Browsing by Subject "AI"

Now showing 1 - 10 of 27
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    AI Based Modelling and Optimization of Turning Process
    (2012-08) Kulkarni, Ruturaj Jayant; El-Mounayri, Hazim; Anwar, Sohel; Wasfy, Tamer
    In this thesis, Artificial Neural Network (ANN) technique is used to model and simulate the Turning Process. Significant machining parameters (i.e. spindle speed, feed rate, and, depths of cut) and process parameters (surface roughness and cutting forces) are considered. It is shown that Multi-Layer Back Propagation Neural Network is capable to perform this particular task. Design of Experiments approach is used for efficient selection of values of parameters used during experiments to reduce cost and time for experiments. The Particle Swarm Optimization methodology is used for constrained optimization of machining parameters to minimize surface roughness as well as cutting forces. ANN and Particle Swarm Optimization, two computational intelligence techniques when combined together, provide efficient computational strategy for finding optimum solutions. The proposed method is capable of handling multiple parameter optimization problems for processes that have non-linear relationship between input and output parameters e.g. milling, drilling etc. In addition, this methodology provides reliable, fast and efficient tool that can provide suitable solution to many problems faced by manufacturing industry today.
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    AI on the Edge with CondenseNeXt: An Efficient Deep Neural Network for Devices with Constrained Computational Resources
    (2021-08) Kalgaonkar, Priyank B.; El-Sharkawy, Mohamed A.; King, Brian S.; Rizkalla, Maher E.
    Research work presented within this thesis propose a neoteric variant of deep convolutional neural network architecture, CondenseNeXt, designed specifically for ARM-based embedded computing platforms with constrained computational resources. CondenseNeXt is an improved version of CondenseNet, the baseline architecture whose roots can be traced back to ResNet. CondeseNeXt replaces group convolutions in CondenseNet with depthwise separable convolutions and introduces group-wise pruning, a model compression technique, to prune (remove) redundant and insignificant elements that either are irrelevant or do not affect performance of the network upon disposition. Cardinality, a new dimension to the existing spatial dimensions, and class-balanced focal loss function, a weighting factor inversely proportional to the number of samples, has been incorporated in order to relieve the harsh effects of pruning, into the design of CondenseNeXt’s algorithm. Furthermore, extensive analyses of this novel CNN architecture was performed on three benchmarking image datasets: CIFAR-10, CIFAR-100 and ImageNet by deploying the trained weight on to an ARM-based embedded computing platform: NXP BlueBox 2.0, for real-time image classification. The outputs are observed in real-time in RTMaps Remote Studio’s console to verify the correctness of classes being predicted. CondenseNeXt achieves state-of-the-art image classification performance on three benchmark datasets including CIFAR-10 (4.79% top-1 error), CIFAR-100 (21.98% top-1 error) and ImageNet (7.91% single model, single crop top-5 error), and up to 59.98% reduction in forward FLOPs compared to CondenseNet. CondenseNeXt can also achieve a final trained model size of 2.9 MB, however at the cost of 2.26% in accuracy loss. Thus, performing image classification on ARM-Based computing platforms without requiring a CUDA enabled GPU support, with outstanding efficiency.
