Sanjay Kumar

Breast cancer diagnosis, immune cell profile, and survival forecasting are important but usually done separately, limiting clinical interpretation. This work combines histopathological diagnosis, immunological microenvironment analysis, and prognostic modeling into a data-driven pipeline. The proposed system involves three phases: PatchSight Classifier uses an optimized InceptionResNetV2 network with patch-based augmentation and transfer learning to classify benign and malignant breast tissue from the BreakHis dataset; ImmuneMap Detector uses Faster R-CNN on immunohistochemistry images from the LYSTO dataset to detect and quantify tumor-infiltrating lymphocytes; and LifeSpan Prognosticator integrates diagnostic and immune features. The PatchSight Classifier outperformed VGG-16, DenseNet-121, and baseline InceptionResNetV2 models with 98.76% accuracy and 0.98 F1-score at 400× magnification. ResNet-101’s ImmuneMap Detector had 98% detection accuracy and low lymphocyte counting inaccuracy. The LifeSpan Prognosticator identified survival-influencing biomarkers with a C-index above 0.90. This comprehensive computational pathology system improves diagnostic precision, immunological assessment, and survival prediction with interpretable, high-accuracy models. We provide end-to-end decision assistance for early detection, immunological assessment, and personalized breast cancer prognosis.

Medical device integration systems interconnect medical devices and systems smoothly and automatically, changing the applications of the modern medical world. This chapter, “Strategies for Development, Scalability, and Maintenance of Medical Device Integration Systems: A Comprehensive Guide, Delving into key aspects for the scaling and look of the chapter begin from the fundamental concepts of MDIS and the role that real-time plays in getting information into data then develop system improvements on technical system systems including IoT, cloud computing and standardized communication protocols for instance, HL7 and FHIR together with long-term successful sustainability on compliance and device management collectively is guaranteed by scalable, sustainable MDISs and discussed modular system architectures. Cloud Native Design, Load-Balancing Techniques Maintenance strategies building integrated cybersecurity management and legacy systems. Finally, they come with ethical, legal, and regulatory considerations that ensure patients are protected in terms of safety and data security.

Breast cancer originates in breast cells and remains one of the most prevalent and life-threatening cancers among women, second only to lung cancer. This study introduces a Convolutional Neural Network (CNN)based method aimed at improving the automated detection of breast cancer through the analysis of histopathological images. The system leverages advanced CNN architectures to classify breast cancer images, emphasizing the identification of malignant tissue in whole slide images (WSIs). The dataset comprises 2,013 RGB images, each resized to a resolution of 200×200 pixels. Preprocessing techniques, including data augmentation methods such as rotation, shifting, and zoom, are applied to enhance the dataset’s diversity and normalize pixel values for consistent model training.The CNN model architecture incorporates multiple convolutional layers, max-pooling layers, and dropout mechanisms for regularization, culminating in a fully connected layer with a sigmoid activation function for binary classification (benign or malignant). The training process utilizes a batch size of 32 images and incorporates optimization techniques such as early stopping, learning rate adjustment, and model checkpointing to enhance performance. Achieving an accuracy of 90.3%, the proposed system significantly outperforms the 78% accuracy commonly associated with traditional machine learning approaches. These findings highlight the superior capabilities of CNN-based models in breast cancer detection, reducing the likelihood of diagnostic errors. This study demonstrates that CNN-based approaches can substantially improve the accuracy and reliability of early breast cancer detection systems, offering a valuable tool for clinical diagnostics.

In this article, a UWB metasurface absorber is designed for a frequency range of 6.75GHz-14.5GHz. The absorber consists of a simple unit cell size of 0.265λ ₀ ×0.265λ ₀ ×0.103λ ₀ on a single-layer FR-4 substrate with a thickness of 3.1mm. It has greater than 90.0% absorbance. Next, an X-band circularly polarized MIMO antenna is developed on an FR-4 substrate with a thickness of 1.0mm. The antenna has dimensions of 0.966λ ₀ ×0.766λ ₀ ×0.053λ ₀ . The MIMO antenna’s S-parameters, radiating efficiency, gain, and diversity parameters are further indicators of its usefulness. The designed metasurface is also employed as a tool to enhance the peak gain of the proposed MIMO antenna up to 8.8dBi and reduce radar cross-section by 18dBm ² , by placing the metasurface array superstrate and down correspondingly. The simulated and measured results for the metasurface and antenna, both individually and collectively, are more harmonious, demonstrating the applicability of the suggested designs for IoT over satellite, military applications, and identifying blood clots in CT-scan applications.

