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Journal articles on the topic 'Algorithmes ad'

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Published: 1 February 2023

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1

Merz, Evan. "Implications of Ad Hoc Artificial Intelligence in Music." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 10, no. 5 (June 29, 2021): 35–39. http://dx.doi.org/10.1609/aiide.v10i5.12763.

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This paper is an examination of several well-known applications of artificial intelligence in music generation. The algorithms in EMI, GenJam, WolframTones, and Swarm Music are examined in pursuit of ad hoc modifications. Based on these programs, it is clear that ad hoc modifications occur in most algorithmic music programs. We must keep this in mind when generalizing about computational creativity based on these programs. Ad hoc algorithms model a specific task, rather than a general creative algorithm. The musical metacreation discourse could benefit from the skepticism of the procedural content practitioners at AIIDE.

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Cromer, Sara J., Victoria Chen, Christopher Han, William Marshall, Shekina Emongo, Evelyn Greaux, Tim Majarian, Jose C. Florez, Josep Mercader, and Miriam S. Udler. "Algorithmic identification of atypical diabetes in electronic health record (EHR) systems." PLOS ONE 17, no. 12 (December 12, 2022): e0278759. http://dx.doi.org/10.1371/journal.pone.0278759.

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Aims Understanding atypical forms of diabetes (AD) may advance precision medicine, but methods to identify such patients are needed. We propose an electronic health record (EHR)-based algorithmic approach to identify patients who may have AD, specifically those with insulin-sufficient, non-metabolic diabetes, in order to improve feasibility of identifying these patients through detailed chart review. Methods Patients with likely T2D were selected using a validated machine-learning (ML) algorithm applied to EHR data. “Typical” T2D cases were removed by excluding individuals with obesity, evidence of dyslipidemia, antibody-positive diabetes, or cystic fibrosis. To filter out likely type 1 diabetes (T1D) cases, we applied six additional “branch algorithms,” relying on various clinical characteristics, which resulted in six overlapping cohorts. Diabetes type was classified by manual chart review as atypical, not atypical, or indeterminate due to missing information. Results Of 114,975 biobank participants, the algorithms collectively identified 119 (0.1%) potential AD cases, of which 16 (0.014%) were confirmed after expert review. The branch algorithm that excluded T1D based on outpatient insulin use had the highest percentage yield of AD (13 of 27; 48.2% yield). Together, the 16 AD cases had significantly lower BMI and higher HDL than either unselected T1D or T2D cases identified by ML algorithms (P<0.05). Compared to the ML T1D group, the AD group had a significantly higher T2D polygenic score (P<0.01) and lower hemoglobin A1c (P<0.01). Conclusion Our EHR-based algorithms followed by manual chart review identified collectively 16 individuals with AD, representing 0.22% of biobank enrollees with T2D. With a maximum yield of 48% cases after manual chart review, our algorithms have the potential to drastically improve efficiency of AD identification. Recognizing patients with AD may inform on the heterogeneity of T2D and facilitate enrollment in studies like the Rare and Atypical Diabetes Network (RADIANT).

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Ma, Renkai, and Yubo Kou. ""How advertiser-friendly is my video?": YouTuber's Socioeconomic Interactions with Algorithmic Content Moderation." Proceedings of the ACM on Human-Computer Interaction 5, CSCW2 (October 13, 2021): 1–25. http://dx.doi.org/10.1145/3479573.

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To manage user-generated harmful video content, YouTube relies on AI algorithms (e.g., machine learning) in content moderation and follows a retributive justice logic to punish convicted YouTubers through demonetization, a penalty that limits or deprives them of advertisements (ads), reducing their future ad income. Moderation research is burgeoning in CSCW, but relatively little attention has been paid to the socioeconomic implications of YouTube's algorithmic moderation. Drawing from the lens of algorithmic labor, we describe how algorithmic moderation shapes YouTubers' labor conditions through algorithmic opacity and precarity. YouTubers coped with such challenges from algorithmic moderation by sharing and applying practical knowledge they learned about moderation algorithms. By analyzing video content creation as algorithmic labor, we unpack the socioeconomic implications of algorithmic moderation and point to necessary post-punishment support as a form of restorative justice. Lastly, we put forward design considerations for algorithmic moderation systems.