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    Comparison of Artificial Intelligence and Eyeball Method in the Detection of Fatty Liver Disease
    (2023-07-26) Catron, Evan J.; Passarelli, Robert P.; Danielle, Wilmes; Wei, Barry; Le, Thi M.U.; Li, Ping; Zhang, Wenjun; Lin, Jingmei; Melcher, Mark L.; Mihaylov, Plamen V.; Kubal, Chandrashekhar A.; Mangus, Robert S.; Ekser, Burcin
    Background: Quantification of liver fat content relies on visual microscopic inspection of liver biopsies by pathologists. Their percent macrosteatosis (%MaS) estimation is vital in determining donor liver transplantability; however, the eyeball method may vary between observers. Overestimations of %MaS can potentially lead to the discard of viable donor livers. We hypothesize that artificial intelligence (AI) could be helpful in providing a more objective and accurate measurement of %MaS. Methods: Literature review identified HALO (image analysis) and U-Net (deep-learning) as high-accuracy AI programs capable of calculating %MaS in liver biopsies. We compared (i) an experienced pathologist’s and (ii) a transplant surgeon’s eyeball %MaS estimations from de-novo liver transplant (LT) biopsy samples taken 2h post-reperfusion to (iii) the HALO-calculated %MaS (Fig.1). 250 patients had undergone LT at Indiana University between 2020-2021, and 211 had sufficient data for inclusion. Each biopsy was digitized into 5 random non-overlapping tiles at 20x magnification (a total of 1,055 images). We used HALO software for analysis and set the minimum vacuole area to 10μm² to avoid the inclusion of microsteatosis. Microsteatosis was excluded by the pathologist and the surgeon by the eyeball method using the same 1,055 images. Each %MaS estimation was compared with early allograft dysfunction (EAD). EAD is defined by the presence of at least one of the following: INR >1.6 on postoperative day (POD) 7, total bilirubin >10mg/dL on POD7, or AST/ALT >2000IU/L within the first 7 days following LT. Results: Of 211 LTs, 42 (19.9%) had EAD. The mean %MaS estimation of pathologist and transplant surgeon were 6.3% (SD: 11.9%) and 3.2% (SD: 6.4%), respectively. HALO yielded a significantly lower mean %MaS of 2.6% (SD: 2.6%) than the pathologist’s eyeball method (p<0.001). The mean %MaS calculated by HALO was higher in EAD patients than in non-EAD (p=0.032), but this difference did not reach statistical significance in the pathologist’s estimation (p=0.069). Conclusions: Although mean %MaS measurements from all parties were mild (<10%), human eyeball estimations of %MaS were significantly higher than HALO’s %MaS. The HALO-calculated %MaS differed significantly between the EAD and non-EAD LTs which might suggest a possible correlation between the AI’s steatosis analysis and EAD outcomes. However, pathologic variables other than %MaS (necrosis or cholestasis) should be included in future analyses to determine whether %MaS is the dominant parameter predicting EAD. AI is a promising tool to quantify liver steatosis and will help pathologists and transplant surgeons predict liver transplant viability.
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    Compassionate Care with Autonomous AI Humanoid Robots in Future Healthcare Delivery: A Multisensory Simulation of Next-Generation Models
    (MDPI, 2024-11-11) Hernandez, Joannes Paulus Tolentino; School of Nursing
    The integration of AI and robotics in healthcare raises concerns, and additional issues regarding autonomous systems are anticipated. Effective communication is crucial for robots to be seen as "caring", necessitating advanced mechatronic design and natural language processing (NLP). This paper examines the potential of humanoid robots to autonomously replicate compassionate care. The study employs computational simulations using mathematical and agent-based modeling to analyze human-robot interactions (HRIs) surpassing Tetsuya Tanioka's TRETON. It incorporates stochastic elements (through neuromorphic computing) and quantum-inspired concepts (through the lens of Martha Rogers' theory), running simulations over 100 iterations to analyze complex behaviors. Multisensory simulations (visual and audio) demonstrate the significance of "dynamic communication", (relational) "entanglement", and (healthcare system and robot's function) "superpositioning" in HRIs. Quantum and neuromorphic computing may enable humanoid robots to empathetically respond to human emotions, based on Jean Watson's ten caritas processes for creating transpersonal states. Autonomous AI humanoid robots will redefine the norms of "caring". Establishing "pluralistic agreements" through open discussions among stakeholders worldwide is necessary to align innovations with the values of compassionate care within a "posthumanist" framework, where the compassionate care provided by Level 4 robots meets human expectations. Achieving compassionate care with autonomous AI humanoid robots involves translating nursing, communication, computer science, and engineering concepts into robotic care representations while considering ethical discourses through collaborative efforts. Nurses should lead the design and implementation of AI and robots guided by "technological knowing" in Rozzano Locsin's TCCN theory.
