R.M.D. Engineering College

The internet of things (IoT) is an important data source for data science technology, providing easy trends and patterns identification, enhanced automation, constant development, ease of handling multi-dimensional data, and low computational cost. Prediction in energy consumption is essential for the enhancement of sustainable cities and urban planning, as buildings are the world's largest consumer of energy due to population growth, development, and structural shifts in the economy. This study explored and exploited deep learning-based techniques in the domain of energy consumption in smart residential buildings. It found that optimal window size is an important factor in predicting prediction performance, best N window size, and model uncertainty estimation. Deep learning models for household energy consumption in smart residential buildings are an optimal model for estimation of prediction performance and uncertainty.

People communicate their views, arguments and emotions about their everyday life on social media (SM) platforms (e.g. Twitter and Facebook). Twitter stands as an international micro-blogging service that features a brief message called tweets. Freestyle writing, incorrect grammar, typographical errors and abbreviations are some noises that occur in the text. Sentiment analysis (SA) centered on a tweet posted by the user, and also opinion mining (OM) of the customers review is another famous research topic. The texts are gathered from users’ tweets by means of OM and automatic-SA centered on ternary classifications, namely positive, neutral and negative. It is very challenging for the researchers to ascertain sentiments as a result of its limited size, misspells, unstructured nature, abbreviations and slangs for Twitter data. This paper, with the aid of the Gradient Boosted Decision Tree classifier (GBDT), proposes an efficient SA and Sentiment Classification (SC) of Twitter data. Initially, the twitter data undergoes pre-processing. Next, the pre-processed data is processed using HDFS MapReduce. Now, the features are extracted from the processed data, and then efficient features are selected using the Improved Elephant Herd Optimization (I-EHO) technique. Now, score values are calculated for each of those chosen features and given to the classifier. At last, the GBDT classifier classifies the data as negative, positive, or neutral. Experiential results are analyzed and contrasted with the other conventional techniques to show the highest performance of the proposed method.

This chapter discusses the implementation of data analytics and artificial intelligence (AI) in the circular economy. The case studies cover various domains, such as resource tracking and tracing, resource recovery, decision-making support systems, and machining optimization. The outcomes include improved supply chain management, extended product lifecycles, reduced waste generation, enhanced product quality, and cost savings. Data analytics and AI have the potential to shape a sustainable circular economy by optimizing resource utilization, improving processes, and enabling informed decision-making.

Abstract Cloud Computing, the efficiency of task scheduling is proportional to the effectiveness of users. The improved scheduling efficiency algorithm (also known as the improved Wild Horse Optimization, or IWHO) is proposed to address the problems of lengthy scheduling time, high-cost consumption, and high virtual machine load in cloud computing task scheduling. First, a cloud computing task scheduling and distribution model is built, with time, cost, and virtual machines as the primary factors. Second, a feasible plan for each whale individual corresponding to cloud computing task scheduling is to find the best whale individual, which is the best feasible plan; to better find the optimal individual, we use the inertial weight strategy for the Improved whale optimization algorithm to improve the local search ability and effectively prevent the algorithm from reaching premature convergence. To deliver services and access to shared resources, Cloud Computing (CC) employs a cloud service provider (CSP). In a CC context, task scheduling has a significant impact on resource utilization and overall system performance. It is a Nondeterministic Polynomial (NP)-hard problem that is solved using metaheuristic optimization techniques to improve the effectiveness of job scheduling in a CC environment. This incentive is used in this study to provide the Improved Wild Horse Optimization with Levy Flight Algorithm for Task Scheduling in cloud computing (IWHOLF-TSC) approach, which is an improved wild horse optimization with levy flight algorithm for cloud task scheduling. Task scheduling can be addressed in the cloud computing environment by utilizing some form of symmetry, which can achieve better resource optimization, such as load balancing and energy efficiency. The proposed IWHOLF-TSC technique constructs a multi-objective fitness function by reducing Makespan and maximizing resource utilization in the CC platform. The IWHOLF-TSC technique proposed combines the wild horse optimization (WHO) algorithm and the Levy flight theory (LF). The WHO algorithm is inspired by the social behaviours of wild horses. The IWHOLF-TSC approach's performance can be validated, and the results evaluated using a variety of methods. The simulation results revealed that the IWHOLF-TSC technique outperformed others in a variety of situations.

