Reviewerly/exHarmony huggingface dataset:
Healthcare Technologies Serving Cancer Diagnosis and Treatment

Fighting cancer is a life-time challenge for the cancer patient. Early prediction and diagnosis of cancer may extend the survival rate. The advent of artificial intelligence in medicine, especially in oncology, finds a new hope in cancer research. This chapter gives an insight into imaging techniques in cancer diagnosis, biopsy, and biomarker available for breast and lung cancer. Also, accombining data analysis with computer-aided techniques gives precise result and work on mass data. Evolutionary techniques, along with neural networks and data mining techniques, give improved localization of cancer detection. Particle Swarm Optimized Wavelet Neural Network is effective in spotting mass in mammogram images. Treatment methods of breast cancer, which includes chemotherapy, radiation therapy, is disclosed.
Automatic Hybrid Deep Learning Network for Image Lesion Prognosis and Diagnosis

Human lives are very precious. Human body parts are affected due to various food habits, environment, social habits, hereditary health issues and many more. One of the most well-known disorders is liver cancer. Liver cancer causes major loss to human lives. Early detection of this can be curable at the initial stages. Due to the advancement of computer vision algorithms and progression of IoT technologies working along with image processing techniques, early detection and curing are inevitable. To improve the classification time with faster duration which can help the medical practitioner, lesion detection algorithms are developed. The work’s key contribution is to apply efficient image segmentation algorithms along with powerful deep learning algorithms to classify the cancerous lesion efficiently. This ensemble or hybrid technique is developed by combining the morphological operations and deep learning procedures for an automated liver cancer prognosis and diagnosis. This fully connected network with U-net architecture improves the classification accuracy. Also increased true positive rate is achieved in the proposed methodology with high sensitivity. This novel work achieves high classification accuracy with minimum time. In addition, it also improves the area under curve region. Work was experimented with open-source datasets as well as with mobile few clinical images.
Prognosis and Diagnosis of Cancer Using Robotic Process Automation

The clinical management of cancer data storage for cancer treatment, analyzing cancer outcomes, providing services for cancer care, accurate measurement for research in future prediction, treatment history, and comparative future research work, plays a very important role in precision cancer health care. However, cancer stage data registries are often found to be inaccurate, missing data, or data are not collected perfectly. To perform all these tasks the need for manpower increases. Therefore, centralization in monitoring and analysis of cancer patient records is an important role in order to have periodic analysis of patients’ history. There are various details on the cancer patient such as individual personal details, cancer stage details, level of growth, and type of treatment in progress. There is also very specific information called quantitative data that are generated from the radiomic features of the patient, which show the specific medical condition and diagnostic features in cancer imaging data and need to be updated periodically. Personalization of cancer patient data, the treatment of every individual, plays an important role in analytics. Data need to be saved for the best treatment options for every individual in the future. However, the processing of the entire sequence of information periodically seems to be a tedious procedure. In order to provide quality medical care and better service to the patients and to reduce the level of medical expenses a software bot called Robotic Process Automation has been introduced that has the ability to solve a problem by training the machine itself using the historical data of the patient, which can be either supervised or unsupervised. The bot supports replacing normal repetitive routine performed by skilled workers with a highly automated procedure. Robotic health care automation allows similar processes to be automated and reused, and can monitor the status of clients accurately and identify situations. Precise medicine is testing tumors at the right time, at the right stage, and to provide the best treatment.
Healthcare Revolution and Integration of Artificial Intelligence

Healthcare has long been attributed to human intelligence and judgement. Twentieth century has sown the seed for a revolution that has geared up the healthcare technology towards a phenomenal development. Healthcare has entered the digital era as a process driven by need. With access to large amounts of medical information from healthcare specialities, technologies, and electronic health records, its usability to improve the healthcare processes was sought for. Specifically, precise, personalized, and proactive form of healthcare decision support system has gained attention with the integration of artificial intelligence (AI). AI systems are capable of performing intelligent tasks akin to human brain. AI in healthcare in present day is the trendsetter for the future of precision diagnostic and therapeutic interventions. This chapter deals with how artificial intelligence has brought the intersection of human judgement and scientific data closer than ever. This chapter aims at unravelling the key timelines associated with health revolution and provides an overview of the integration of AI with healthcare, its components, impact, and challenges. AI in healthcare is a vast topic with lots of subdomains involving the components of AI, each contributing to specific needs in the healthcare processes. The chapter has briefed upon the role of each AI component in addressing specific healthcare problems in relation to the datatype used. Possibility of AI integration with traditional medicine and its promising role in drug design with respect to the unprecedented rise in new diseases is also discussed. As an end note the chapter also discusses the impact, risks, and challenges associated with AI and the future role of physicians. AI undoubtedly has induced an unfathomed revolution in the field of healthcare decision support and delivery systems. However, it also requires a lot of fine-tuning adding more explainability and reliability to the outcome of intelligent models to surpass the need of human intervention.
Performance analysis of convolutional neural networks for image classification with appropriate optimizers

