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Graphene doped spray dried ceramic nano oxides for high capacity battery electrodes
Electric vehicles or portable electronic devices have come to rely heavily upon electrochemical devices, such as rechargeable batteries with optimum charge discharge characteristics, current ratings, charge-discharge rate (rate capability), cyclability etc. to perform under the expected service conditions. One of the goals of a rechargeable battery materials researcher is to fabricate materials to realize solid-state batteries with high reliability and lithium-air batteries with ultimate capacities. Most of the materials although possess high theoretical energy density values: invariably suffer from inferior cyclic performance. The performance of these batteries is guided by the electrodes within these devices which in turn depend upon the materials used to fabricate them. Chemical composition and its uniformity, consistency in microstructural features, and adequate choice of various layers that may be in the form of coatings to be overlaid on the base materials mostly comprised of ceramic oxides such as oxides of Li doped with niobates, manganates, vanadate etc. with carbon or graphene coated over layers to provide with the suitable interfacial conductivity as electrode materials in Li-ion batteries. The interfacial layers and the mechanism of interfacial phenomena encompassing the grains play a significant role in determining the performance. Optimum microstructure is obtained by choosing the right processing equipment and spray drying the composition in slurry form provides the most optimum solution. Further, spray drying offers high potential for a transfer from a lab scale technology to industrial level extrapolation. In this paper, nano graphene has been spray dried along with nano alumina grains in water media and polyvinyl alcohol binder to ascertain the free flowability, consistency in formation of graphene over layer on alumina grains as well as uniformity in graphene on alumina composition. The free flowing spray dried graphene coated alumina powders were analysed via SEM, EDS and XRD and results are presented. Additional information based on a review conducted on published information on most popular compositions in terms of electrode materials such as in Li-ion, sodium-ion etc have also been included. In the review section the rapidly increasing literature on spray drying of solutions and suspensions are also included. Published under licence by IOP Publishing Ltd. -
Application of artificial neural networks in optimizing MPPT control for standalone solar PV system
Increasing demand of power supply and the limited nature of fossil fuel has resulted for the world to focus on renewable energy resources. Solar photovoltaic (PV) energy source being the most easily available, it is considered to have the potential to meet the ever increasing energy demand. Developing an intelligent system with Artificial Neural Networks (ANN) to track the Maximum Power Point (MPP) of a PV Array is being proposed in this paper. The system adopts Radial Basis Function Network (RBFN) architecture to optimize the control of Maximum Power Point Tracking (MPPT) for PV Systems. A PV array has non-linear output characteristics due to the insolation, temperature variations and the optimum operating point needs to be tracked in order to draw maximum power from the system. The output of the intelligent MPPT controller can be used to control the DC/DC converters to achieve maximum efficiency. 2014 IEEE. -
A Heuristic Model For Personalised Risk Assesment of PCOS
According to WHO 8-13% of women are affected by Polycystic Ovary Syndrome (PCOS) out of which 70% women remain undiagnosed, it is a common endocrine disorder necessitating early diagnosis for timely intervention. In this paper a heuristic model is developed for PCOS prediction, by combining XGBoost and Random Forest through stacking techniques. Class imbalance was addressed using Random Oversampling. Cross-validation demonstrated the meta-model's superior accuracy compared to individual XGBoost and Random Forest models, highlighting its potential for reliable PCOS prediction. It is observed that the best possible results that the meta-model was able to provide was a score of 93.5% which was acquired in the 4th sample, the lowest score was 87.90% attained in the 2nd sample. To finalise the results, the mean accuracy was calculated which is 90.98% with a standard deviation of 1.96. deterministic model offers reproducible results and interpretability, aiding clinical decision-making. Future research could explore additional biomarkers and probabilistic techniques for personalized risk assessment. 2024 IEEE. -
Students Perception of Chat GPT