Eco-friendly environment concern contributes to the survival of life on earth. Monitoring chemicals produced by growth requires a multidisciplinary scientific approach. Climate change is a result of pollution and overuse, particularly in the air. Although government and industry have taken initiatives to limit air pollution, it is still necessary to check the quality of the air at ground level. The goal of this endeavour is to build intercommunication-based individual vehicle air pollution monitoring and identification by internet of things (IoT). Idling and accelerating conditions were recorded using tailpipe emission sensing. Authorities can act and assure upkeep if they are able to identify a car at a busy intersection; mobile connectivity enabled route and vehicle identification. In India, fewer people will die from air pollution because to data gathered in the cloud.

The integration of deep learning and blockchain technologies has the potential to revolutionize soil quality prediction in smart agriculture. Deep learning models, like neural networks and convolutional neural networks, enable accurate predictions of soil properties by considering intricate relationships within data. Contextual learning approaches, including embeddings and data fusion, enrich the prediction process by incorporating external factors like weather conditions and land management practices. Blockchain technology ensures secure storage of predictions and data, while smart contracts facilitate automated model execution. This integrated system empowers farmers with accurate predictions for optimal resource allocation and fosters collaboration through decentralized data sharing. Future directions include advancements in deep learning algorithms, blockchain applications, and potential integration with IoT and remote sensing technologies.

The teaching and learning methodology in a typical engineering education system involves a teacher teaching to a group of 30 to 60 students and hence the students have a common learning path. With the advent of technology like Artificial Intelligence/Machine Learning, past historic data in an institution and current data of student profiles and performance can be used to analyse and predict learning gaps and suggest the learning steps a student has to take to improve his/her performance. This will result in each student having a personalized learning path. As many institutions have a well-defined Quality Management System (QMS) in line with the ISO 9001: 2015 standards and past data, they can adopt this new technology and hence achieve disruption in the education system in a phased manner.

Generative artificial intelligence (AI) can revolutionize K-12 education in India by enhancing curriculums and pedagogies. This chapter explores the principles of generative AI, including generative adversarial networks (GANs), variational autoencoders (VAEs), and natural language generation (NLG), and its potential to create content indistinguishable from human-generated materials. Generative AI addresses challenges like resource scarcity, linguistic diversity, and personalized learning experiences, while emphasizing ethical considerations, data privacy, and bridging the digital divide. The future of K-12 education in India will see personalized learning journeys, inclusivity, and empowered educators. A comprehensive policy framework, infrastructure support, and teacher training are needed to realize the full potential of generative AI in shaping the educational landscape.

Cloud computing has now evolved as an unavoidable technology in the fields of finance, education, internet business, and nearly all organisations. The cloud resources are practically accessible to cloud users over the internet to accomplish the desired task of the cloud users. The effectiveness and efficacy of cloud computing services depend on the tasks that the cloud users submit and the time taken to complete the task as well. By optimising resource allocation and utilisation, task scheduling is crucial to enhancing the effectiveness and performance of a cloud system. In this context, cloud computing offers a wide range of advantages, such as cost savings, security, flexibility, mobility, quality control, disaster recovery, automatic software upgrades, and sustainability. According to a recent research survey, more and more tech-savvy companies and industry executives are recognize and utilize the advantages of the Cloud computing. Hence, as the number of users of the Cloud increases, so did the need to regulate the resource allocation as well. However, the scheduling of jobs in the cloud necessitates a smart and fast algorithm that can discover the resources that are accessible and schedule the jobs that are requested by different users. Consequently, for better resource allocation and job scheduling, a fast, efficient, tolerable job scheduling algorithm is required. Efficient Hybrid Job Scheduling Optimization (EHJSO) utilises Cuckoo Search Optimization and Grey Wolf Job Optimization (GWO). Due to some cuckoo species' obligate brood parasitism (laying eggs in other species' nests), the Cuckoo search optimization approach was developed. Grey wolf optimization (GWO) is a population-oriented AI system inspired by grey wolf social structure and hunting strategies. Make span, computation time, fitness, iteration-based performance, and success rate were utilised to compare previous studies. Experiments show that the recommended method is superior.