many firms and manufacturers, knowledge on public opinion is critical. They frequently want to know what consumers and the general public think of their products and services in real time. However, personally reading every post on the website and extracting important viewpoint information is not feasible. The frequency of data is very high to do it manually. Sentiment analysis enables for efficient and cost-effective data processing on a wide scale. We investigated the application of sentiment analysis in business to better understand its strengths and limits in order to learn more about it. Using Python and Machine Learning Algorithms, this project created a representation to forecast the response of a remark given the observation declaration using a dataset of Amazon Alexa reviews.

The spectral image analysis of complex analytic systems is usually performed in analytical chemistry. Signals associated with the key analytics present in an image scene are extracted during spectral image analysis. Accordingly, the first step in spectral image analysis is to segment the image in order to extract the applicable signals for analysis. In contrast, using traditional methods of image segmentation in chronometry makes it difficult to extract the relevant signals. None of the approaches incorporate contextual information present in an image scene; therefore, the classification is limited to thresholds or pixels only. An image translation pixel-to-pixel (p2p) method for segmenting spectral images using a generative adversary network (GAN) is presented in this paper. The p2p GAN forms two neuronal models. During the production and detection processes, the representation learns how to segment ethereal images precisely. For the evaluation of the results, a partial discriminate analysis of the least-squares method was used to classify the images based on thresholds and pixels. From the experimental results, it was determined that the GAN-based p2p segmentation performs the best segmentation with an overall accuracy of 0.98 ± 0.06. This result shows that image processing techniques using deep learning contribute to enhanced spectral image processing. The outcomes of this research demonstrated the effectiveness of image-processing techniques that use deep learning to enhance spectral-image processing.

The cloth and loom were a long history since the emergence of human beings on the earth. The Hindu epics, the Mahabharata and the Ramayana, the Vedas and the sacred books of Hindu mythology speak of the art of weaving. Loom work is proving a large-scale employment in rural and urban places and it is an ancient industry. Weaving is the traditional occupation in India as well as in the Bhagalpur district of Bihar. Even though weavers form an integral part of the Indian loom industry, there is not enough recognition of their contribution to the sector due to various production and marketing constraints. It leads to poor economic conditions which casts doubt on their social security and future aspirations. Bhagalpur district of Bihar state was selected for this study.120 respondents were selected from 3 villages based on the population of weavers in villages. The present study was conducted in the years 2021-2022.The main constraints in production and marketing are revealed by respondents as scarcity of raw materials, insufficient weaving facility, COVID- 19 lockdown, lack of cooperative support, high commission charges, less transportation etc.

The humans have always tried to improve quality of living and technology plays a major role in this cause. Modern technology has been evolving with the emergence of smart technologies. Cash Based transaction involves lot of human effort of withdrawing and then storing the cash. It is also an insecure way of payment involving fears of theft etc… With large amounts of data transfer and e-payment needs, NFC is one of the prime technologies contributing towards fast and secure transmission of electronic signals, data and money. This paper focuses on NFC technology utilisation for a simplified and secure unified transaction system using IOT. Internet of things is a technology of present and future which has its applications in various fields like healthcare, transportation, manufacturing etc… There has already been a lot of research and developments on utilisation of NFC technology for transactions, but we propose a secure wireless transaction system by providing features such as database connectivity and user authentication in the system

The Cloud based Computing is a trending technology in present IT scenario in which data is stocked in a Scattered or distributed manner & resources are shared among people. The data centers are widely distributed across the globe and are accessed by anyone at any time. Data is fetched, uploaded and manipulated by several users simultaneously hence identification and authentication of users who are fetching the data need to be done mandatorily without any error. In this Research Paper we have presented a new advanced security architecture for user identification which includes two factor authentication, AES based file encryption and decryption of data uploaded on cloud, admin verification and locking of users, fetching IP details of users, distributed database storage i.e. data is stored in tiers which means user login details is stored in one database &encryption/decryption details such as (files uploaded, key) is stored on different database. Whole work deals on enhancing security for cloud computing.