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Demystifying Data Justice: Legal Response To India's Privacy And Security Standards: Challenges In Cloud Computing
Data is the new oil of this economy. Cloud Computing acts in the capacity of storing databases, in operational analytics, networking and intelligence. Indian cloud computing market is valued at 2.2 billion dollars, which is said to scale by 30 percent in 2022. It's therefore pertinent to understand Indian's data protection landscape in the light of Personal Data Protection Bill, 2018 to answer the questions of ownership, controlling, processing of data in order to reflect upon the liability, obligations, and compliances by intermediaries, dispute resolution forums, data portability and indemnification. The authors will explore by means of doctrinal method, the challenges posed on the content regulatory mechanism for the internet architecture which paves responsibility of data classification into lawful and unlawful, with the exception of section 79 of Information Technology Act. The authors will further examine the encryption standard tools exhibiting data security and the obstacles created by the 40-bit limit encryption standard as part of the DoT's telecom licensing conditions and section 84A IT Act, 2008, to provide suggestions towards pragmatic delimitation. Cloud computing being the next growth frontier of the IT industry, makes it more evident to enable cloud forensics in entrusting with investigations and establishing confidence within the end-users. Goal 16 of SDG's deal with Promote just, peaceful and inclusive societies. The Electrochemical Society -
Green Synthesized Fluorescent Nano-Carbon derived from Indigofera Tinctora (L.) leaf extract for sensing of Pb2+ ions
Plant-based synthesis of nanomaterials is a more reliable method since it is easy, quick, and environmentally friendly, and it does not require any specific conditions, unlike other methods. For the first time, we report the sensing of metal ions using a fluorescent nano-carbon material via a plant-based synthesis from the medicinal plant, Indigofera Tinctora (L.) (IBLH). This nanomaterial from the leaf extract of IBLH was synthesized by hydrothermal assisted green synthesis method. The as-synthesized sample was characterized by various spectroscopic techniques for confirming the formation of nano-carbon material. Optical studies revealed that IBLH was influential in determining toxic heavy metal ions (Pb2+). Detection of Pb2+ was observed from a range of 1 Molar to as low as 1Nano-Molar using IBLH as the probe. Stern-Volmer plot exhibits the progressive detection of the metal ion, proving that the IBLH nano-carbon material is capable of progressive sensing of various heavy metal ions. The Electrochemical Society -
Survey on Malicious URL Detection Techniques
Crimes in the cyberspace are increasing day by day. Recent cyber threat defense reports states that 80.7% of the systems are compromised at least once in 2020. Cyber criminals taking the pandemic situation as an opportunity for the mass attack through malicious URL circulated by email or text messages in social media. Performing cyber-attacks through malicious URLs is the handy method for the cyber criminals. Protecting from such attacks requires proper awareness and solid defense system. Some of the common approaches followed by the cybercriminals to deceive the victims are 1. Phishing URLs which is very similar to the legitimate URLs. 2. Redirecting URLs 3. Using JavaScript, redirects to the phishing URL when user interacts with webpage 4. Social engineering etc. As soon as the novice internet users clicks on the malicious URL link, cyber criminals can easily steal personal information or install malware on their device to get additional access. Recently malicious URLs are generated algorithmically and uses URL shortening service to evade the existing security setup such as firewall and web filters. In literature, the researchers have proposed several ways to detect the malicious URLs but, new attack vectors that are introduced by the cyber criminals can easily bypass the security system. The purpose of this paper is to provide an overview of various malicious URL detection techniques which includes blacklist based, rules based, machine learning and deep learning-based techniques. Most importantly, the paper discusses the common features used by the detection system from webpages to classify the URL as malicious or benign and various performance metrics. This will encourage the new researchers to bring out the innovative solutions. 2022 IEEE. -
Novel PAPR Reduction in UFMC system for 5G Wireless Networks Using Precoding Algorithm
The Universal Filtered Multi-carrier (UFMC) system is promising alternative multicarrier modulation scheme for fifth generation (5G) cellular networks. UFMC systems offer many advantages such as larger spectral efficiency, robustness, lower latency and minimizing out of band emission. However, the most serious problem in the UFMC system is high peak to average power ratio (PAPR). This high peak signal is seriously harmed by the high power amplifier (HPA). Therefore, this research presents a novel Square Root raised Cosine function (SRC)-Precoding method introduced to reduction of PAPR. A performance analysis of various methods being examined upon in terms of CCDF of PAPR and the BER. The Simulation result shows that the proposed approach can effectively reduce the PAPR 6dB compared to standard UFMC. Moreover, the bit error rate (BER) study of the UFMC model indicates that the proposed approach significantly improves 15 dB compared with conventional UFMC systems. 2022 IEEE. -