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    COVID-19 and Bone Loss: A Review of Risk Factors, Mechanisms, and Future Directions
    (Springer, 2024) Creecy, Amy; Awosanya, Olatundun D.; Harris, Alexander; Qiao, Xian; Ozanne, Marie; Toepp, Angela J.; Kacena, Melissa A.; McCune, Thomas; Orthopaedic Surgery, School of Medicine
    Purpose of review: SARS-CoV-2 drove the catastrophic global phenomenon of the COVID-19 pandemic resulting in a multitude of systemic health issues, including bone loss. The purpose of this review is to summarize recent findings related to bone loss and potential mechanisms. Recent findings: The early clinical evidence indicates an increase in vertebral fractures, hypocalcemia, vitamin D deficiencies, and a loss in BMD among COVID-19 patients. Additionally, lower BMD is associated with more severe SARS-CoV-2 infection. Preclinical models have shown bone loss and increased osteoclastogenesis. The bone loss associated with SARS-CoV-2 infection could be the result of many factors that directly affect the bone such as higher inflammation, activation of the NLRP3 inflammasome, recruitment of Th17 cells, the hypoxic environment, and changes in RANKL/OPG signaling. Additionally, SARS-CoV-2 infection can exert indirect effects on the skeleton, as mechanical unloading may occur with severe disease (e.g., bed rest) or with BMI loss and muscle wasting that has also been shown to occur with SARS-CoV-2 infection. Muscle wasting can also cause systemic issues that may influence the bone. Medications used to treat SARS-CoV-2 infection also have a negative effect on the bone. Lastly, SARS-CoV-2 infection may also worsen conditions such as diabetes and negatively affect kidney function, all of which could contribute to bone loss and increased fracture risk. SARS-CoV-2 can negatively affect the bone through multiple direct and indirect mechanisms. Future work will be needed to determine what patient populations are at risk of COVID-19-related increases in fracture risk, the mechanisms behind bone loss, and therapeutic options. This review article is part of a series of multiple manuscripts designed to determine the utility of using artificial intelligence for writing scientific reviews.
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    Cracking the Code: The Role of Peripheral Nervous System Signaling in Fracture Repair
    (Springer, 2024) Morris, Ashlyn J.; Parker, Reginald S.; Nazzal, Murad K.; Natoli, Roman M.; Fehrenbacher, Jill C.; Kacena, Melissa A.; White, Fletcher A.; Orthopaedic Surgery, School of Medicine
    Purpose of review: The traditionally understated role of neural regulation in fracture healing is gaining prominence, as recent findings underscore the peripheral nervous system's critical contribution to bone repair. Indeed, it is becoming more evident that the nervous system modulates every stage of fracture healing, from the onset of inflammation to repair and eventual remodeling. Recent findings: Essential to this process are neurotrophins and neuropeptides, such as substance P, calcitonin gene-related peptide, and neuropeptide Y. These molecules fulfill key roles in promoting osteogenesis, influencing inflammation, and mediating pain. The sympathetic nervous system also plays an important role in the healing process: while local sympathectomies may improve fracture healing, systemic sympathetic denervation impairs fracture healing. Furthermore, chronic activation of the sympathetic nervous system, often triggered by stress, is a potential impediment to effective fracture healing, marking an important area for further investigation. The potential to manipulate aspects of the nervous system offers promising therapeutic possibilities for improving outcomes in fracture healing. This review article is part of a series of multiple manuscripts designed to determine the utility of using artificial intelligence for writing scientific reviews.
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    Development of Data-driven and AI-powered Systems Biology Methods for Understanding Human Disease
    (2024-08) Dang, Pengtao; Zhang, Chi; Salama, Paul; Cao, Sha; King, Brian; Ben-Miled, Zina
    Systems biology dynamic models, which are based on differential equations, offer a flexible and accurate framework to explain physiological properties emerging from complex biochem- ical or biological systems. These models enable explicit quantification and interpretation, allowing for simulation and perturbation analysis to study biological features and their inter- actions, as well as understanding system progression and convergence under various initial conditions. However, their application in human disease systems is limited due to unknown kinetics parameters under disease conditions and a reductionist paradigm that fails to cap- ture the complexity of diseases. Meanwhile, the advent of omics technologies provides high- resolution molecular measurements from single cells and spatially resolved samples, as well as comprehensive disease-specific molecular signatures from large patient cohorts. This wealth of data holds the promise for characterizing complex biological systems, necessitating ad- vanced systems biology models and computational tools that can harness multi-omics data to reliably depict biological processes. However, this endeavor faces the challenge of nonlinear relationships between omics data and the system’s dynamic properties, such as the global or local low-rank gene expression patterns across cell types and the nonlinear complexities within transcriptional regulatory networks revealed by single-cell RNA sequencing. The overall goal of this report is to develop new computational frameworks, AI-empowered methods, and related mathematical theories to explicitly represent and approximate the dy- namics of complex biological systems by using biological omics data. Our aim is to unravel the intricacies of context-specific dynamic systems using multi-Omics data. Specifically, we solved two different but related computational tasks and enabled the first-of-its-kind methods to (1) identify local low-rank matrices from large omics data, and (2) a robust optimization strategy to approximate metabolic flux. Subsequently, we delve into the realm of data-driven and AI-powered systems biology, harnessing the power of statistical learning and artificial intelligence to approximate differential equations or their representations. This research en- deavor not only contributes to the advancement of subspace modeling but also offers insights into a wide array of complex phenomena across diverse domains, with profound implications for computational biology and beyond.
