI and education research papers that include descriptions of constructionist activities, for example Yazdani (1984). What this paper adds This paper adds an in‐depth focus on historical and current efforts on AI and education that support constructionist teaching. This focus enables us to delve deeper than a broad survey. Uniquely, we provide a 50‐year historical perspective on constructionist AI tools, trials, and research. Grounded in this history and our survey of current tools and projects, we provide speculations about future directions. Implications for practice and/or policy We hope our descriptions of current AI programming tools for non‐experts placed in a broad historical context will be of use to teachers wishing to introduce AI to their students in a constructionist manner, as well as to developers and researchers aiming to support such teaching.

Artificial Intelligence Approaches to Assessing Primary Cilia.

Cilia are microtubule based cellular appendages that function as signaling centers for a diversity of signaling pathways in many mammalian cell types. Cilia length is highly conserved, tightly regulated, and varies between different cell types and tissues and has been implicated in directly impacting their signaling capacity. For example, cilia have been shown to alter their lengths in response to activation of ciliary G protein-coupled receptors. However, accurately and reproducibly measuring the lengths of numerous cilia is a time-consuming and labor-intensive procedure. Current approaches are also error and bias prone. Artificial intelligence (Ai) programs can be utilized to overcome many of these challenges due to capabilities that permit assimilation, manipulation, and optimization of extensive data sets. Here, we demonstrate that an Ai module can be trained to recognize cilia in images from both in vivo and in vitro samples. After using the trained Ai to identify cilia, we are able to design and rapidly utilize applications that analyze hundreds of cilia in a single sample for length, fluorescence intensity and co-localization. This unbiased approach increased our confidence and rigor when comparing samples from different primary neuronal preps in vitro as well as across different brain regions within an animal and between animals. Moreover, this technique can be used to reliably analyze cilia dynamics from any cell type and tissue in a high-throughput manner across multiple samples and treatment groups. Ultimately, Ai-based approaches will likely become standard as most fields move toward less biased and more reproducible approaches for image acquisition and analysis.

AN EMPIRICAL MODEL FOR EXPLORING AI IN GOVERNMENT: PUTTING SOCIO-TECHNOLOGICAL SYSTEMS PERSPECTIVES INTO USE

This study explores the evolution of global AI dynamics by discussing its role in government with a focus on aspects of development and governance of social and technological systems (STS). This document reports three research questions, including the extent of the analysis: (1) theories regarding the concept of AI in the public sector; (2) expectations regarding the development of AI in the public sector; and, (3) the challenges and opportunities of AI in the public sector. This experimental study provides an experimental framework for a comprehensive approach to measuring the magnitude of AI policy that allows for the methods of evaluating different governance practices and policy priorities in different countries. The study sheds light onto areas of policy that have the potential to implement AI programs and strategies; administrative functions open to the acceptance of AI applications and strategies; and the challenges / risks that community managers may face in defining AI policies and projects in the public sector including how to deal with cyber-troops.

Research on the Requirements of Artificial Intelligence on Applied Talents Cultivation

Strengthening the training of artificial intelligence talents and developing the artificial intelligence industry should be the only way to gain a foothold in the future of technology and talent competition. At the same time, it can promote the establishment of the highland of independent innovation in the field of AI. Talents market situation provides new requirement to talents cultivation in universities. But it should also be noted that AI development in universities meets with pretty problems. These are the newly-constructed related programs just growing in recent few years, the weakening of AI faculties in universities, especially the fragmentary of education system. Therefore, universities should promote the development of quality programs in the field of AI to get a new goal of talents cultivating. Otherwise, we should strengthen the integration and cooperation between AI and other applied programs to perform AI program structure and keep on classified and stratified cultivating to form a brand-new mode of talents training in all the field of AI.

Second-wave AI and Afro-existential norms

The idea of afro-existentialism connotes how Africans make sense of living and the meaning and meaninglessness attached to human existence. Different phenomena inform the way humans interpret existence, and one of such in the contemporary period, with great influence on Africans, is human involvement with non-human intelligence (AI), in its different eruptions. This paper focuses on the second-wave AI, which is a period of improved simulation of natural intelligence, whose singularity principle hypothesizes individualist motives. The paper asks, to what extent do Afroexistential norms accommodate second-wave AI? Partly in disagreement with the claim that AI is for everyone, we argue that second-wave artificial intelligence weakly adapts to Afro-existential practices, which is largely communal, emphasizing shared experience. We justify this claim by arguing that Western ethical patterns, which inform the features of the second-wave AI such as statistical patterns, smart algorithm, specialized hardware, and big data sets, emerge from individualist notions. This paper argues that second-wave AI trends do not reflect African norms of existence being factored into ordering algorithmic patterns that set up AI systems and programs. We infer that Afro-existential practices unsettles with the individualist principle which underlines second-wave AI and therefore, a conversation around the development and application of communal interpretation of AI is important.

