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| 1 | A comprehensive review from sequential association computing to Hadoop-MapReduce parallel computing in a retail scenario显示文摘Today,the customer’s requirements are entirely transformed.Many big retail organizations are facing sudden decline in the sales and revenues caused due to indecisive and erratic purchasing habits of recent generation of users,as they get abundant preferred information such as cheaper rates,amazing offers,discounts,comparison of similar products,etc.over their smartphones or laptops hence they straightaway place order instead of walking down to showroom.As a result,large companies such as Tesco,Wal-Mart,Target,etc.have realized that it is requisite to shake hands with startup firms which already supports platform to retain customers either via deep exploration of transactional data or by offering lucrative offers in the benefit of customer and to promote market basket.The data which are generated from consumer purchase pattern,Big Data is a concern for companies as a result various big retail organizations are applying advanced and scalable data mining algorithms to precisely store and evaluate data in real-time manner to boost market basket analysis.This research work discusses various improved association rule mining(ARM)algorithms.The objective of this study is to identify gaps,providing opportunities for new research,to recognize expansion of Big Data analytics with retail environment and its future directions.This paper assimilates various aspects of parallel ARM algorithm for market basket analysis against sequential and distributed nature which are further escalated to Hadoop and MapReduce computing platform.Further various use cases highlighting the need of‘Big Data Retail Analytics’are discussed for emerging trends to promote sales and revenues,to keep check on competitor’s websites,comparison of various brands,enticing new customers. | Neha Verma Jatinder Singh | 2017 | Journal of Management Analytics2017，4，4： | 4 |
| 2 | Big data analytics for retail industry using MapReduce-Apriori framework显示文摘Presently,retailing has changed its face from unordered stacked traditional stores to beautifully decorated and appropriately managed merchandise stores or shopping malls with excellent ambiance and comfort.Therefore,these stores try to accommodate all needed items for daily use or rarely required items under the same roof.However,the primary challenge for today’s retailer is that the modern customer is quality and brands conscious as well as compare for services provided to them by different outlets at the comfort of home with a single click.Therefore,customers prefer to purchase from E-Commerce websites instead of physically visiting a retail store,which leads to the downfall in the sales of retailers which become a serious threat to them.Therefore,retailers are required to work sincerely towards their customer expectations by providing all their needed goods under the same roof.Therefore,the objective of this paper is to assist retail business owners to recognize the purchasing needs of their customers and hence to entice customers to physical retail stores away from competitor E-Commerce websites.This paper employs a systematic research methodology based on association rule mining deployed over Map-Reduce based Apriori association mining and Hadoop based intelligent cloud architecture to determine useful buying patterns from purchase history of previous customers,in order to assist retail business owners.The finding acknowledges that the traditional mining algorithms have not progressed to support big data analysis as required by current retail businesses owners.The job of finding unknown association rules from big data requires a lot of resources such as memory and processing engines.Moreover,traditional mining systems are inadequate to provide support for partial failure support,extensibility,scalability etc.Therefore,this study aims to implement and develop MapReduce based Apriori(MR-Apriori)algorithm in the form of Intelligent Retail Mining Tool i.e.IRM Tool to recognize all these concerns in an efficient manner.The proposed system adequately satisfy all significant requisites anticipated from modern Big Data processing systems such as scalability,fault tolerance,partial failure support etc.Finally,this study experimentally verifies the effectiveness of the proposed algorithm i.e.MR-Apriori by speed-up,size-up,and scale-up evaluation parameters. | Neha Verma Dheeraj Malhotra tinder Singh | 2020 | Journal of Management Analytics2020，7，3： | 1 |
| 3 | Review of Artificial Intelligence with Retailing Sector显示文摘This research service provides an original perspective on how artificial intelligence(AI)is making its way into the retail sector.Retail has entered a new era where ECommerce and technology bellwethers like Alibaba,Amazon,Apple,Baidu,Facebook,Google,Microsoft,and Tencent have raised consumers’expectations.AI is enabling automated decision-making with accuracy and speed,based on data analytics,coupled with selflearning abilities.The retail sector has witnessed the dramatic evolution with the rapid digitalization of communication(i.e.Internet)and;smart phones and devices.Customer is no longer the same as they became more empowered by smart devices which has entirely prevailed their expectation,habits,style of shopping and investigating the shops.This article outlines the Significant innovation done in retails which helped them to evolve such as Artificial Intelligence(AI),Big data and Internet of Things(IoT),Chatbots,Robots.This article further also discusses the ideology of various author on how AI become more profitable and a close asset to customers and retailers. | Venus Kaur Vasvi Khullar Neha Verma | 2020 | Journal of Computer Science Research2020，2，1： | 0 |