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Poselentseva, D. Yu, and E. B. Zamyatina. "An experience of routing algorithms and a data transfer algorithms using simulation." Вестник Пермского университета. Математика. Механика. Информатика, no. 4 (47) (2019): 76–85. http://dx.doi.org/10.17072/1993-0550-2018-4-76-85.

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Bessa, Maria. "Algorithmic Design." Architectural Design 79, no. 1 (January 2009): 120–23. http://dx.doi.org/10.1002/ad.831.

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Register, Yim, and Emma S. Spiro. "Developing Self-Advocacy Skills through Machine Learning Education: The Case of Ad Recommendation on Facebook." Proceedings of the International AAAI Conference on Web and Social Media 16 (May 31, 2022): 817–28. http://dx.doi.org/10.1609/icwsm.v16i1.19337.

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Facebook users interact with algorithms every day. These algorithms can perpetuate harm via incongruent targeted ads, echo chambers, or "rabbit hole" recommendations. Education around the machine learning (ML) behind Facebook (FB) can help users to point out algorithmic bias and harm, and advocate for themselves effectively when things go wrong. One algorithm that FB users interact with regularly is User-Based Collaborative Filtering (UB-CF) which provides the basis for ad recommendation. We contribute a novel research approach for teaching users about a commonly used algorithm in machine learning in real-world context -- an instructive web application using real examples built from the user's own FB data on ad interests. The instruction also prompts users to reflect on their interactions with ML systems, specifically Facebook. In a between-subjects design, we tested both Data Science Novices and Experts on the efficacy of the UB-CF instruction. Taking care to highlight the voices of marginalized users, we use the application as a prompt for surfacing potential harms perpetuated by FB ad recommendations, and qualitatively analyze themes of harm and proposed solutions provided by users themselves. The instruction increased comprehension of UB-CF for both groups, and we show that comprehension is associated with mentioning the mechanisms of the algorithm more in advocacy statements, a crucial component of a successful argument. We provide recommendations for increased algorithmic transparency on social media and for including marginalized voices in the conversation of algorithmic harm that are of interest both to social media researchers and ML educators.

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Pawan, Kumar Verma, Gupta Tarun, Rakesh Nitin, and Nitin Nitin. "A Mobile Ad-Hoc Routing Algorithm with Comparative Study of Earlier Proposed Algorithms." International Journal of Communications, Network and System Sciences 03, no. 03 (2010): 289–93. http://dx.doi.org/10.4236/ijcns.2010.33037.

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Paik, Ji Woong, Jong-Mann Kim, and Joon-Ho Lee. "Performance Improvement of AD-MUSIC Algorithm Using Newton Iteration." Journal of Korean Institute of Electromagnetic Engineering and Science 28, no. 11 (November 2017): 880–85. http://dx.doi.org/10.5515/kjkiees.2017.28.11.880.

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Taniguchi, Hirohito, Michiko Inoue, Toshimitsu Masuzawa, and Hideo Fujiwara. "Clustering algorithms in ad hoc networks." Electronics and Communications in Japan (Part II: Electronics) 88, no. 1 (2004): 51–59. http://dx.doi.org/10.1002/ecjb.20143.

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Pestin, Maxim S., and Alexander S. Novikov. "Simulation model of wireless ad-hoc network to study algorithms of traffic routing." Journal Of Applied Informatics 17, no. 4 (August 31, 2022): 75–86. http://dx.doi.org/10.37791/2687-0649-2022-17-4-75-86.