Use of artificial intelligence in sports medicine: a report of 5 fictional cases

BackgroundArtificial intelligence (AI) is one of the most promising areas in medicine with many possibilities for improving health and wellness. Already today, diagnostic decision support systems may help patients to estimate the severity of their complaints. This fictional case study aimed to test the diagnostic potential of an AI algorithm for common sports injuries and pathologies.MethodsBased on a literature review and clinical expert experience, five fictional “common” cases of acute, and subacute injuries or chronic sport-related pathologies were created: Concussion, ankle sprain, muscle pain, chronic knee instability (after ACL rupture) and tennis elbow. The symptoms of these cases were entered into a freely available chatbot-guided AI app and its diagnoses were compared to the pre-defined injuries and pathologies.ResultsA mean of 25–36 questions were asked by the app per patient, with optional explanations of certain questions or illustrative photos on demand. It was stressed, that the symptom analysis would not replace a doctor’s consultation. A 23-yr-old male patient case with a mild concussion was correctly diagnosed. An ankle sprain of a 27-yr-old female without ligament or bony lesions was also detected and an ER visit was suggested. Muscle pain in the thigh of a 19-yr-old male was correctly diagnosed. In the case of a 26-yr-old male with chronic ACL instability, the algorithm did not sufficiently cover the chronic aspect of the pathology, but the given recommendation of seeing a doctor would have helped the patient. Finally, the condition of the chronic epicondylitis in a 41-yr-old male was correctly detected.ConclusionsAll chosen injuries and pathologies were either correctly diagnosed or at least tagged with the right advice of when it is urgent for seeking a medical specialist. However, the quality of AI-based results could presumably depend on the data-driven experience of these programs as well as on the understanding of their users. Further studies should compare existing AI programs and their diagnostic accuracy for medical injuries and pathologies.

ARTIFICIAL INTELLIGENCE APPLICATION IN EDUCATION: FINANCIAL IMPLICATIONS AND PROSPECTS

Modern education systems face new possibilities of application of innovative technological decisions in education that promote adaptive learning systems. Nowadays artificial intelligence becomes the central element of these systems and the basic tool to obtain competitive advantages on education services market. Thus, the aim of our research is to determine main trends, challenges and opportunities in application of artificial intelligence in education, exploring financial and social benefits, prospects and threats of that process. In order to achieve main aim of the research, some general and specific methods, such as content analysis, synthesis, abstraction and logical-graphic structuring, were used. Main trends are presented from three points of view: new horizons of teaching and learning opportunities, new threats for teaching and learning opportunities, and new financial and economic opportunities. Despite the social threats of AI usage, such as risks of human teacher unemployment and social interaction destruction within the learning process, market of AI for education systems steep increase. The highest rates of growth are typical for Asia-Pacific region, particularly for fast-growing economies such as China, and South Korea; North America market for AI in education still maintains the lead. Comparing trends in AI spread in education with financial successes of these countries we conclude that AI use in education systems can mitigate social drawbacks via greater accessibility for knowledge, higher quality of the educational process, individual and country competitiveness increase. Consequently, investments in innovation and promising AI programs in education remain a priority for economic growth and demand further support of educational policy makers.

ARTIFICIAL INSEMINATION OF PIGS IN NIGERIA

The beneficial effects of the use of Al and the stage of development in pigs in Nigeria were reviewed. A method for the training of the boars to mount an artificial dummy sow for the collection of semen for an AI programme has been modified for the Nigerian situation was adopted and perfected for this purpose. The normal procedure for the evaluation of semen samples have been used as one of the various methods for the selection of desirable sires for an AI programme

Artificial Intelligence-Based ECG Diagnosis of Myocardial Infarction in High-Risk Emergency Department Patients

Background: Myocardial infarctions are often missed in the emergency department. Deep learning models have shown promise in ECG classification in managed settings. We postulate that deep learning models can be useful for ECG myocardial infarction discrimination also in real-world emergency department patients with a high suspicion of an acute coronary syndrome. Methods: We studied emergency department patients in the Stockholm region between 2007 and 2016 that had an ECG obtained and were admitted to a coronary care unit. We developed a deep neural network based on convolutional layers similar to a residual network. Inputs were ECG, age and sex; output label was non-ST-elevation myocardial infarction (NSTEMI), ST-elevation myocardial infarction (STEMI), and control status as registered in the SWEDEHEART registry by the discharging coronary care unit physician. We used an ensemble of five models. Findings: Of the included 12,311 coronary care unit admissions, 3,993 were recorded with an NSTEMI, 1,340 a STEMI, and 6,978 had not had a myocardial infarction. In a random test set, our model could discriminate STEMIs with fair precision, with a C-statistic of 0.85 and a Brier score of 0.10. Discrimination of NSTEMIs was poorer, with a C-statistic of 0.76 and a Brier score of 0.18. The performance was similar in a temporal test set that did not overlap in time with the training set. Interpretation: We developed and validated a deep learning model with human-level performance in classifying NSTEMI and STEMI on the presenting ECG of a real-world sample of emergency department patients with a high suspicion of an acute coronary syndrome. Deep learning models for ECG decision support could be valuable in the emergency department. Funding: The Kjell and Marta Beijer Foundation, Anders Wiklof, the Wallenberg AI, Autonomous Systems and Software Program (WASP) funded by the Knut and Alice Wallenberg Foundation, and Uppsala University. Declaration of Interest: JS reports stock ownership in companies providing services to Itrim, Amgen, Janssen, Novo Nordisk, Eli Lilly, Boehringer, Bayer, Pfizer and AstraZeneca, outside the submitted work. No other authors have anything to declare. Ethical Approval: The study was approved by the Swedish Ethical Review Agency, application number 2020-01654.