An artificial intelligence based Chatbot, ChatGPT was launched by Open AI in November 2022. In the field of education, ChatGPT has several benefits as well as challenges. Chat GPT can be considered as an advanced and a powerful tool to enhance the learning experience. It adds value to the education system only when it is used wisely. However, it is important to understand that the challenges must be addressed. It may act as a good source for collating the information, but it is always advised by the researchers that ones own perspective must be added to draw inferences from the output generated by ChatGPT. Our study supports the finding that ChatGPT can be used for the generation of ideas or to learn a new language. It also becomes imperative for the faculties to motivate students to use ChatGPT and add their inferences as well. AI models like ChatGPT can provide assistance, answer questions and provide explanations on various topics, making learning more accessible and tailored to individual needs. With this paper, we aim to provide a more informed discussion around the usage of ChatGPT in education. 2023 IEEE. -
Cloud Computing, Machine Learning, and Secure Data Sharing enabled through Blockchain
Blockchain technologies are sweeping the globe. Cloud computing & secure data sharing have emerged as new technologies, owing to current advances in machine learning. Conventional machine learning algorithms need the collection & processing of training information on centralized systems. With the introduction of new decentralized machine learning algorithms & cloud computing, ML on-device information learning is now a reality. IoT gadgets may outsource training duties to cloud computing services to enable AI at the network's perimeter. Furthermore, these dispersed edges intelligence architectures bring additional issues, also including consumer confidentiality & information safety. Blockchain has been proposed as a viable alternative to these issues. Blockchain, as a dispersed intelligent database, has evolved as a revolutionary innovation for the future phase of multiple industries' uses due to its decentralized, accessible, & safe structure. This system also includes trustworthy automatic scripting running & unchangeable information recordings. As quantum technologies have proven more viable in the latest days, blockchain has faced prospective challenges from quantum computations. In this paper, we summarize the existing material in the study fields of blockchain-based cloud computing, machine learning, and secure data sharing, as well as a basic orientation to post-quantum blockchain to offer a summary of the existing state-of-the-art in these cutting-edge innovations. 2022 IEEE. -
Analysing Collaborative Contributions and Sentiments in the Quantum Computing Ecosystem
Quantum computing, a revolutionary paradigm leveraging the principles of quantum mechanics, has emerged as a transformative technology with the potential to solve complex problems at unparalleled speeds. Within the quantum computing ecosystem, companies and research institutes play pivotal roles in advancing hardware, algorithms, and applications. This research explores the transformative landscape of quantum computing, focusing on key contributors such as Google, IBM, D-Wave, Azure, Amazon, Intel, EeroQ, and IonQ. Through sentiment analysis, topic modelling, and thematic analysis, the study aims to comprehensively understand the current state and trends within the quantum computing ecosystem. The findings unveil an overall positive sentiment and identified topics ranging from cloud computing services to quantum computing advancements. Thematic analysis provides actionable insights, emphasizing collaboration within the ecosystem. Rooted in the analysis of secondary data from key companies' articles, the methodology establishes a robust framework for discerning contributions, collaborations, and strategic orientations in quantum computing. 2024 IEEE. -
MARS: Manual andAutomatic Robotic Sanitization onSocial Milieu
Sanitization is not a new term, but with the evolution of deadly COVID-19, the process came into the limelight quickly. The process was already utilized widely in hospitals, vaccination centers, food processing units, and medicine industries and suddenly became crucial in every domain related to our lives. Even though sanitization is considered the first line of defense against pandemic viruses like COVID-19, it is highly difficult to sanitize every nook and corner of bigger buildings and external structures like airports, railway stations, theaters, institutions, and hospitals. Slight carelessness to eliminate the virus from the sanitization process can reciprocate in the pandemic spread. Our proposed work deals with utilizing the accuracy and precision of robots to effectively sanitize bigger structures. The multi-faceted methodology of the work manages the comprehensive investigation of the robotic unit for the social setting. The concentrate additionally stretches out to refine the standard human behavioral reaction for modern robotic consideration in our lives. This will ease up the process and, at the same time, will reduce the chance of human error. The robotic structure is powered by a 12 V rechargeable battery, which has manual and automation cleaning modes. During manual mode, we control the robot with an Android application installed on the phone and connected with the robot through Bluetooth wireless connectivity. During automation, the mode robot moves in different directions and cleans and sanitizes the area independently. There is an ESP8266-based IoT connection unit to update the overall process for the cloud. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Green and Sustainable Software Model for IT Enterprises