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Communication network simulators are software designed to model, explore, test and debug network technologies, including wireless decentralized self-organizing networks or ad-hoc networks. They greatly simplify the research, development and optimization of routing protocols in these networks. However, the well-known simulators have a number of disadvantages, including the difficulty of adding custom extensions to ad-hoc network routing protocols, the lack of the necessary network stack, the lack of routing algorithm visualization modes, low performance, and difficulty in debugging communication protocols. The purpose of this work is to create a simulation model of a wireless network that would allow us to explore, debug and evaluate the developed algorithms and routing protocols for ad-hoc networks. At the same time, the requirements for interface ergonomics and the ability to visualize the operation of algorithms, ensure the collection of statistics, and create various scenarios for the operation of the network come to the fore. The article proposes the structure of the simulation model, which includes the modules of the network subscriber, application software, network layer of the OSI data transmission model, radio module, radio transmission environment, statistics collection, visualization and scenario management. To solve the tasks set, the approach of discrete-event modeling was used. To create a simulator of wireless decentralized networks and routing algorithms, a set of classes was developed that implement the modules of the simulation model. Based on the proposed structure, module classes and discrete event simulation algorithm, a software implementation of the simulation model was created using the C++ programming language and the Qt framework. The developed simulation model was used in the course of an experimental study of the effectiveness of the network routing algorithm. The proposed software will simplify the development and debugging of algorithms and routing protocols for ad-hoc networks.

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Bosia, Daniel. "Long Form and Algorithm." Architectural Design 81, no. 4 (July 2011): 58–65. http://dx.doi.org/10.1002/ad.1269.

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Abdallah, Alaa E., Mohammad Bsoul, Emad E. Abdallah, Ibrahim Al–Oqily, and George Kao. "Cluster-Based Online Routing Protocols for Ad Hoc Network." International Journal of Information Technology and Web Engineering 9, no. 4 (October 2014): 54–66. http://dx.doi.org/10.4018/ijitwe.2014100105.

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In geographical routing algorithms, mobile nodes rely on geographical position to make routing judgments. Researchers frequently discuss such routing algorithms in (2D) space. However, in reality, mobile nodes spread in (3D) space. In this paper the authors present four new 3D geographical-based routing algorithms Cylinder, Greedy-Cylinder, Cluster-Cylinder, and Greedy-cluster-Cylinder. In Cylinder routing, the nodes are locally projected on the inner surface of a cylinder, perimeter routing is executed after that. Greedy-Cylinder starts with Greedy routing algorithm until a local minimum is reached. The algorithm then switches to Cylinder routing. Cluster-Cylinder elects a dominating set for all nodes and then uses this set for projection and routing. The fourth algorithm Greedy-cluster-Cylinder is a combination between Greedy-Cylinder and Cluster-Cylinder. The authors evaluate their new algorithms and compare them with many classical known algorithms. The simulation outcomes show the substantial enhancement in delivery rate over other algorithms.

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Ghaffari, Ali. "Real-time routing algorithm for mobile ad hoc networks using reinforcement learning and heuristic algorithms." Wireless Networks 23, no. 3 (January 8, 2016): 703–14. http://dx.doi.org/10.1007/s11276-015-1180-0.

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14

Aydın, Nevin. "Heuristic Clustering Algorithms in Ad hoc Networks." EMAJ: Emerging Markets Journal 3, no. 3 (March 5, 2014): 77–80. http://dx.doi.org/10.5195/emaj.2014.39.

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The clustering allows dividing the geographical region to be covered into small zones in which each zone can be handled with a powerful node called clusterhead. The clusterheads have direct communication link with each of its members whereas the member nodes of a cluster must go through the clusterhead to communicate with each other. Since choosing clusterheads optimally is an NP-hard problem, existing solutions to this problem are based on heuristic (mostly greedy) approaches. In this paper, we present three well-known heuristic clustering algorithms: the Lowest-ID, the Highest-Degree, and the Node-Weight.

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Miralles Pechuán, Luis, Claudia Sánchez Gómez, and Lourdes Martínez Villaseñor. "Ad Exchange Optimization Algorithms on Advertising Networks." Research in Computing Science 84, no. 1 (December 31, 2014): 77–88. http://dx.doi.org/10.13053/rcs-84-1-7.

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Wattenhofer, Roger. "Algorithms for ad hoc and sensor networks." Computer Communications 28, no. 13 (August 2005): 1498–504. http://dx.doi.org/10.1016/j.comcom.2004.12.037.

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Peters, Brady. "Computation Works: The Building of Algorithmic Thought." Architectural Design 83, no. 2 (March 2013): 8–15. http://dx.doi.org/10.1002/ad.1545.

