Shivnath Ghosh’s scientific contributions

Evolution of smart governance addresses challenges of urbanization, resource constraints, and sustainability by prioritizing citizen engagement and inclusive urban development. This chapter examines the integration of participatory governance models, emphasizing theoretical frameworks such as social capital theory and the technology acceptance model to explore dynamics of community involvement. Models like Arnstein's Ladder of Citizen Participation and the IAP2 Spectrum highlight the varying levels of citizen influence in decision-making. Emerging trends such as gamified engagement, real-time data utilization, and the use of augmented and virtual reality reshape citizen participation. The chapter emphasizes the significance of ethical frameworks, digital literacy initiatives, and equitable resource allocation for sustainable smart city development, providing practical insights for policymakers and researchers.

In modern knowledge-rich healthcare scene, combining artificial intelligence (AI) approaches alongside covering-based rough set theory offers a potential method for identifying significant trends in web-mined health care information. Traditional data analysis tools frequently struggle to deal with the inherent inconsistency and complicated nature of healthcare data, posing obstacles for making choices and treatment of patients. Nevertheless, by combining the strength of AI with the solid foundation of covering-based rough set theory, healthcare organisations can open up new avenues to enhanced processes for making decisions, increasing patient outcomes, and encouraging creativity in the delivery of healthcare. This study investigates the complementary nature of AI approaches and covering-based rough set theory in healthcare data analysis, highlighting its potential to revolutionise the delivery of health care through facilitating personalised therapy, optimising the deployment of resources, and improving the overall effectiveness of care provided to patients.

Social media addiction is a condition that continues to demand immediate attention, interworking with various social and cultural factors. With design decisions such as infinite scrolling and algorithmic content that keep users engaged longer, the influence of tech companies cannot be understated. Social norms, the desire to be continually connected with their cohort, certainly push people into compulsive behaviors. There's a distinction across generations, especially with respect to Millennials and Gen Z, who are digital natives and know of digital media through the scope of communication and socialization. That's why one should take the multi-pronged approach involving school-based education about digital literacy to enhance awareness toward addiction, along with campaigning that will spread awareness in people about possible risks of addiction and parental monitoring that inspires better digital habits in children. These would give the society an opportunity to strike a symbiotic relation with technology and combat the social media addiction.

This chapter proposes a dexterously advancement organization and adversity-shirking system that utilizes vehicle checking and distance-based brake control to optimize the movement stream and improve security. By choosing the number of vehicles on the road and enabling versatile braking based on inter-vehicle restrictions, the system centers on overcoming the obstacles of current action systems. The system comprises vehicle-mounted sensors, a central overseeing unit, and action control contraptions. The sensors recognize adjoining vehicles and transmit this information to the central organizing unit, which businesses calculate to check the total number of vehicles. The sensors engage distance-based brake control, enacting the vehicle's brakes to dodge collisions by always measuring the disconnected to the going a couple of times as of late vehicle. Other than that, the system businesses the vehicle number data to capably modify action light timing at crossing centers, diminishing clogs. On the off chance that effectively executed, this clever action organization system has the potential to insides and redesign security and capability on the way.

Traffic congestion is a prevalent problem in urban areas, that leads to increase in time of travel, consumes more fuel, and environmental pollution. Efficient traffic signal management and control systems are crucial for mitigating congestion and improving road safety. This paper introduces a comprehensive solution, a Traffic Signal Management and Control System (TSMCS), leveraging advanced technologies and data-driven approaches. The system integrates ultrasonic and Light Detection and Ranging (LiDAR) sensors for accurate vehicle counting, adaptive signal control algorithms, and cloud-based servers for real-time traffic predictions. A key innovation is the prioritization of emergency vehicles, enhancing public safety. Real-world case studies in Mumbai and Bangalore highlight the urgency for advanced traffic management. The proposed system's novelty lies in its dynamic signal control, hybrid sensor integration, and cloud-based predictive capabilities. Contributions include multi-agent reinforcement learning, practical case studies, and a vision for smart city integration. The results demonstrate improved traffic flow and emergency response times, making TSMCS a promising advancement in intelligent transportation systems.

Even after all these years, it seems that cancer still casts this long shadow over every aspect of public health and the number of its victims keeps increasing making cancer the second leading cause of death all over the world. In this fight, researchers are wielding a powerful tool: the mapping of gene behaviour. As much as being individualized, genes have revealed secrets about the cell's activity as well as ancestral information. Genes tell a story about when they are turned on or off. Therefore, researchers intend to pick up cancer earlier when it is easy to stop it from spreading. Here, machine learning starts to make its contribution which is one specific type on the list of deep learning. Now envisage deep learning as a top-class detective, an inner investigator who scrutinizes hundreds of gene expression data in extremely tiny details. Its secret weapon? The capacity to discover real links that could be overlooked in the otherwise complicated world. It will be this unprecedented demonstration of skill that can transform cancer systematics by replacing the precedent practices with new, and far more accurate, ones. In this review, we take a close look at the most recent development in treating cancer genes classified by deep learning tools. Convolutional and multi-layer perceptron in the end multi-layer perceptron in the end will be explored, convolutional neural networks, which spot the embedded visual patterns and recurrent neural networks which excel at recognizing-sequential patterns and understanding context in the data. But there's a catch: the gene expression information is all complicated and abundant comprising genetic data for a wide range of genes. The review will also focus on how researchers can understand this information gap and use approaches like feature engineering and data pre-processing for preparing this data so that deep learning can be carried out. Finally, to wrap it up we`ll go deeper into the interesting future of this domain, and examine the possible new directions for the ML-based gene expression analysis. Therefore, deep learning techniques appear very powerful in cancer classification and feature extraction through the learning algorithms, which could ultimately help with leading more effective treatment methods. Introduction Cancer denotes a set of debilitating disease symptoms having the growth of aberrant cells that overgrow and infiltrate the body. These are the malicious cells whose development is due to the occurrence of genetic mutations and who can penetrate surrounding tissues and organs. Also, as the second major cause of death on earth next to cardiovascular obstructions [1], cancer is always one of the most serious health problems. Gene expression analysis has been an outstanding research tool for the apt detection of cancer and also drug development in the last couple of years [2,3]. Through the identification of those genes that are expressed, i.e. their turning on or off as required, scientists can garner important insights into those genes responsible for the development and progression of the disease. This data can be employed to create early detection systems and determine the ones that can be suitable for tailored novel therapies. Disclosing the molecular basis of cancer's epidemiology is a critical step for coming up with more efficient diagnostic and therapeutic methods. Add to Bookmark the story's progress, navigate differently, and make decisions based on their previous knowledge and experience. Transcriptomics, which examines which genes are expressed actively in cells, contributes with information about the genes linked with the run of cancer [3, 2]. This analysis is carried out by registering the mRNA transcripts number and consequently determining the genes that are operating and how intensively they function. Through the alignment of the-sequenced mRNA outputs to a reference genome, researchers are informed about the genes in use. DNA Microarrays and next-generation-sequencing (NGS) methods are the most popular technologies that researchers apply to gene expression studies. Microarray assay technologies which provide faster sample processing with higher throughput make work more convenient while RNA-seq technologies which are much more sensitive, and specific and cover a larger width of detectable gene expressions can be more accurate [6, 7, 8, 9]. RNA-seq (cDNA-sequencing) achieves the expression analysis by the use of reverse transcriptase enzyme (RNA-dependent DNA polymerase) which converts RNA molecules into its complementary DNA (cDNA) and then the-sequencing analysis of this cDNA. The multiplexed genetic profiling of the RNA transcriptome made up of all