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Security Aspects for Mutation Testing in Mobile Applications
Due to the increase in the number of Android Platform Devices, there are more and more applications being developed across various domains. It is interesting to see the involvement of bugs/crashes even in the deployed applications even though it has been through various test phases. Unit tests are essential in a well-trusted testing environment; however, it does not guarantee that the range of test caries every component of the application. This writes up discusses the overview of mutation testing method concerning Android Applications. Even though mutation testing is found out to be very effective in other applications, it is not that easy to implement the same for an Android Developed Application because of additional resources it would hold. Further, various measures for mutation testing are discussed with types of mutant operators, tools etc. The current studies of mutation analysis mainly focus on testing all the functionalities irrespective of the resource usage. However, the target of the future mutation tests must be also to evaluate the efficiency of the applications under the same test cases. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Security Intensification using Blockchain coupled with Internet of Things: Proposal, Challenges and Anatomization
Internet of things is an important part of our day-to-day life where all things are connected in the network with the internet. The number of devices linked to the network grows steadily each day in recent years. The innovation in the manufacturing industry also the reason for the production of different devices that uses various technologies to make a possible connection between the devices. Even though the Internet of Things has been developing and demonstrating its potential in recent years, its security when connected to the internet is in doubt. Blockchain is a disruptive technology that provides security to their network without tampering with the data in the network. Researchers and experts have recommended using the blockchain to address security vulnerabilities in the Internet of Things. In this paper, we have analyzed some of the issues which are occurring while integrating blockchain into the Internet of things. The major issues were discussed and which will be helpful to move towards the research direction to solve those problems. 2022 IEEE. -
Security mechanisms in cloud computing-based big data
In the existent system, data is encrypted and stored when passed to the cloud. During any operations on the data, it is decrypted and then the computation is done. This decrypted data is vulnerable and prone to be misused. After the computations are done, the data and the result are encrypted and stored back in the cloud. This creates an overhead to the system as well as increases time complexity. With this chapter, the authors aim to reduce the overhead of the systems to perform repeated encryptions and decryptions. This can be done by allowing the computations to happen directly on the encrypted text. The result obtained by performing computations on encrypted data will be the same as the ones done on the original plain text. This new security solution is fully fit for processing and retrieval of encrypted data, effectively leading to the broad applicable project, the security of data transmission, and the storage of data. The work is secured further with additional concepts like probabilistic and time stamp-based encryption processes. 2021, IGI Global. -
Security mechanisms in cloud computing-based big data
In the existent system, data is encrypted and stored when passed to the cloud. During any operations on the data, it is decrypted and then the computation is done. This decrypted data is vulnerable and prone to be misused. After the computations are done, the data and the result are encrypted and stored back in the cloud. This creates an overhead to the system as well as increases time complexity. With this chapter, the authors aim to reduce the overhead of the systems to perform repeated encryptions and decryptions. This can be done by allowing the computations to happen directly on the encrypted text. The result obtained by performing computations on encrypted data will be the same as the ones done on the original plain text. This new security solution is fully fit for processing and retrieval of encrypted data, effectively leading to the broad applicable project, the security of data transmission, and the storage of data. The work is secured further with additional concepts like probabilistic and time stamp-based encryption processes. 2021, IGI Global. -
Security Standards in Serviced Apartments - With Special Reference to Bangalore and Chennai (South India) - An Analysis
International Journal of Research in Computer Application & Management, Vol-2 (11), pp. 130-135. ISSN-2231-1009 -
Security Threats and Privacy Issues in Cloud Data