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18

Khan, Zulai, and Rashmi Raj. "Comparative Analysis of Green Algorithm within Active Queue Management for Mobile Ad Hoc Network." International Journal of Trend in Scientific Research and Development Volume-3, Issue-4 (June 30, 2019): 935–38. http://dx.doi.org/10.31142/ijtsrd23950.

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Seo, Hoon, Lodewijk Brand, Hua Wang, and Feiping Nie. "Integrating Static and Dynamic Data for Improved Prediction of Cognitive Declines Using Augmented Genotype-Phenotype Representations." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 1 (May 18, 2021): 522–30. http://dx.doi.org/10.1609/aaai.v35i1.16130.

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Alzheimer’s Disease (AD) is a chronic neurodegenerative disease that causes severe problems in patients’ thinking, memory, and behavior. An early diagnosis is crucial to prevent AD progression; to this end, many algorithmic approaches have recently been proposed to predict cognitive decline. However, these predictive models often fail to integrate heterogeneous genetic and neuroimaging biomarkers and struggle to handle missing data. In this work we propose a novel objective function and an associated optimization algorithm to identify cognitive decline related to AD. Our approach is designed to incorporate dynamic neuroimaging data by way of a participant-specific augmentation combined with multimodal data integration aligned via a regression task. Our approach, in order to incorporate additional side-information, utilizes structured regularization techniques popularized in recent AD literature. Armed with the fixed-length vector representation learned from the multimodal dynamic and static modalities, conventional machine learning methods can be used to predict the clinical outcomes associated with AD. Our experimental results show that the proposed augmentation model improves the prediction performance on cognitive assessment scores for a collection of popular machine learning algorithms. The results of our approach are interpreted to validate existing genetic and neuroimaging biomarkers that have been shown to be predictive of cognitive decline.

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Chen, Xiao Yun, Sha Liu, and Long Jie Li. "ABCR-STC: An Improved Adaptive Backoff Algorithm for MAC Protocol in Ad Hoc Networks." Applied Mechanics and Materials 40-41 (November 2010): 462–68. http://dx.doi.org/10.4028/www.scientific.net/amm.40-41.462.

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The performance of backoff algorithm plays an important role in designing efficient Medium Access Protocols for Ad hoc networks. In this paper, we propose three backoff algorithms for Ad hoc networks. The first algorithm aims at higher fairness, and the second aims at stronger adaptivity. Then, we combine the first two algorithms to propose an optimized backoff algorithm. We use NS2 to simulate Ad hoc network environment to evaluate the performance of our improved backoff algorithms. The results show that our algorithms exhibit a signiﬁcant improvement in throughput and fairness with the existing BEB and AASC algorithm.

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Llabres, Enriqueta, and Eduardo Rico. "Relational Urban Models: Parameters, Values and Tacit Forms of Algorithms." Architectural Design 86, no. 2 (March 2016): 84–91. http://dx.doi.org/10.1002/ad.2028.

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22

Vélez, Jorge I., Luiggi A. Samper, Mauricio Arcos-Holzinger, Lady G. Espinosa, Mario A. Isaza-Ruget, Francisco Lopera, and Mauricio Arcos-Burgos. "A Comprehensive Machine Learning Framework for the Exact Prediction of the Age of Onset in Familial and Sporadic Alzheimer’s Disease." Diagnostics 11, no. 5 (May 17, 2021): 887. http://dx.doi.org/10.3390/diagnostics11050887.

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Machine learning (ML) algorithms are widely used to develop predictive frameworks. Accurate prediction of Alzheimer’s disease (AD) age of onset (ADAOO) is crucial to investigate potential treatments, follow-up, and therapeutic interventions. Although genetic and non-genetic factors affecting ADAOO were elucidated by other research groups and ours, the comprehensive and sequential application of ML to provide an exact estimation of the actual ADAOO, instead of a high-confidence-interval ADAOO that may fall, remains to be explored. Here, we assessed the performance of ML algorithms for predicting ADAOO using two AD cohorts with early-onset familial AD and with late-onset sporadic AD, combining genetic and demographic variables. Performance of ML algorithms was assessed using the root mean squared error (RMSE), the R-squared (R2), and the mean absolute error (MAE) with a 10-fold cross-validation procedure. For predicting ADAOO in familial AD, boosting-based ML algorithms performed the best. In the sporadic cohort, boosting-based ML algorithms performed best in the training data set, while regularization methods best performed for unseen data. ML algorithms represent a feasible alternative to accurately predict ADAOO with little human intervention. Future studies may include predicting the speed of cognitive decline in our cohorts using ML.