Cancer is characterized by abnormal cell growth and proliferation, which are both diagnostic indicators of the disease. When cancerous cells enter one organ, there is a risk that they may spread to adjacent tissues and eventually to other organs. Cancer of the cervix of the uterus often initially manifests itself in the uterine cervix, which is located at the very bottom of the uterus. Both the growth and death of cervical cells are characteristic features of this condition. False-negative results provide a significant moral dilemma since they may cause women to get an incorrect diagnosis of cancer, which in turn can result in the woman's premature death from the disease. False-positive results do not raise any significant ethical concerns; but they do require a patient to go through an expensive and time-consuming treatment process, and they also cause the patient to experience tension and anxiety that is not warranted. In order to detect cervical cancer in its earliest stages in women, a screening procedure known as a Pap test is often performed. This article describes a technique for improving images using Brightness Preserving Dynamic Fuzzy Histogram Equalization. To individual components and find the right area of interest, the fuzzy c-means approach is applied. The images are segmented using the fuzzy c-means method to find the right area of interest. The feature selection algorithm is the ACO algorithm. Following that, categorization is carried out utilizing the CNN, MLP, and ANN algorithms.

In this study, recycling of spent coffee grounds (SCG) as a potential feedstock for alternative fuel production and compounds of added value in Turkey was assessed. The average oil content was found (≈ 13% w/w). All samples (before and after extraction) were tested for scanning electron microscopy (SEM), differential scanning calorimetry (DSC), thermogravimetric analysis (TGA), X-ray diffraction (XRD), calorific value, surface analysis and porosity, Fourier transform infrared (FT-IR), and elemental analysis to assess their potential towards fuel properties. Elemental analysis indicated that carbon represents the highest percentages (49.59% and 46.42%, respectively), followed by nitrogen (16.7% and 15.5%), hydrogen (6.74% and 6.04%), and sulfur (0.851% and 0.561%). These results indicate that SCG can be utilized as compost, as it is rich in nitrogen. Properties of the extracted oil were examined, followed by biodiesel production. The quality of biodiesel was compared with American Society for Testing and Materials (ASTM) D6751 standards, and all the properties complied with standard specifications. The fatty acid compositions were analyzed by gas chromatography. It was observed that coffee waste methyl ester (CWME) is mainly composed of palmitic (35.8%) and arachidic (44.6%) acids, which are saturated fatty acids. The low degree of unsaturation provides an excellent oxidation stability (10.4 hr). CWME has also excellent cetane number, higher heating value, and iodine value with poor cold flow properties. The studies also investigated blending of biodiesel with Euro diesel and butanol. Following this, a remarkable improvement in cloud and pour points of biodiesel was obtained. Spent coffee grounds after oil extraction is an ideal material for garden fertilizer, feedstock for ethanol, biogas production, and as fuel pellets. The outcome of such research work produces valuable insights on the recycling importance of SCG in Turkey. IMPLICATIONS: Coffee is a huge industry, and coffee has been widely used due to its refreshing properties. This industry generates large quantities of waste. Therefore, recycling of spent coffee grounds for producing alternative fuels and compounds of added value is crucial. Elemental analysis indicated that coffee waste can be utilized as compost, as it is rich in nitrogen. Coffee waste after oil extraction is an ideal feedstock for ethanol and biogas production, garden fertilizer, and as fuel pellets. The low degree of unsaturation provides excellent oxidation stability. Its biodiesel has also excellent cetane number, higher heating value, and lower iodine value.