Optimizers in Convolutional Neural Networks play an important role in many advanced deep learning models. Studies on advanced optimizers and modifications of existing optimizers continue to hold significant importance in the study of machine tools and algorithms. There are a number of studies to defend and the selection of these optimizers illustrate some of the challenges on the effectiveness of these optimizers. Comprehensive analysis on the optimizers and alteration with famous activation function Rectified Linear Unit (ReLU) offered to protect effectiveness. Significance is determined based on the adjustment with the original Softmax and ReLU. Experiments were performed with Adam, Root Mean Squared Propagation (RMSprop), Adaptive Learning Rate Method (Adadelta), Adaptive Gradient Algorithm (Adagrad) and Stochastic Gradient Descent (SGD) to examine the performance of Convolutional Neural Networks for image classification using the Canadian Institute for Advanced Research dataset (CIFAR-10).
Creativity Tools in QM

Brainstorming is an idea generation method developed by Alex F. Osborn in 1939 and modified by Charles Hutchison Clark, the purpose of which is to encourage the generation of new, unusual ideas in a group of people. He named it after the idea behind this method, namely, using the brain to storm a problem (literally: using the brain to storm a problem). In brainstorming, ideas and suggestions on a topic are freely expressed and collected. It does not matter how mature and high quality an idea is, but first of all that as many ideas as possible are collected. It is important that all participants collect and publish ideas.
Unsupervised Adaptation of Polyp Segmentation Models via Coarse-to-Fine Self-Supervision

Unsupervised Domain Adaptation (UDA) has attracted a surge of interest over the past decade but is difficult to be used in real-world applications. Considering the privacy-preservation issues and security concerns, in this work, we study a practical problem of Source-Free Domain Adaptation (SFDA), which eliminates the reliance on annotated source data. Current SFDA methods focus on extracting domain knowledge from the source-trained model but neglects the intrinsic structure of the target domain. Moreover, they typically utilize pseudo labels for self-training in the target domain, but suffer from the notorious error accumulation problem. To address these issues, we propose a new SFDA framework, called Region-to-Pixel Adaptation Network (RPANet), which learns the region-level and pixel-level discriminative representations through coarse-to-fine self-supervision. The proposed RPANet consists of two modules, Foreground-aware Contrastive Learning (FCL) and Confidence-Calibrated Pseudo-Labeling (CCPL), which explicitly address the key challenges of “how to distinguish” and “how to refine”. To be specific, FCL introduces a supervised contrastive learning paradigm in the region level to contrast different region centroids across different target images, which efficiently involves all pseudo labels while robust to noisy samples. CCPL designs a novel fusion strategy to reduce the overconfidence problem of pseudo labels by fusing two different target predictions without introducing any additional network modules. Extensive experiments on three cross-domain polyp segmentation tasks reveal that RPANet significantly outperforms state-of-the-art SFDA and UDA methods without access to source data, revealing the potential of SFDA in medical applications.
Edge-Based Graph Neural Networks for Cell-Graph Modeling and Prediction

Identification and classification of cell-graph features using graph-neural networks (GNNs) has been shown to be useful in digital pathology. In this work, we consider the role of edge labels in cell-graph modeling, including histological modeling techniques, edge aggregation in GNN architectures, and edge label prediction. We propose EAGNN (Edge Aggregated GNN), a new GNN model that aggregates both node and edge label information to take advantage of topological information about cellular data and facilitate edge label prediction. We introduce new edge label features that improve histological modeling and prediction. We evaluate our EAGNN model for the task of detecting the presence and location of the basement membrane in oral mucosal tissue, as a proof-of-concept application.
Mixup-Privacy: A Simple yet Effective Approach for Privacy-Preserving Segmentation