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Jie, Liu. "Ad Hoc Access Gateway Selection Algorithm." Physics Procedia 25 (2012): 2242–48. http://dx.doi.org/10.1016/j.phpro.2012.03.377.

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Jang, In-Hun, and Kwee-Bo Sim. "Optimal Routing Path Selection Algorithm in Ad-hoc Wireless Sensor Network." Journal of Korean Institute of Intelligent Systems 15, no. 6 (December 1, 2005): 736–41. http://dx.doi.org/10.5391/jkiis.2005.15.6.736.

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Kamali, K., and Dr K. Selvakumar. "Cross-layer based Mobility-Aware Backoff Algorithm for Collision Avoidance in Wireless Ad hoc Networks." Journal of Advanced Research in Dynamical and Control Systems 11, no. 12-SPECIAL ISSUE (December 31, 2019): 328–36. http://dx.doi.org/10.5373/jardcs/v11sp12/20193228.

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Desai, Kirtiraj Mohan, and Prof Ms T. T. Mohite Patil. "Performance Analysis of Congestion Control Algorithm for Mobility Model in Mobile Ad-Hoc Networks MANET." International Journal of Trend in Scientific Research and Development Volume-2, Issue-5 (August 31, 2018): 2348–53. http://dx.doi.org/10.31142/ijtsrd18335.

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Sanjay, V., and P. Swarnalatha. "Deep Learning Techniques for Early Detection of Alzheimer’s Disease: A Review." International Journal of Electrical and Electronics Research 10, no. 4 (December 30, 2022): 899–905. http://dx.doi.org/10.37391/ijeer.100425.

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Alzheimer's disease (AD) is the most prevalent kind of dementia illness that can significantly impair a person's capability to carry out everyday tasks. According to findings, AD may be the third provoking reason of mortality among older adults, behind cancer and heart disease. Individuals at risk of acquiring AD must be identified before treatment strategies may be tested. The study's goal is to give a thorough examination of tissue structures using segmented MRI, which will lead to a more accurately labeling of certain brain illnesses. Several complicated segmentation approaches for identify AD have been developed. DL algorithms for brain structure segmentation and AD categorization have gotten a lot of attention since they can deliver accurate findings over a huge amount of data. As a result, DL approaches are increasingly favored over cutting-edge Machine Learning (ML) techniques. This study provides you with an overview of current trend deep learning-based segmentation algorithms for analyzing brain Magnetic Resonance Imaging for the treatment of AD. Finally, a conversation on the approaches' benefits and drawbacks, as well as future directives, was held, which may help researchers better comprehend present algorithms and methods in this field, and eventually design new and more successful algorithms.

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Cho, Young-Joo, Yeo-Jin Lee, and Il-Yong Chung. "The establishment of Proactive Routing Selection and Maintenance Algorithms for Mobile Ad Hoc Networks." KIPS Transactions:PartC 14C, no. 1 (February 28, 2007): 73–80. http://dx.doi.org/10.3745/kipstc.2007.14-c.1.073.

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Desai, Rahul M., B. P. Patil, and Davinder Pal Sharma. "Learning Based Route Management in Mobile Ad-Hoc Networks." Indonesian Journal of Electrical Engineering and Computer Science 7, no. 3 (September 1, 2017): 718. http://dx.doi.org/10.11591/ijeecs.v7.i3.pp718-723.

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Ad hoc networks are mobile wireless networks where each node is acting as a router. The existing routing protocols such as Destination sequences distance vector, Optimized list state routing protocols, Ad hoc on demand routing protocol, dynamic source routing are optimized versions of distance vector or link state routing protocols. Reinforcement Learning is new method evolved recently which is learning from interaction with an environment. Q Learning which is based on Reinforcement learning that learns from the delayed reinforcements and becomes more popular in areas of networking. Q Learning is applied to the routing algorithms where the routing tables in the distance vector algorithms are replaced by the estimation tables called as Q values. These Q values are based on the link delay. In this paper, various optimization techniques over Q routing are described in detail with their algorithms.