Multiple Safety Equipment's Detection at Active Construction sites Using Effective Deep Learning Techniques
The safety of human labour is the most important thing in this era no matter where the labour force works. Governments and various NGOs focus on ensuring the delivery of the top safety to the labor class of the country. One such example is the working of the labour force at huge construction sites. For them a lot of work includes a huge amount of risks hence following full safety is the need of the hour for the workers working at construction sites. In order to deal with proper monitoring of the safety being followed at Construction sites. In order to make use of the latest technologies in this field also some of the good object detection models can be used for detecting the safety equipment of the workers which include things like Hard Hats, Masks, Vest, Boots. A lot of research is going on in improving the detection speed and accuracy of objects using state-of-the-art techniques in Computer Vision and this could lead to providing better results. Based on the available research and compute resources future work can be done to improve the results in this specific domain also. 2022 IEEE. -
Ethical Tenets of Stock Price Prediction Using Machine Learning Techniques: A Sustainable Approach
The visible decline of ethics primarily gets reflected in financial markets, as it portrays human actions and sentiments in numerical terms than any sector. Accuracy in Stock market prediction remains inefficient due to many known and unknown variables. Academia and industry recently relied on ML at large to track the market and monetise the movements. The norms of fairness, accuracy, dependability, transparency in financing are left unattended in ML prediction models with assumptions far from reality. This study focuses on the ethical dimension of Machine Learning models and generates a sustainable framework for investors. Specifically, the Sustainable Development goals (SDG) can enhance the prediction models in ML with improved efficiency. Along with SDG, this research broadens the variables' horizon of prediction in ML of computer science domain with concepts of Socially responsible Investing (SRI), Environmental Social and Corporate Governance (ESG), and Carbon footprints. With One hundred fifteen articles reviewed, the proposed framework ensures sustainability in investments at the grassroots level. The Electrochemical Society -
Multimodal Classification on PET/CT Image Fusion for Lung Cancer: A Comprehensive Survey
Medical image fusion has become essential for accurate diagnosis. For example, a lung cancer diagnosis is currently conducted with the help of multimodality image fusion to find anatomical and functional information about the tumor and metabolic measurements to identify the lung cancer stage and metastatic information of the disease. Generally, the success of multimodality imaging for lung cancer diagnosis is due to the combination of PET and CT imaging advantages while minimizing their respective limitations. However, medical image fusion involves the registration of two different modalities, which is time-consuming and technically challenging, and it is a cause of concern in a clinical setting. Therefore, the paper's main objective is to identify the most efficient medical image fusion techniques and the recent advances by conducting a collective survey. In addition, the study delves into the impact of deep learning techniques for image fusion and their effectiveness in automating the image fusion procedure with better image quality while preserving essential clinical information. The Electrochemical Society -
Nature's Lament: A Comparative Psychoanalytical Reading of Childhood Trauma in Select War Narratives
Sustainable Development has become an inevitable need of the hour. This paper problematizes the trauma of children as represented in the narratives, Beasts of No Nation by Uzodinma Iweala and A Long Way Gone by Ishmael Beah. The incomprehensibility of trauma, it's varied representation in fiction, dissociation of child psyche, and its detrimental effect on children is substantiated using psychoanalytic theory of trauma proposed by Cathy Caruth and contemporary trauma theorists. The paper argues the atrocities children are forced to be involved into, causes profound trauma in themselves leading to, encumbering of sustainable developmental goals. A comparative study of interpretive textual analysis is employed to study the havoc the society endears as a result of war, that wrecks the child, hindering the overall sustainable development. As it voices out the voiceless trauma of children the paper also aims in divulging the decisive influence of the select literary narratives in sensitizing the society in achieving societal as well as environmental sustainability. The Electrochemical Society -
Do Millennial Exhibit Environmentally Responsive Consumption BehaviorsA Study on Determinants of Green Purchase Decision?