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    Do Not Lose Your Nerve, Be Callus: Insights Into Neural Regulation of Fracture Healing
    (Springer, 2024) Nazzal, Murad K.; Morris, Ashlyn J.; Parker, Reginald S.; White, Fletcher A.; Natoli, Roman M.; Kacena, Melissa A.; Fehrenbacher, Jill C.; Orthopaedic Surgery, School of Medicine
    Purpose of review: Fractures are a prominent form of traumatic injury and shall continue to be for the foreseeable future. While the inflammatory response and the cells of the bone marrow microenvironment play significant roles in fracture healing, the nervous system is also an important player in regulating bone healing. Recent findings: Considerable evidence demonstrates a role for nervous system regulation of fracture healing in a setting of traumatic injury to the brain. Although many of the impacts of the nervous system on fracture healing are positive, pain mediated by the nervous system can have detrimental effects on mobilization and quality of life. Understanding the role the nervous system plays in fracture healing is vital to understanding fracture healing as a whole and improving quality of life post-injury. This review article is part of a series of multiple manuscripts designed to determine the utility of using artificial intelligence for writing scientific reviews.
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    Does your AI discriminate?
    (2020-05-15) Magid Manning, Julie; Kelley School of Business - Indianapolis
    My research indicates that relying on data analytics to eliminate human bias in choosing leaders won’t help.
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    Enhanced 3D Object Detection and Tracking in Autonomous Vehicles: An Efficient Multi-Modal Deep Fusion Approach
    (2024-08) Kalgaonkar, Priyank B.; El-Sharkawy, Mohamed; King, Brian S.; Rizkalla, Maher E.; Abdallah, Mustafa A.
    This dissertation delves into a significant challenge for Autonomous Vehicles (AVs): achieving efficient and robust perception under adverse weather and lighting conditions. Systems that rely solely on cameras face difficulties with visibility over long distances, while radar-only systems struggle to recognize features like stop signs, which are crucial for safe navigation in such scenarios. To overcome this limitation, this research introduces a novel deep camera-radar fusion approach using neural networks. This method ensures reliable AV perception regardless of weather or lighting conditions. Cameras, similar to human vision, are adept at capturing rich semantic information, whereas radars can penetrate obstacles like fog and darkness, similar to X-ray vision. The thesis presents NeXtFusion, an innovative and efficient camera-radar fusion network designed specifically for robust AV perception. Building on the efficient single-sensor NeXtDet neural network, NeXtFusion significantly enhances object detection accuracy and tracking. A notable feature of NeXtFusion is its attention module, which refines critical feature representation for object detection, minimizing information loss when processing data from both cameras and radars. Extensive experiments conducted on large-scale datasets such as Argoverse, Microsoft COCO, and nuScenes thoroughly evaluate the capabilities of NeXtDet and NeXtFusion. The results show that NeXtFusion excels in detecting small and distant objects compared to existing methods. Notably, NeXtFusion achieves a state-of-the-art mAP score of 0.473 on the nuScenes validation set, outperforming competitors like OFT by 35.1% and MonoDIS by 9.5%. NeXtFusion's excellence extends beyond mAP scores. It also performs well in other crucial metrics, including mATE (0.449) and mAOE (0.534), highlighting its overall effectiveness in 3D object detection. Visualizations of real-world scenarios from the nuScenes dataset processed by NeXtFusion provide compelling evidence of its capability to handle diverse and challenging environments.