The present study is based on developing a Green and Sustainable software because in the present-day computing devices are used for all kinds of purposes and they consume a lot of energy to perform these services. The ICT sector itself consumes a lot of energy so there is a need to think of alternatives that can reduce the level of energy consumption, thus, green ICT practice can be a good option. There is, however, a scarcity of researches that explains how the maintenance of green knowledge in ICT software development may be implemented. Since we recognize that software development process (SDL) plays an essential role in enabling the ICT community, uncontrolled green knowledge in developing software that would lead to the dilemma of failing to satisfy both the community's business and environmental requirements. Therefore, this research will concentrate on presenting a methodology applying an innovative model for managing the green software development and implementation. Keeping this concern in mind the present paper is going to provide a Green and Sustainable software model which can be used in green ICT practices and will be helpful in reducing the energy consumption used by computers. 2021 IEEE. -
Analysis of Flexoelectricity with Deformed Junction in Two Distinct Piezoelectric Materials Using Wave Transmission Study
Analysis of flexoelectricity in distinct piezoelectric (PE) materials bars (PZT-7A, PZT-6B) with deformed interface in stick over Silicon oxide layer is studied analytically with the help of Love-type wave vibrations. Using the numerical data for PE material, then research achieves the noteworthy fallouts of flexoelectric effect (FE) and PE. The effect of flexoelectricity is compared first between biomaterials of piezoelectric ceramics. Dispersion expressions are procured logically for together electrically unlocked/locked conditions under the influence of deformed interface in the complex form which is transcendental. Fallouts of the research identify that contexture consisting of FE has a noteworthy impact on the acquired dispersion expressions. Existence of FE displays that the unreal section of the phase velocity rises monotonically. Competitive consequences are displayed diagrammatically and ratified with published outcomes. The outcomes of the present research done on both the real and imaginary section of the wave velocity. The comparative study between the two piezo-ceramics bars helps us to understand the properties of one piezo-material over the another and as an outcomes the significance of the present study helps in structural health monitoring, bioengineering for optimizing the detection sensitivity in the smart sensors. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Impact of Machine Intelligence on Clinical Disease Outbreak Prediction
This research paper examines the utilization of Artificial Intelligence (AI) in disease outbreak prediction and its importance in public health. It explores the hurdles associated with predicting disease outbreaks, including data quality and accessibility, ethical considerations, algorithmic bias, and integration and interpretability challenges. The paper presents an overview of AI techniques applied in healthcare and their relevance to forecasting disease outbreaks. Case studies demonstrate the efficacy of AI -based models in predicting infectious diseases, vector-borne diseases, and epidemics/pandemics, employing diverse data sources. The limitations and future prospects of AI in disease outbreak prediction are addressed, accompanied by recommendations for enhancement. In conclusion, the paper highlights AI's potential to revolutionize disease outbreak prediction, leading to proactive public health interventions and improved response strategies. 2023 IEEE. -
Smell Technology: Advancements and Prospects in Digital Scent Technology and Fragrance Algorithms
Smell technology, a rapidly expanding sector of the scent business, aims to digitally replicate and transmit aromas. Its applications include virtual reality, e-commerce, and healthcare. Recent advances in the field include the creation of smell algorithms and the use of artificial intelligence to create more realistic fragrances. Fragrance algorithms are mathematical models that predict the scent of a fragrance based on its chemical composition. They might be used to the perfume industry to streamline the production of perfumes and do away with the need for expensive trial-and-error methods. Artificial intelligence is also being used to create digital representations of fragrances that closely resemble the real thing by analysing the chemical composition of actual odours. A possible benefit of this technology in the healthcare sector is that synthetic odours may mimic the scents of diseases and aid physicians in making more precise diagnoses. Additionally, some companies are developing small devices that can be connected to computers or mobile devices to emit odours on demand, providing users of virtual reality, gaming, and online shopping with a more realistic experience. In spite of these advancements, it is still exceedingly challenging to recreate the complexity of natural scents, which can include hundreds of different components. With more research and development, there is still a tonne of promise for fragrance technology in the future. 2023 IEEE. -