The purchase behavior of green products is largely affected by the intention-action gap and skepticism present among consumers. The purpose of this study was to analyze the various factors that affect the purchase behavior of green products among millennials. The practical benefit of this research is that it will assist in the convergence of green marketing and environmental consumer behavior theories. The theory used in the study is the theory of planned behavior. It helps to understand the specific behaviors of consumers as a possibility of a particular behavioral intention. For this purpose, we identified five constructs, namely, Environmental Concerns and Belief (ECB), Eco-Labelling (EL), Green Packaging and Branding (GPB), Green Product, Premium, and Pricing (GPPP), and Consumers Beliefs Towards the Environment (CBTE). These constructs have helped in identifying and analyzing the various factors that affect the purchase behavior of green products among millennials. We analyzed the purchase behavior of green products using a questionnaire approach. For this descriptive study, there were 251 millennials as our respondents who were chosen using the convenience sampling technique. The data was collected through a structured questionnaire via Google form and was analyzed using regression analysis, correlation. It was found that the key factors of green marketing such as Environmental Concerns and Beliefs (ECB), Green Packaging and Branding (GPB), and Green Product, Premium and Pricing (GPPP) have a positive influence on Consumers Beliefs Towards the Environment (CBTE). It implies that by increasing the spending on green packaging and branding there will be a positive effect on consumers environmental beliefs. On the other hand, Eco-Labelling (EL) has a negative influence on Consumers Beliefs Towards the Environment (CBTE) and this is caused by skepticism present among millennials. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
An Intelligent Recommendation System Using Market Segmentation
Electronic commerce, sometimes known as E-Commerce, is exchanging services and goods over the internet. These E-Commerce systems generate a lot of information. To solve these Data Overload issues, Recommender Systems are deployed. Because of the change to online buying, companies must now accommodate customers needs while also providing more options. The strategies and compromises of common recommender systems will be discussed to assist clients in these situations. Recommendation algorithms generate lists of things that the user have been previously using (content filtering) or develop recommendations and analyzing what items users purchase and identify similar target users (collaborative filtering). To assist clients in these situations, The Apriori algorithm, standard and custom metrics, association rules, aggregation, and pruning are used to improve results after a review of popular recommender system strategies that have been used. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
University-Community Collaboration for A Sustainable School-Based Program for The Holistic Education and Wellness of Adolescents
Adolescents have been particularly affected by the COVID-19 pandemic and the closure of schools that are already struggling to carry out their mission of quality education and holistic well-being of students. Research suggests that community-collaborative schools are improving students' academic engagement and reducing learning barriers. When communities and universities are involved in holistic education, it benefits all the stakeholders by enhancing mutual learning and strengthening both. Community members' involvement for student development encourages students and their families to be more involved in community-service initiatives. The paper reports DREAMS, a multi-stakeholder partnership (schools, universities and communities) after-school mentoring model's sustainability. The study identifies and delineates how the model has incorporated the Sustainable Development Goals (SDGs) calling for Good Health and Well-being (SDG-3), Quality Education (SDG-4), Sustainable Cities and Communities (SDG-11) through Partnerships to Achieve its Goals (SDG-17) and proposes it as a sustainable afterschool plan for the post COVID scenario. The Electrochemical Society -
Sentiment Analysis On Covid-19 Related Social Distancing Across The Globe Using Twitter Data
Covid 19 pandemic has devastated the lives of several people across the globe. Social distancing is considered a major preventive measure to stop the spread of Covid 19. The practice of social distancing has caused a sense of loneliness and mental health problems in society. The aim of this study is to consider global tweet data with social distancing keywords for analyzing the sentiments behind them. Classification of tweets as positive or negative is carried out using Support Vector Machine and Logistic Regression. The Electrochemical Society -