Privacy protection in medical data is a legitimate obstacle for centralized machine learning applications. Here, we propose a client-server image segmentation system which allows for the analysis of multi-centric medical images while preserving patient privacy. In this approach, the client protects the to-be-segmented patient image by mixing it to a reference image. As shown in our work, it is challenging to separate the image mixture to exact original content, thus making the data unworkable and unrecognizable for an unauthorized person. This proxy image is sent to a server for processing. The server then returns the mixture of segmentation maps, which the client can revert to a correct target segmentation. Our system has two components: 1) a segmentation network on the server side which processes the image mixture, and 2) a segmentation unmixing network which recovers the correct segmentation map from the segmentation mixture. Furthermore, the whole system is trained end-to-end. The proposed method is validated on the task of MRI brain segmentation using images from two different datasets. Results show that the segmentation accuracy of our method is comparable to a system trained on raw images, and outperforms other privacy-preserving methods with little computational overhead.
Model-Informed Deep Learning for Surface Segmentation in Medical Imaging

Automated surface segmentation is an important tool for utilizing medical image data in modern precision medicine for routine clinical practice and research. Deep-learning based methods have been developed for various medical image segmentation tasks. The inherent classification nature of those methods yet limits their capability of modeling global spatial dependency, which poses great challenges in incorporating geometric priors for segmentation, such as surface shape and surface smoothness, significantly compromising the accuracy and robustness of segmentation performance. To solve this problem, we propose integrating the graph-based optimal surface segmentation model into a new form of Convolutional Neural Networks (CNNs) that unifies the strengths of both deep learning and the graph segmentation model. To this end, we propose to parameterize the graph-based surface segmentation model and formulate the optimal surface segmentation as a quadratic programming problem, which admits an efficient inference for globally optimal solutions. The resulting network fully unifies graph segmentation modeling with CNNs, making it possible to train the whole deep network end-to-end with the usual back-propagation algorithm. Our experiments on two medical image segmentation applications demonstrated high performance of the proposed method with respect to segmentation accuracy, demands for annotated training data, and robustness to adversarial noise.
Book review

The Survivor Interaction Contrast (SIC) is a distribution-free measure for assessing the fundamental properties of human information processing such as architecture (i.e., serial or parallel) and stopping rule (i.e., minimum time or maximum time). Despite its demonstrated utility, there are some vital gaps in our knowledge: first, the shape of the serial maximum time SIC is theoretically unclear, although the one 0-crossing negative-to-positive signature has been found repeatedly in the simulations. Second, the theories of SIC have been restricted to two-process cases, which restrict the applications to a limited class of models and data sets. In this paper, we first prove that in the two-process case, a mild condition known as strictly log-concavity is sufficient as a guarantor of a single 0-crossing of the serial maximum time SIC. We then extend the definition of SIC to an arbitrary number of processes, and develop implicated methodology of SIC in its generalized form, again in a distribution-free manner, for both parallel and serial models in conjunction with both the minimum time and maximum time stopping rules. We conclude the paper by demonstrating application of the theorems to data from a short-term memory search task.
Using Automatically Recommended Seed Mappings for Machine Learning-Based Code-to-Architecture Mappers

Software architecture consistency checking (SACC) is a popular method to detect architecture degradation. Most SACC techniques require software engineers to manually map a subset of entities of a system's implementation onto elements of its intended software architecture. Manually creating such a "seed mapping" for complex systems is a time-consuming activity.
Enhancement of MRI Images Using Optimal Filter Topology

Now a day, the medical images play a vital role in detection and diagnosis of diseases. Magnetic Resonance Imaging is considered as the safest tool and hence it is commonly employed for diagnosis of brain related problems. However, the images obtained from MRI scans are affected by different type of noises like Salt and pepper noise, Gaussian noise and Speckle noise. These noises will deteriorate the quality of the images. Image denoising is the only solution to this problem. In this regard, this work formulated a new filtering topology to denoise the noises present in MRI brain images and also compared its performance with existing three filters namely Median, Lee and Frost filters over the brain images. Hence, to determine the efficiency of these filters, Error Sensitivity Measure parameters like Mean Squared Error, Peak Signal to Noise Ratio and Structural Similarity Index are calculated. The experiments have been performed over three datasets of brain images. From the analysis, it is proven that for the all kinds of noises, the proposed algorithm exhibits higher performance metric than the other filters. This algorithm can be used for images obtained from various smart electronic devices.
Dependable Mobile Federated Learning Classifications Focused for Mobile Intelligent Source Distribution System in Big Data Analytics using Artificial Internet of Things

