Parvinder Singh

Fifth-generation wireless technology, or 5G, is the most recent mobile telecommunications standard. With higher data speeds, reduced latency, more capacity, and better connectivity than its forerunner, 4G, it represents a considerable improvement. In 5G networks, the scheduling mechanism regulates how much data is sent between different devices and the network. Cellular networks are really used in a variety of situations, including dense (urban regions), non-dense (rural areas), ultra-dense, etc. Schedulers are formed over the base stations to effectively allocate resources to the cell's customers. These schedulers operate according to the circumstances in which they are employed. Two scenarios—Single-Cell with Interference and Single-Cell with Femto-Cell—are developed in this paper by editing the code. The defined scenarios are then implemented using the two existing schedulers: Log_Rule and Maximum Throughput (MT). These approaches are applied to the aforementioned conditions, and the results are measured in terms of throughput, packet loss ratio, latency, and spectrum efficiency.

Natural Language Processing (NLP) is an interdisciplinary field that investigates the fascinating world of human language with the goal of creating computational models and algorithms that can comprehend, produce, and analyze natural language in a way that is similar to humans. LLMs still encounter issues with loud and unpolished input material despite their outstanding performance in natural language processing tasks. TextRefine offers a thorough pretreatment pipeline that refines and cleans the text data before using it in LLMs to overcome this problem . The pipeline includes a number of actions, such as removing social tags, normalizing whitespace, changing all lowercase letters to uppercase, removing stopwords, fixing Unicode issues, contraction unpacking, removing punctuation and accents, and text cleanup. These procedures work together to strengthen the integrity and quality of the input data, which will ultimately improve the efficiency and precision of LLMs. Extensive testing and comparisons with standard techniques show TextRefine's effectiveness with 99% of the accuracy. El Procesamiento del Lenguaje Natural (PLN) es un campo interdisciplinar que investiga el fascinante mundo del lenguaje humano con el objetivo de crear modelos computacionales y algoritmos que puedan comprender, producir y analizar el lenguaje natural de forma similar a los humanos. Los LLM siguen teniendo problemas con el material de entrada ruidoso y sin pulir, a pesar de su excelente rendimiento en tareas de procesamiento del lenguaje natural. TextRefine ofrece un proceso de pretratamiento exhaustivo que refina y limpia los datos de texto antes de utilizarlos en los LLM para superar este problema. El proceso incluye una serie de acciones, como la eliminación de etiquetas sociales, la normalización de los espacios en blanco, el cambio de todas las minúsculas a mayúsculas, la eliminación de palabras vacías, la corrección de problemas Unicode, la eliminación de contracciones, la eliminación de signos de puntuación y acentos, y la limpieza del texto. Estos procedimientos trabajan conjuntamente para reforzar la integridad y calidad de los datos de entrada, lo que en última instancia mejorará la eficacia y precisión de los LLM. Pruebas exhaustivas y comparaciones con técnicas estándar demuestran la eficacia de TextRefine con un 99% de precisión.

Brain tumor is one of the most prevalent and life-threatening illness these days. A tumor is an aberrant mass of tissue caused by unrestrained cell proliferation and multiplication. It is important to detect and diagnose brain tumors at the early stages. For disease diagnosis at an initial stage, there is a high demand for accurate analysis of healthcare data. But tumors vary greatly in size, shape, and existence, making it extremely difficult to collect precise measurements in order to properly diagnose them. Digital image processing enacts a crucial role in the analysis of medical images for timely and efficient planning of treatment. This paper provides an insight into brain tumors, the mechanism involved in their detection along with the different image processing steps that can be applied to medical images for automating the brain tumor detection process. This paper reviews a significant number of recently proposed brain tumor detection techniques related to the current study along with their tabulated comparison. This work can help in designing a solution that provides different applications such as detection, localization, or identifying the type of tumor under a single model.