Effect of Doping in Aluminium Nitride (AlN) Nanomaterials: A Review
Piezoelectric materials can generate electrical charges when subjected to mechanical pressure through the piezoelectric effect. In addition to generating electricity from environmental vibrations, they are also used as nano energy generators for micro electro mechanical systems (MEMS). Aluminum Nitride (AlN) with a doping element exhibits unique physical and chemical properties. It is used to manufacture many electromechanical devices. They are ideal candidates for many applications, including MEMS resonators and microwave filters, due to their large piezoelectric coefficient and low resistance. A number of material properties led to its selection, including high thermal conductivity, good mechanical strength, high resistance, corrosion resistance, and the largest piezoelectric coefficient. A piezoelectric coefficient d33 characterizes the piezoelectric response of AlN thin films. By doping this material, a wide range of applications have been explored. The Electrochemical Society -
Fortitude, and Sense of Coherence in achieving Financial Resilience and Financial Health of Micro and Small Entrepreneurs
The COVID 19 pandemic has brought economic shock s all over the world. India is not an exception to this. The pandemic has made the lives of poor, and downtrodden people, micro, and small entrepreneurs miserable. Micro and small enterprises struggle to bounce back financially and to achieve financial health. Micro and small entrepreneurs face many problems such as no adequate income and savings, debt repayment, rising costs, lack of funds to run the business, financial and mental stress, uncertain future, and so on. Despite these problems, the micro and small enterprises move on steadily to achieve the goal of financial health. What makes them move on steadily? How do they manage their resources to achieve financial resilience? To seek answers to these questions, this study would like to examine the role of fortitude and sense of coherence in achieving financial resilience and financial health of micro and small entrepreneurs. The Electrochemical Society -
Sentimental Analysis on Online Education Using Machine Learning Models
Sentimental analysis is a simple natural language processing technique for classifying and identifying the sentiments and views represented in a source text. Corona pandemic has shifted the focus of education from traditional classrooms to online classes. Students mental and psychological states alter as a result of this transition. Sentimental study of the opinions of online education students can aid in understanding the students learning conditions. During the corona pandemic, only, students enrolled in online classes were surveyed. Only, students who are in college for pre-graduation, graduation, or post-graduation were used in this study. To grasp the pupils feelings, machine learning models were developed. Using the dataset, we were able to identify and visualize the students feelings. Students favorable, negative, and neutral opinions can be successfully classified using machine learning algorithms. The Naive Bayes method is the most accurate method identified. Logistic regression, support vector machine, decision tree, and random forest these algorithms also gave comparatively good accuracy. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Impact of Urban Environmental Quality, Residential Satisfaction, and Personality on Quality of Life among Residents of Delhi/NCR
Environmental quality and Sustainability seek to preserve, enhance and protect our environmental resources that directly aim at providing an amicable quality of life and sustainable development for the upcoming generations. Considering the hazardous environmental urban quality in Delhi NCR, air pollution is the topmost factor deteriorating health of the population in general. The urban air database by WHO reports Delhi exceeding the maximum PM10 limit by almost 10-times at 292 ?g/m3. Noticing that an individual's surroundings have an enormous value in human lives, the study aimed at understanding the impact of urban environmental quality, residential satisfaction, and personality on the quality of life among residents of Delhi NCR. In addition, we also track the environmental worldviews to attitudes on pro-environmental behavior in understanding sustainability. The results from the SEM model indicated that one index rise in RESS lead to a fall in quality of life by 0.029-point value whereas one index rise in personality could enhance the quality of life by 0.15-point value. Pro-Environmental Behaviors and Urban Environmental factors did not showcase any significant impact on the quality of life. The Electrochemical Society. -