The quick advancement of Web-based applications has led to a huge amount of information being scanned and gathered for business examination or scholarly research purposes, which may disregard individual protection. Organizations, industries and individuals data are at stake. In this paper, utilizing on the Web Personal Health Record (PHR) as contextual analysis, first demonstrate the need of inquiry ability approval that lessens the security introduction coming about because of the list items, and build up a versatile structure for authorized private keyword Search (APKS) over encoded cloud information. This particular model proposes two novel answers for APKS given on-going cryptographic crude, hierarchical predicate encryption (HPE). Our answers empower efficient multi-dimensional watchword looks with a run question, permit assignment and renouncement of hunt abilities. Additionally, the proposed system improves the question protection which conceals clients inquiry watchwords against the server. Actualize our plan on an advanced workstation, and exploratory outcomes exhibit its appropriateness for reasonable use. Privacy has seen advancement lately as information mining of the datasets in a dispersed huge information condition has turned into a successful worldwide business which is none other than data management or data analytics which ensures the security of data. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
SECURITY VIEWPOINT IN ARTIFICIAL INTELLIGENCE-BASED SYSTEMS
Artificial intelligence (AI) is playing a key role in recent times linked with automation in almost all the fields on human interference and inventions. Industry 4.0 also signifies automation as one of the major aspects from its allies. However, on the other hand, issues and security concerns are at high priority in these systems and often are seen ignored as the AI systems are evolving as state-of-the-art technology. In this chapter, we focus on elaborating security vulnerabilities in AI-based infrastructure, explain the relation between AI attacks and cyber-attacks, and describe the sustained AI systems with inherited resilience. Case study is also considered to elicit threats and vulnerable issues associated with AI-based systems. Securing artificial intelligence and its associated techniques help in better outcomes which are tightly coupled with human monitored and controlled environments. AI algorithms need to be checked for accuracy in prediction model contexts on the evolution of technology for sustaining adversarial attacks. 2026 by Apple Academic Press, Inc. -
Segment Anything Model (SAM) to Segment lymphocyte from Blood Smear Images
Automated lymphocyte segmentation from smear images plays an important role in disease diagnosis and monitoring, aiding in the assessment of immune system function and pathology detection. This study proposes an approach for lymphocyte segmentation utilizing Segment Anything Model (SAM) which is a deep learning model. Our method leverages a pre trained SAM architecture and fine-tunes it on a custom dataset comprising smear images containing lymphocytes. The pretrained model's ability of versatile segmentation combined with fine-tuning on the specific dataset enhances its performance in accurately identifying lymphocyte boundaries. We evaluate the proposed approach on a diverse set of smear images, demonstrating its effectiveness in segmenting lymphocytes with impressive IOU score and Dice Score. SAM deep learning model, fine-tuned on custom datasets, holds promise for robust and efficient lymphocyte segmentation from blood smear images. 2024 IEEE. -
Segmentation and identification of MRI Brain segment in digital image
Brain image segmentation is important in the area of clinical diagnosis. MRI Brain image segmentation is time consuming and there is always a chance of occurrence of error when the segmentation is done manually. It is always possible to detect the infected tissues easily in the current medical field. However, the accuracy and the characteristics of abnormalities of the tissues are not precise. In the past, many researchers have identified the drawbacks of manual segmentation and hence proposed the semiautomatic and fully automatic segmentation methods in the field of medical imaging. The amount of precision about the detection of defective tissues leads to acceptance of a particular image segmentation method. In this article three segmentation methods are hybridized to get the optimum extraction of the region of interest (ROI) in brain MRI image. Further, the region properties of segment is extracted and stored as knowledgebase. The proposed algorithm integrates multiple segmentation methods and identifies the Brain Outer layer in MRI image. This identification AIDS medical experts for optimum diagnosis of defective tissues in the brain. IAEME Publication. -
Segmentation and Recognition of E. coli Bacteria Cell in Digital Microscopic Images Based on Enhanced Particle Filtering Framework
Image processing and pattern recognitions play an important role in biomedical image analysis. Using these techniques, one can aid biomedical experts to identify the microbial particles in electron microscopy images. So far, many algorithms and methods are proposed in the state-of-the-art literature. But still, the exact identification of region of interest in biomedical image is a research topic. In this paper, E. coli bacteria particle segmentation and classification is proposed. For the current research work, the hybrid algorithm is developed based on sequential importance sampling (SIS) framework, particle filtering, and Chan–Vese level set method. The proposed research work produces 95.50% of average classification accuracy. 2019, Springer Nature Singapore Pte Ltd. -
Segmentation of ancient and historical gilgit manuscripts