AI as the Court: Assessing AI Deployment in Civil Cases

Lawyers and some branches of the government use artificial intelligence based programs to take decisions and develop their business strategies. In addition, several research teams have developed AI programs that are able to predict court decisions. Similar systems may be used in courts in the near future to administer files, support judges, or maybe replace them. In this paper, we assess possible consequence of deploying AI in European courts. The paper is divided in four main sections. First, we distinguish between AI in the court – when AI is used by parties or the court administration, – and AI as the court – AI that can support or replace judges. Second, we categorise civil cases in Europe according to their complexity and conflict, suggesting that judges may be assisted by AI systems, but cannot be replaced for complex high-conflict cases. Third, we assess to what extent AI can replace judges and still meet the legal requirements following from (1) principles of access to justice like accessibility, transparency, and accountability; and (2) from the fundamental right to an effective remedy and the right to a fair trial as protected by the European Convention on Human Rights. Fourth, we conclude that under the current legal framework it is already feasible to replace judges by AI for non-complex low-conflict cases. In addition, using only AI to decide cases with higher complexity and conflict would threaten access to justice as well as the right to a fair trial. We, also, recognizes that in the future an increasing use of AI in courts will challenge our traditional understanding of concepts like access to justice and the right to a fair trial. This understanding will be driven by the perception court-users will have of AI as the court.

Developing a gamified AI-enabled online learning application to improve students’ perception of university physics

Abstract This article discusses the current progress of the on-going design efforts in creating an AI-enabled gamified web-based online learning application in the university introductory physics courses. The application aims to cater to the learning needs of students with diversity of the background, particularly students with little or weak background. The overall design architecture and principles are discussed, focusing on the rationale and design of the gamified elements that have been incorporated within the application, namely: (i) incremental difficulties, (ii) points and streaks, (iii) leaderboard, and (iv) gamified and animated user interface. Particularly, the gamified elements are considered through the lens of the ‘Head, Hands, and Heart’ of the students, with the objective to first encourage and motivate participation, stimulate practice, and strengthen the students’ domain knowledge of physics. In this article, an architecture to incorporate AI program into the web-based gamification platform by including various AI models (learner model, pedagogy model and domain model) is proposed. With the architecture, we aim to create a personalized tutor that provide effective and unique learning experience to help a learner achieve learning objectives. The AI enabled gamification platform also serves as an efficient feedback tool to teacher for optimization and redesigning of curriculum to best suit the need of individual learner.

184 Utilizing Breeding Indictors for an Effective AI Program in Brahman Cows

Abstract Estrus detection is crucial to an effective AI program. The objective of this study was to assess whether Estrotect Breeding Indicators were comparable to vasectomized gomer bulls wearing chin ball markers to accurately and efficiently identify estrus. It was hypothesized that Estrotect Breeding Indicators will accurately and more efficiently detect estrus than gomer bulls. On day 0, an Estrotect Breeding Indicator was placed on Brahman cows (n = 204) that were kept with four vasectomized bulls for the following 45-day study period. Once estrus was detected via activated heat patch, cows were artificially inseminated 14 hr later and the bull score was recorded on a scale from 1–3 (1- no bull marks; 3- solid bull marks). Cows were continually monitored for return to estrus and a pregnancy diagnosis was conducted on day 110. All cows displaying estrus (n = 137) had fully activated patches, while 67 cows were anestrous for the trial duration. A ROC curve analysis indicated that the sensitivity of the bulls detecting estrus was 77.4% compared with the Estrotect Breeding Indicators. Out of the cows with a fully activated patch, 62.77% (86/137) became pregnant to AI. Of these, 17.4% (15/86) of pregnant cows received a bull score of 1 or 2, meaning there were little to no marks left from the bulls. Failure of bulls to identify estrus can be attributed to bulls’ loss of energy, cows displaying homosexual behavior, bulls staying with a chosen cow, or even calves preventing the bull access to the cows in estrus. In conclusion, the patches detected estrus in a greater number of cows than the action of vasectomized bulls wearing chin ball markers leading to an increased pregnancy rate.