AI Sovereignty in Autonomous Driving: Exploring Needs and Possibilities for Overcoming Challenges
With the development of artificial intelligence, advancements in navigation systems for self-driving cars have become a new direction over the last decade. The inclusion of AI-driven actuators in autonomous vehicles has broken the barriers in terms of real-time high-quality data processing resources, accuracy of decisive actions and generalization of environment-action pairs. Upgradation from a car with no automation to a car with minimal to no human intervention has become a boon of AI, as it resolves most of the transportation problems on roads, including human error, lack of visibility in adverse weather conditions, tiredness of drivers in long journeys, etc. This study focuses on AI-enabled tasks, including object detection and identification, lane detection, notification for lane departure and reinforcement learning from the operational environment. However, there exist serious issues in deploying AI-empowered modules in autonomous cars, as the consumer rights to explain, trustworthiness, and reliability of the machine have not yet met the requirements. Our work explores the needs and prospects of AI sovereignty in autonomous driving by overcoming the aforementioned issues so that the healthy progress of technological society can take care of the future world. 2023 IEEE. -
Sustainable Interior Designing in the 21st Century - A Review
The concept of Sustainable interior designing has gained recognition in recent times. The study focuses on the history, growth and the future of sustainable interior designing. The main aim of the research was to review 102 select journal articles from various Sustainability, Interior designing and combined fields from 2001 all through to 2020, to provide an apprehension on the frequency, study methods, data collection and analysis procedures of the reviewed articles; Alongside providing the readers with an insight on the functionality, aesthetic appeal, client satisfaction and benefits to both environment and the clients. The study also sheds light on the important concepts of Biomimicry, Biophilia and Natural Luxury. The Electrochemical Society -
A Novel Two-Step Bayesian Hyperparameter Optimization Strategy for DoS Attack Detection in IoT
Variations of Hyperparameter in Machine Learning (ML) algorithm effectively strikes the model's performance in terms of accuracy, loss, F1 score and many others. In the current study a two-step hyperparameter optimization approach is represented to analyse selected ML models' performance in detecting specific Denial of Service attacks in IoT. These attacks are Synchronization Flooding Attack at Transport layer, DIS Flooding attack and Sinkhole attack at Network layer. The two-step approach is a combination of Manual Hyperparameter tuning followed by Bayesian Optimization technique. The first stage manually analyses the hyperparameters of ML algorithms by considering the nature of the attack datasets. This technique is quite rigorous as it demands thorough analysis of the dependencies of the nature of datasets with hyperparameter types. At the same time this process is time consuming. The output of the first stage is the ranges of independent hyperparameter values that give maximum accuracy (minimum error rate). In the next stage Bayesian Hyperparameter tuning is used to specifically derive the single set of all hyperparameters values that give optimized accuracy faster than the BO. The input to the second stage is the ranges of individual hyperparameters that gave maximum accuracy in the first stage. The efficiency of the approach is depicted by comparative analysis of training time between the proposed and existing BO. NetSim simulator is used for generating attack datasets and Python packages are used for executing the two-step approach. 2024 IEEE. -
Role of AI in Enhancing Customer Experience in Online Shopping
AI-powered tools and applications may provide customers with a positive, effective, and customized purchasing experience. By studying client preferences and behaviours, AI systems can anticipate future customer needs, improving and personalizing the shopping experience. The main aim of this study is to examine the role of artificial intelligence (AI) on enhancing customer experience. The results of this study revealed that there is a positive significant relationship between AI features like perceived convenience, personalization and AI-enabled service quality and Customer experience. A total of 416 responses were analysed using a structured questionnaire. The findings indicate significant role of trust as factor, mediating the effects of independent variables on customer experience. Data was analysed using T-test, ANOVA and regression. 2024 IEEE. -
Consumer Characteristics and Consumption Patterns of Soft Drinks