The Gilgit manuscripts belong to fifth century A.D. and are oeuvre of texts which deal with Buddhist work. It is one of the oldest manuscripts in the world and is considered to be a milestone in the history of Buddhist works in India. It is a collection of both official and unofficial Buddhist works which are believed to have helped in the evolution of many literatures including Chinese, Japanese, and Sanskrit. Since this manuscript is almost seventeen centuries old it has not been able to fully decipher the text yet. It has been laminated by the National Archives of India which proves it is one of the most important literatures concerning India. In this paper, we perform character- based image segmentation on Gilgit manuscript in order to simplify and to better identify character in the image of the manuscript. The employed method gives an accuracy of nearly 87%. Springer India 2016. -
Segmentation of overlapping leukemic cells in histopathological images using HSV- based watershed transformation
Accurate segmentation of white blood cells (WBCs) is essential for computer-aided diagnosis, as overlapping and densely clustered cells often present significant challenges. This work introduces a hybrid framework for segmentation that proposes a fusion of hue and saturation in the Hue Saturation Value (HSV) domain. Gaussian smoothing, Otsu thresholding, and Morphological refinement is employed to enhance cell contrast and eliminate noise. A marker-based watershed algorithm is subsequently applied for accurate separation of overlapping WBCs. Evaluation on the ALL-IDB2 dataset confirms the methods capability through achieving a Dice Similarity Coefficient(DSC) of 0.8929 and an Intersection over Union (IoU) of 0.8099 to produce well-defined cellular boundaries. The novelty of this study lies in the integrated hue-saturation fusion and marker-based watershed strategy, offering improved boundary localization and reliable segmentation of overlapping WBCs. Bharati Vidyapeeth's Institute of Computer Applications and Management 2025. -
Segmentation technique for medical image processing: A survey
Segmentation is one of the popular and efficient technique in context to medical image analysis. The purpose of the segmentation is to clearly extract the Region of Interest from the medical images. The main focus of this paper is to review and summarize an efficient segmentation method. While doing the comparison study on segmentation methods using the Support Vector Machine, K-Nearest Neighbors, Random Forest and the Convolutional Neural Network for medical image analysis identifies that Convolutional Neural Network works efficiently for doing in-depth analysis. The Convolutional Neural Network can be used as segmentation technique for achieving the high accuracy on medical image analysis. 2017 IEEE. -
Segregating direct and indirect dimensions in ecosystem services valuation: The case of a coastal wetland ecosystem of south india
This paper provides insights into the multiple (direct and indirect) benefits of Kuttanad coastal wetland ecosystem in Kerala. Total annual direct ecosystem services generated from the wetlands are INR 8.45 billion or USD 0.11 billion per annum at 2020 prices. The estimates of the case study indicate that the annual value of indirect ecosystem services is thrice of direct provisioning services (Rs 22.52 billion or USD 0.31 billion per annum at 2020 prices). The valuation study would improve the knowledge and awareness of economic importance of wetland ecosystems among the various stakeholders including the policy makers of the society and their sustainable management to benefit the society. 2021 Ecological Society of India. All rights reserved. -
Segregation and researcher's positionality: Challenges of conducting policy ethnography in Southern polarized settings
Researchers conducting policy ethnography in conflict environments are faced with a valuable ethical dilemma is there an ethical standard to determine how a dataset should be pursued in the field? What if the method of pursuing data carries the potential of possibly disrupting one's rapport with the community and being perceived as a partisan ideologically driven researcher with ulterior motives? This question becomes more pronounced in socio-legal, conflict and public policy research in spatially polarized settings of the South. In these settings, knowledge is co-produced through one's own positionality and the nuances of grey areas that do not often feature in aggregated datasets. Scholarship on positionality has questioned whether scholars should explicate their position on the field by pointing towards the intentional or unintentional perpetuation of hierarchies. This paper situates itself in the positionality debate with reference to castelessness in socio-legal research through nine months of ethnographic fieldwork in a Southern spatially polarized setting. It grapples with an emerging contrasting view of whether researchers should at all engage in explicating their positionality. The paper argues that data is a socio-spatial product. It is to suggest that the production of data in conflict settings is informed by the spatial dynamics of social relations that emerge in the co-production of knowledge, and the researcher's reflexive positionality that itself impacts the outcome of data that emerges. 2025 The Author(s). Journal of Law and Society 2025 Cardiff University (CU). -
Seismic Activity-based Human Intrusion Detection using Deep Neural Networks