In this chapter, the growth and utilisation of digital technologies in the Indian education system during the COVID-19 pandemic have been illustrated. During a pandemic lockdown, all humans live alone in their homes to limit COVID-19 spread. A lot of digital tools have been used in Indian schools and colleges to improve or sustain the teaching and learning processes. However, some impacts and causes have been observed while using new digital platforms during that time. The utilisation of digital tools for the school and higher education systems has been elaborately explained in this chapter. In addition, the future scope and summary of digital technology utilizations have been derived.

Microplastic pollution in water systems is growing, requiring novel detection and analysis methods. This research presents an Internet of Things (IoT)-driven image identification system using Convolutional Neural Networks (CNNs) to detect and quantify microplastics in water samples. The suggested method is more scalable and responsive due to IoT real-time data capture and remote monitoring of water infrastructure. An innovative CNN architecture for image processing allows the system to accurately identify micro plastics. The CNN model is trained and validated using a large dataset of micro plastic-containing water samples. The trained model can recognize various sizes, shapes, and colors of micro plastics, making it responsive to different environmental situations. The IoT architecture also allows image recognition modules in dispersed sensor nodes to cover water systems. Extensive studies prove the system can analyze vast amounts of image data quickly and reliably. Edge computing also minimizes latency and improves micro plastic analysis system responsiveness. The suggested IoT-driven image recognition method for continuous micro plastic pollution monitoring and evaluation in water systems seems promising. Scalability, realtime capabilities, and accuracy make it useful for environmental monitoring agencies and academics trying to reduce microplastics’ influence on aquatic ecosystems. This system advances IoT applications in environmental and pollution management.

The rapid enhancement in social media over the internet generates massive information in real-time scenarios, which has a striking impact on big data analysis. It resulted in the elevated usage of emotions and sentiments in social media. This paper proffers a proficient sentiment analysis technique in Twitter data. The Twitter database is preprocessed includes, stemming, tokenisation, number removal and stop word removal, etc. The preprocessed words are then passed into the HDFS (Hadoop Distributed File System) to reduce the repeated words and are eliminated using the MapReduce technique. The emoticons and the non-emoticons are extorted as features. The resulted features are ranked with their intended meaning. Then, the classification is performed utilising the DLMNN (Deep Learning Modified Neural Network). The experimental results were examined by using the output parameter such as Accuracy, Recall, Precision, F-Score and Execution Time with other conventional techniques such as ANN, SVM, K-Means and DCNN to show the greatest outcome of the proposed model. Evaluation result shows that DLMNN achieved the greatest performance in terms of precision (95.78%), Recall (95.84%), F-Score (95.87%) and Accuracy (91.65%) when compared with the existing models.

Data mining on the web has developed into a simple and crucial tool for finding relevant information. When it comes to file transfers, the World Wide Web is the user’s first choice. Finding useful information and trends in the ever-expanding amount of data available online is becoming ever more challenging and time-consuming. When dealing with massive amounts of textual data often seen online, there are a number of benefits to using the Naive Bayes method for web data mining. Web data mining using the Naive Bayes algorithm seeks to mine massive amounts of textual material on the web for useful patterns, insights, and knowledge. Text classification and categorization tasks are the most common uses of the Naive Bayes method in online data mining. Expert and user-requested data may be difficult to mine from the sea of unorganized and contradictory material that is the World Wide Web. Relevant data (hyperlinks, contents, web use records) is extracted from the web using a variety of mining methods. Internet-centric data mining is a subfield of data science. Structure mining, content mining, and use mining are the three main categories of online data mining. Each of these categories employs a unique set of methods, instruments, strategies, and Naïve Bayes algorithm to mine the web’s vast data stores for useful insights. The results show the density and velocity of web mining using Naïve Bayes algorithm.

Hospital waste management is crucial for healthcare operations, ensuring safe disposal and handling of waste types. The integration of IoT and deep learning technologies offers a promising solution to address waste volumes and environmental concerns. This chapter presents an advanced research overview on hospital waste management using IoT and DL, exploring its potential benefits and applications. IoT-DL integration enables real-time monitoring of waste fill levels, temperature, and other parameters, while advanced DL techniques improve waste collection efficiency, segregation accuracy, and data-driven decision-making for optimized waste management practices. The chapter discusses challenges, benefits, challenges, and considerations in IoT implementation, waste sorting techniques, and ethical and environmental aspects, including sustainability and circular economy principles.