A soft drink is generally treated as very common product aimed at a very casual consumption. Normally, not much of attention is paid to this product, which has almost become 'commoditized'. But, a deeper and more careful observation would reveal that soft drinks are strong demographic descriptors of their consumers. Key insights into the characteristics and consumption patterns of consumers can be obtained through an incisive study of the soft drinks market. This research paper makes a concerted effort at unearthing the demographic details and consumption contours of the soft drink users in Kanpur, Agra, Varanasi, Allahabad, and Lucknow - the five representative cities of Uttar Pradesh, the most populous state of India. It has been conclusively established through this research that the residents of these five cities - which are demographically similar in nature - exhibit varying consumption patterns when it comes to soft drinks. It was also found that demographic variables like age, gender, educational qualification, income, and marital status do not significantly impact the consumption of soft drinks, whereas employment status is a key influencer of the same. 2021 IEEE. -
A Brief Review on the Role of Blockchain in Supply Chain Management
Blockchain is a proficient technology when used in combination with other intelligent technologies which gives an opportunity to an organization to rethink about improvement of their supply chain internal and external processes. It helps in improvement of transparency and provenance by removing shortfalls and building a better organizational control overall. However, blockchain faces numerous challenges, e.g., transaction speed, decentralization, scalability, interoperability, and lack of standardization that could affect its adoption across organizations. However, a greater number of research are required to overcome the governance, standardization, and technological challenges involved within. Concisely, blockchain in supply chain is still in initial phase, many improvements are needed for better adaptation of blockchain using Machine Learning, Neural Network algorithms to make optimized computation decision of blockchain framework. In this paper, we studied and discussed about blockchain and its type, consensus mechanism, blockchain in supply chain, key issues of blockchain and supply chain and intelligence in blockchain. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Unveiling the pattern of PhishingAttacks using the Machine Learning approach
This study introduces a unique approach to strengthening cybersecurity by combining advanced models for real-time detection of phishing websites. A classifier is trained to discern patterns associated with legitimate and phishing URLs, leveraging a carefully organized labeled dataset. The model in this paper forms the foundation for a real-time detection system, providing users with real-time information on potential phishing threats. Integrating an adaptive decision-making algorithm improves decision-making adaptability, particularly in scenarios challenging the model's confidence. A user feedback loop ensures the continuous learning and refinement of the system, aligning it more closely with user expectations. The future scope of this research involves exploring advanced models, improving explainability, and incorporating dynamic features for enhanced detection. Adaptive policies, large-scale deployment, and ethical implications are pivotal for real-world applicability. In conclusion, this study contributes to advancing phishing detection methodologies and lays the groundwork for future innovations in cybersecurity. The collaborative efforts of academia, industry, and cybersecurity stakeholders arenecessaryfor realizing the full potential of this paper and ensuring a safer online platform for users. 2024 IEEE. -
Assessing Human Stress Through Smartphone Usage
Stress occurs in a human being when they are faced with exigent situations in life. Assessing stress has been always challenging. Smartphones have become a part of everyones day-to-day activity in the present time. Considering humansmartphone interaction, sensing of stress in an individual can be assessed as todays youth spends most of their time with smartphones. Taking this into consideration, a study is carried out in this paper on assessing stress of an individual based on their interaction with the smartphone. In this work, humansmartphone interaction features, like swipe, scroll, and text input, are examined. Text input is incorporated by disabling the autocorrection and spelling checker features of the keyboard. Moreover, sensor data is used by Google activity recognition API to analyze the physical activity of the individual to assess the stress level. 2019, Springer Nature Singapore Pte Ltd. -
Assessing Academic Performance Using Ensemble Machine Learning Models
Artificial Intelligence (AI) shall play a vital role in forecasting and predicting the academic performance of students. Societal factors such as family size, education and occupation of parents, and students' health, along with the details of their behavioral absenteeism are used as independent variables for the analysis. To perform this study, a standardized dataset is used with data instances of 1044 entries and a total of 33 unique variables constituting the feature matrix. Machine learning (ML) algorithms such as Support Vector Machine (SVM), Random Forest (RF), Multilayer Perceptron (MLP), LightGBM, and Ensemble Stacking (ES) are used to assess the specified dataset. Finally, an ES model is developed and used for assessment. Comparatively, the ES model outclassed other ML models with a test accuracy of 99.3%. Apart from accuracy, other parameters of metrics are used to evaluate the performance of the algorithms. 2023 IEEE.