Human intrusion detection systems have found their applications in many sectors including the surveillance of critical infrastructures. Generally, these systems make use of cameras mounted on strategic locations for surveillance purposes. Cameras based detection systems are limited by line-of-sight, need regular maintenance and dependence of electricity for operations. These are all detrimental to the efficiency of these detection systems, especially in remote locations. To overcome these challenges, intrusion detection systems based on seismic activities have been in use. The seismic activities collected through geophones from the human footfalls can act as the input for these detection systems. This also poses a challenge as the data generated by the geophones for the seismic activities produced from footsteps are not always identical and hence not accurate. In this proposed work, a Deep Neural Network based approach has been used on the dataset collected from the geophones to effectively predict the presence of humans. The results gave a success rate with 94.86% accuracy with testing data and 92.00% accuracy with real-time data with the geophones deployed on an area covered with grass. 2022 IEEE. -
Seismic Performance Assessment of Reinforced Concrete Frames: Insights from Pushover Analysis
This paper offers a comprehensive exploration of the seismic response of Reinforced Concrete (RC) frames examined through pushover analysis. The frames analyzed are designed as per IS 13920 and IS 456 for different levels of earthquake intensities and different levels of axial loads. Nonlinear analysis techniques have gained prominence in assessing the response of RC frames, especially when subjected to extreme loading events or when accurate predictions of structural behavior are required beyond the linear elastic range. The study aims to delve into the structural behavior of RC frames under seismic influences, employing pushover analysis as the principal analytical tool. With a focus on assessing the effectiveness and reliability of pushover analysis, the research endeavors to elucidate the seismic performance of RC frames while considering their response to different seismic zones and axial loading scenarios. The methodology involves conducting a series of pushover analyses on RC frames using advanced structural analysis software. The results obtained are meticulously analyzed to discern the shear capacities and ultimate displacements of the frames, by investigating the displacement versus shear capacity relationship across varying seismic zones and axial loading scenarios. Through this comprehensive investigation, the paper aims to enhance our understanding of the seismic behavior of RC frames and will provide valuable insights for seismic design. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Selection of cobot for human-robot collaboration for robotic assembly task with Best Worst MCDM techniques
Since the first industrial robot was produced at the beginning of the 1960s, robotic technology has completely changed the sector. Industrial robots are made for various tasks, including welding, painting, assembling, disassembling, picking and placing printed circuit boards, palletizing, packing and labeling, and product testing. Finding flexible solutions that allow production lines to be swiftly re-planned, adjusted, and structured for new or significantly modified product development remains a significant unresolved problem. Today's Industrial robots are still mostly pre-programmed to do certain jobs; they cannot recognize mistakes in their work or communicate well with both a complicated environment and a human worker. Full robot autonomy, including organic interaction, learning from and with humans, and safe and adaptable performance for difficult tasks in unstructured contexts, will remain a pipe dream for the foreseeable future. Humans and robots will work together in collaborative settings such as homes, offices, and factory setups to execute various object manipulation activities. So, it is necessary to study the collaborative robots (cobots) that will play a key role in human-robot collaborations. Multiple competing variables must be considered in a thorough selection process to assess how well industrial cobots will work on an industrial working floor. To select a collaborative robot for the human-robot collaborative application, a straightforward multi-criteria decision-making (MCDM) methodology is based on the best-worst method (BWM). The ranking derived using the BWM method is displayed. The outcomes demonstrated the value of MCDM techniques for cobot selection. 2023 IEEE. -
Selection of specialization In management program a decision tree approach /
International Journal For Research In Engineering Application & Management, Vol.4, Issue 11, pp.96-103, ISSN No: 2454-9150. -
Selection of Tightened-Normal-Tightened sampling scheme under the implications of intervened Poisson distribution
Tightened-normal-tightened (TNT) sampling scheme is one of the most frequently used sampling schemes for making decisions about the finished product lots by examining certain samples from the lots. TNT sampling scheme includes two attribute sampling plans, one for tightened inspection and other for normal inspection along with switching rules. This paper introduces a procedure for TNT by incorporating two single sampling plans (SSP) under the conditions of intervened Poisson distribution (IPD) for the lots which may have a possibility of someintervention during the production process. The paper also assesses the performance of the proposed scheme procedure through its operating characteristic curves. Also, the unity value table is provided for certain parameters of specified producer's risk and consumer's risk for shop floor conditions. Further, the efficiency of proposed TNT scheme over the individual SSP under the conditions of IPD is demonstrated with illustrations. 2019 University of the Punjab.