Online Education and English Language Learning Among Tribal Students of Kerala
Kerala, a South Indian state has tribal population in all her districts. About 1.5% of the total population of the state constitute tribal population. They depend upon natural environment and resources for their survival. Children from the same community usually depend on government funded schools for their education. Education for this deprived section during COVID 19 Pandemic was a massive exclusion and an uphill task. Digital divide and medium of communication (Standard Malayalam) were some of the critical concerns to knowledge acquisition among tribal children. This paper primarily focuses on the challenges of online education among tribal students with a clear emphasis on the English language acquisition. This study was conducted in four most tribal populated districts of the State, namely, Wayanad, Malappuram, Palakkad, and Idukki. This is a qualitative explorative study that explores the experiences of the tribal students' English language learning challenges from the teachers' perspective in these districts. The Electrochemical Society -
CBMIR: Content Based Medical Image Retrieval Using Hybrid Texture Feature Extraction Method
Due to the revolution of digital era in the medical domain at various hospitals across the world, the online users on the internet access have been increased. So the amount of collections of digitized medical images has grown rapidly and continuously. As well it is ratting significant to mention that the images are globally used by radiologists, professors in medical colleges and Lab technicians, etc. These Images are increasingly applied to communicate information about patient history. In this context, there is a necessity to develop appropriate systems to manage these medical images in storage and retrieval for diagnosis of the patient information. Another big issue is the convolution of image data and that can be interpreted in different ways. In order to manipulate these data and establish policies to its content is very tedious job. This will raise another big question. These issues motivated the researchers to give more focus on the image retrieval area whose goal is trying to solve those problems to provide an efficient retrieval system to the user community. In this perspective, this work has been proposed to facilitate radiologists, professors in medical colleges, lab technicians, and all other medical image user communities for their purpose for easy access from the remote location. 2022 IEEE. -
Secure Authenticated Communication Via Digital Signature And Clear List In VANETs
Vehicular ad hoc network (VANET) plays a vital role in the intelligent transportation system(ITS), When a vehicle receives a message through network, the CRL (certificate revocation list) checking process will operate before certificate and signature verification. After successful authentication,a CRL list is created based on authentication. This CRL is used to verify whether a vehicle node can be permitted for communication in the VANET network. But when using CRL, a huge amount of storage space and checking time is needed. So we proposed a method without CRL list, but mentions a key management list to overcome large storage space and checking time even it reduce the access delay too. For the access permission we can do an authentication system based digital novel signature authentication(DNSA) for each vehicles in the vanet with the RSU unit or with other participant node vehicles in the communication as per the Topology.So we can perform an efficient and secured communication in VANET. The Electrochemical Society -
An Efficient Multi-Modal Classification Approach for Disaster-related Tweets
Owing to the unanticipated and thereby treacherous nature of disasters, it is essential to gather necessary information and data regarding the same on an urgent basis; this helps to get a detailed overview of the situation and helps humanitarian organizations prioritize their tasks. In this paper, "An Efficient Multi-Modal Classification Approach for Disaster-related Tweets,"the proposed framework based on Deep Learning to classify disaster-related tweets by analyzing text and image contents. The approach is based on Gated Recurrent Unit (GRU) and GloVe Embedding for text classification and VGG-16 network for image classification. Finally, a combined model is proposed using both text and image modules by the Late Fusion Technique. This portrays that the proposed multi-modal system performs significantly well in classifying disaster-related content. 2022 IEEE.