Latest Mobile Federated learning (MFL) is an enormous procedure for confidentiality and limitations using Artificial Internet of Things (AIOT) tenders in Future-generation mobile networks (FGMN), the latest generation of 6G and 5G is created on the Latest radio (LR) knowledge, the Many-In(data) and Many-Out(data) (MIMO) amenities assorted for AIOT strategies have been accomplished. The independent reserve distribution then the smart excellence of facility group uniqueness in mobile networks founded on MFL schemes are grateful to encounter the rations of secrecy limitations of AIOT applications. In enormous MFL infrastructures, the heterogeneous local strategies broadcast their representations in locally and the parameters accomplished 5G and 6G networks to the aggregation servers in edge devices. Therefore, the quality of network consistency is required to simplify the NodeEnd-to-NodeEnd (NE2NE) consistency of MFL transportations deliver that gratification of various choices. This paper proposed a Mobile smart trivial system built the orientation Domain-defined networking (DDN) architecture to grip more huge MFL transportations upon customers as well as collectors for encounter stated viewpoints. The management technique regulates the typical restrictions and groups extent of the discrete consumer to reproduce the deceptive network circumstances secret by the Node K-nearest neighbours (NKNN) algorithm. The proposed scheme presented famous verified metrics, counting the NE2NE MFL message latency, quantity, system dependability, and model correctness.
A Replica-aware optimal path Based Data Transfer Scheduling on Cloud Using Adaptive Sunflower Optimization Algorithm

Two of the essential components of cloud computing are high-performance computing and mass storage, and when compared to other conventional distributed systems, cloud computing offers both components on a more accessible, elastic, and scalable platform. Due to disaster in the cloud, the stored data will be lost. To avoid this problem, replica management-based data transfer scheduling is proposed in this paper. For the scheduling process, the adaptive sunflower optimization (ASFO) algorithm is used which is selecting the optimal path among the number of paths. The selected path should be taken at a minimum distance. The entire work consists of solution encoding, fitness evaluation, and updation. The effectiveness of the suggested methodology is evaluated using response time, turnaround time, and waiting time, and our suggested method also produces better outcomes when compared to other methods.
CEDCES: A Cost Effective Deadline Constrained Evolutionary Scheduler for Task Graphs in Multi-Cloud System

Due to large computational resource requirements, a single cloud cannot meet the requirements of the workflow. Hence, a multi-cloud system, where multiple cloud providers pool their resources together becomes a good solution. The major objectives considered while scheduling the tasks present in a task graph include execution cost and makespan. In this paper, we present Cost Effective Deadline Constrained Evolutionary Scheduler (henceforth mentioned as CEDCES) which aims to minimize the execution cost under a given deadline constraint. CEDCES is a PSO based-method with novel initialization, crossover, and mutation schemes. Experiments on real-world workflows show that CEDCES outperforms the state-of-art algorithms, in particular, 60.41% on average in terms of execution cost. In cases where the deadline is violated, CEDCES gives the least overshoot in execution time and outperforming the others by 10.96% on average.
Evaluating synthetic pre-Training for handwriting processing tasks

In this work, we explore massive pre-training on synthetic word images for enhancing the performance on four benchmark downstream handwriting analysis tasks. To this end, we build a large synthetic dataset of word images rendered in several handwriting fonts, which offers a complete supervision signal. We use it to train a simple convolutional neural network (ConvNet) with a fully supervised objective. The vector representations of the images obtained from the pre-trained ConvNet can then be considered as encodings of the handwriting style. We exploit such representations for Writer Retrieval, Writer Identification, Writer Verification, and Writer Classification and demonstrate that our pre-training strategy allows extracting rich representations of the writers’ style that enable the aforementioned tasks with competitive results with respect to task-specific State-of-the-Art approaches.
Image Data Security in Cloud by using Steganography

By making computer system resources available to all users without requiring active supervision, cloud computing has had a tremendous impact on how data and information are managed. To safeguard users of their privacy and the security of information or data saved and exchanged over the platform. Data security is a major significant component of cloud computing. Now days, as data is accessible in many ways the demand for data security methods has aided in the development of various methods required to guarantee the confidentiality of data transmitted over the different network, hence assuring competent transfer medium for data security. Steganography refers to encrypting information such that the recipient can see it. Data concealing together within image file is referred to as image steganography. So, the cover picture is the image selected to secure data file, while the stego image is the image that outcome from steganography. In this paper authors discuss about steganography fundamentals as well applications and techniques of steganography.