The chapter introduces the concepts of sustainability, green manufacturing, and lean manufacturing, emphasizing their importance in the manufacturing industry. It then highlights the relevance of machine learning techniques in supporting sustainable manufacturing practices. It focuses on the application of machine learning in sustainable manufacturing, presenting case studies that illustrate the use of machine learning algorithms in optimizing energy consumption, reducing waste, and improving process efficiency. It also discusses the challenges and limitations of implementing machine learning techniques in sustainable manufacturing, as well as potential future advancements. Machine learning techniques can be used to achieve sustainable and efficient manufacturing operations, providing valuable insights for researchers, practitioners, and policymakers.

The Internet of Things (IoT) is another useful perspective that brings questions from different disciplines together over the Internet. As more wearable technologies become available on the market, teleworking in Wearable Wireless Body Area Networks (WBAN) is becoming more widespread for Internet of Things-related medical applications. This article proposes a solar-powered wearable touch sensor based on a complete social network (SPWT) and Wi-Fi transmission that connects to a WBAN. Different focal points of the sensor network can be assigned to different points in the frame to determine a person's core temperature and heart rate and understand falls. Software for mobile phones and smartphones that displays sensor data and alerts of falls has also been developed. This software includes a web interface. To increase the service life of the central part of the wearable sensor, the pressure in the middle part mainly uses a full-strength flexible solar-oriented sensor with the result based on superior technology like Maximum power point tracking (MPPT). Preliminary results show that the average element of the wearable sensor performs well, although limited by a fully controlled daylight-based power source. The button-less 24-hour movement is incubated with an initial effect. The proposed local energy storage-based photovoltaic device suggests that WBAN-based long-term continuous medical surveillance is possible because the situation is away from home for a short time per day.

Abstract In this review, the EMI shielding properties of the various carbonaceous fillers are thoroughly reviewed. Electromagnetic interference (EMI) had been a cause of major concern in the live broadcasting, entertainment, aviation and defense industries since vital radio signals could create more interference, which could lead to poor performance. To reduce the effect of EMI, the organic polymeric composites along with the carbonaceous fillers are mostly used since they are flexible, low denser, high mechanical strength, high thermo‐stability, high electrical and thermal conductivity, excellent fracture toughness, and high friction/wear resistance. There are lot of carbon based materials are being used as EMI shielding material in mono and compound form. This review gives a broad understanding of the utilization of carbonaceous fillers in polymer matrixes. Thus, the overall coverage on this carbon based materials and their effectiveness could help the researchers to find right carbon material for suitable application. According to this review, the absorption mechanism is vital to achieve high EMI shielding effect. The fillers such as graphene and CNTs are most preferable EMI shielding filler, according to the vast coverage of previous articles. However, there are more magnetoelectric materials also evolved recently, having combined properties of both conductive and magnetic, yielding high SE at elevated frequencies.

The proposed system integrates Internet of Things (IoT) technologies with Brain-computer interfaces to improve disability-related communication and control. BCIs may directly communicate between the brain and external equipment, providing a lifeline for persons with severe physical restrictions. Incorporating IoT concepts may boost BCI efficacy. Integration of BCI with IoT technology demonstrates unique advantages. BCIs may be connected to the IoT framework to provide a more flexible and comprehensive communication and control environment. Thanks to IoT connection, BCIs can seamlessly interface with assistive devices, home automation systems, wearables, and digital platforms. Interconnectedness expands BCIs’ reach, improves user experiences, and allows creative applications. It shows how IoT-enabled BCIs may help disabled people connect with their surroundings, enhance their quality of life, and recover independence. The study discusses data security, privacy, latency, and device compatibility difficulties while integrating various technologies. This combination promises immediate practicality and future progress in both sectors, creating a more comprehensive and accessible digital world.