| 4 | Recent updates on nano-phyto-formulations based therapeutic intervention for cancer treatment显示文摘Cancer is a leading cause of death globally,with limited treatment options and several limitations.Chemotherapeutic agents often result in toxicity which long-term conventional treatment.Phytochemicals are natural constituents that are more effective in treating various diseases with less toxicity than the chemotherapeutic agents providing alternative therapeutic approaches to minimize the resistance.These phytoconstituents act in several ways and deliver optimum effectiveness against cancer.Nevertheless,the effectiveness of phyto-formulations in the management of cancers may be constrained due to challenges related to inadequate solubility,bioavailability,and stability.Nanotechnology presents a promising avenue for transforming current cancer treatment methods through the incorporation of phytochemicals into nanosystems,which possess a range of advantageous characteristics such as biocompatibility,targeted and sustained release capabilities,and enhanced protective effects.This holds significant potential for future advancements in cancer management.Herein,this review aims to provide intensive literature on diverse nanocarriers,highlighting their applications as cargos for phytocompounds in cancer.Moreover,it offers an overview of the current advancements in the respective field,emphasizing the characteristics that contribute to favourable outcomes in both in vitro and in vivo settings.Lastly,clinical development and regulatory concerns are also discussed to check on the transformation of the concept as a promising strategy for combination therapy of phytochemicals and chemotherapeutics that could lead to cancer management in the future. | ABHISHEK WAHI MAMTA BISHNOI NEHA RAINA MEGHNA AMRITA SINGH PIYUSH VERMA PIYUSH KUMAR GUPTA GINPREET KAUR HARDEEP SINGH TULI MADHU GUPTA | 2024 | Oncology Research2024，32，1： | 0 |
| 5 | Axially Oriented Structured Porous Layers for Heat Transfer Enhancement in a Solar Receiver Tube显示文摘The present work reports a numerical investigation of heat transfer and pressure drop characteristics in a solar receiver tube with different shaped porous media for laminar and low Reynolds number turbulent flow regimes.Numerical simulations have been performed with finite volume-based code ANSYS(v-2017)for different shapes of porous layers axially oriented in the tube.The plain-shaped porous medium fitted up to 50%of the tube shows better performance than other-shaped porous layers.Simulations have also been performed for axially oriented structured porous media with different sizes.Axially oriented structured porous medium develops a lateral flow disturbance enhancing the intermixing of the liquid and porous medium at their interface.Structured porous medium with a 3-crest configuration shows the best heat transfer performance among all the shapes of porous media.It offers a maximum of 148%heat transfer enhancement compared to a half-filled plain porous layer,whereas it reports a maximum of 564%enhancement compared to the flow without a porous layer.The lateral flow tendency or the swirling effect helps better heat transfer performance in the axially oriented structured porous media.Performance evaluation criterion(PEC)in all types of porous media is more in the transitional flow regime than in the laminar and turbulent flow regimes.For the same operating conditions,the maximum value of the PEC in the present work is 120%higher than the maximum value of PEC for other-shaped porous media reported in the literature.Correlations for Nusselt number have been developed for both laminar and turbulent flow regimes for three crests shaped porous medium. | DAS Shefali VERMA Neha PATHAK Manabendra BHATTACHARYYA Suvanjan | 2021 | Journal of Thermal Science2021，30，5： | 0 |
| 6 | Machine learning for weed–plant discrimination in agriculture 5.0:An in-depth review显示文摘Agriculture 5.0 is an emerging concept where sensors,big data,Internet-of-Things(IoT),robots,and Artificial In-telligence(AI)are used for agricultural purposes.Different from Agriculture 4.0,robots and AI become the focus of the implementation in Agriculture 5.0.One of the applications of Agriculture 5.0 is weed management where robots are used to discriminate weeds from the crops or plants so that proper action can be performed to remove the weeds.This paper discusses an in-depth review of Machine Learning(ML)techniques used for discriminating weeds from crops or plants.We specifically present a detailed explanation of five steps required in using ML algorithms to distinguish between weeds and plants. | Filbert H.Juwono W.K.Wong Seema Verma Neha Shekhawat Basil Andy Lease Catur Apriono | 2023 | Artificial Intelligence in Agriculture2023，，4： | 0 |
| 7 | Emerging peril of post-dengue mucormycosis:A case report显示文摘Rationale:Dengue fever is a leading cause of death in tropical and subtropical countries.Although most patients have a self-limited febrile illness,the viral infection can induce virus-mediated host changes,making immunocompetent persons susceptible to deadly fungal infections.However,there are only a few reports of such an association.Here we present a case of this deadly co-infection.Patient’s Concern:A 17-year-old male patient was diagnosed with dengue fever.He presented to us with facial swelling,periorbital edema,and black discoloration over the palate during the second week of his illness.Diagnosis:Diagnostic tests confirmed the presence of fungal hyphae.A diagnosis of post-dengue mucormycosis was made.No other comorbidity or underlying immune deficit was detected.Interventions:The patient underwent surgical debridement and antifungal treatment.Outcomes:The patient recovered and showed signs of palatal healing with an advancing mucosal edge.Lessons:Dengue virus and mucor co-infection has brought to light a new pathogenic paradigm.Clinicians need to be aware of this emerging medical condition and maintain a high index of suspicion for mucor co-infections while treating dengue patients. | Neha Verma Neelima Gupta Vashi Gupta Smita Nath | 2023 | Journal of Acute Disease2023，12，1： | 0 |
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