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Chatzigiannakis, Ioannis, Sotiris Nikoletseas, and Paul Spirakis. "Distributed communication algorithms for ad hoc mobile networks." Journal of Parallel and Distributed Computing 63, no. 1 (January 2003): 58–74. http://dx.doi.org/10.1016/s0743-7315(02)00034-5.

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J., Amir. "Matlab Simulations of Ad Hoc Sensors Network Algorithms." International Journal on Recent and Innovation Trends in Computing and Communication 3, no. 1 (2015): 288–93. http://dx.doi.org/10.17762/ijritcc2321-8169.150159.

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Pradhan, Nuraj L., and Tarek Saadawi. "Power control algorithms for mobile ad hoc networks." Journal of Advanced Research 2, no. 3 (July 2011): 199–206. http://dx.doi.org/10.1016/j.jare.2011.04.009.

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Desai, Rahul, and B. P. Patil. "Adaptive Routing for an Ad Hoc Network Based on Reinforcement Learning." International Journal of Business Data Communications and Networking 11, no. 2 (July 2015): 40–52. http://dx.doi.org/10.4018/ijbdcn.2015070103.

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This paper describes and evaluates the performance of various reinforcement learning algorithms with shortest path algorithms that are widely used for routing packets throughout the network. Shortest path routing is simplest policy used for routing the packets along the path having minimum number of hops. In high traffic or high mobility conditions, the shortest path gets flooded with huge number of packets and congestions occurs, so such shortest path does not provide the shortest path and increases delay for reaching the packets to the destination. Reinforcement learning algorithms are adaptive algorithms where the path is selected based on the traffic present on the network at real time. Thus they guarantee the least delivery time to reach the packets to the destination. Analysis is done on a 6-by-6 irregular grid and sample ad hoc network shows that performance parameters used for judging the network such as packet delivery ratio and delay provide optimum results using reinforcement learning algorithms.

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Kim, Jin-Su, Jun-Ho Choi, Myeong-Jin Shin, Ji-Seon Lee, and Sang-Jo Yoo. "An Efficient Routing Algorithm Considering Packet Collisions in Cognitive Radio Ad-hoc Network." Journal of Korea Information and Communications Society 38B, no. 9 (September 30, 2013): 751–64. http://dx.doi.org/10.7840/kics.2013.38b.9.751.

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Chouaki, Salim, Islem Bouzenia, Oana Goga, and Beatrice Roussillon. "Exploring the Online Micro-targeting Practices of Small, Medium, and Large Businesses." Proceedings of the ACM on Human-Computer Interaction 6, CSCW2 (November 7, 2022): 1–23. http://dx.doi.org/10.1145/3555103.

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Facebook and other advertising platforms exploit users' data for marketing purposes by allowing advertisers to select specific users and target them with well-crafted messages (the practice is being called micro-targeting). However, advertisers such as Cambridge Analytica have maliciously used these targeting features to manipulate users in the context of elections. The European Commission plans to restrict or even ban some targeting functionalities in the new European Democracy Action Plan act to protect users from such harms. The difficulty in finding appropriate restrictions is that we do not know the economic impact of these restrictions on regular advertisers. In this paper, to inform the debate, we take a first step by understanding who is advertising on Facebook and how they use the targeting functionalities of ad platforms. For this, we asked 890 U.S. users to install a monitoring tool on their browsers to collect the ads they receive on Facebook and information about how these ads were targeted. By matching advertisers on Facebook with their LinkedIn profiles, we could see that 71% of advertisers are small and medium-sized businesses with 200 employees or less, and they are responsible for 61% of ads and 57% of ad impressions. Regarding micro-targeting, we found that only 32% of small and medium-sized businesses and 30% of large-sized businesses in our dataset micro-target at least one of their ads. These results should not be interpreted as micro-targeting not being useful as a marketing strategy, but rather that advertisers prefer to outsource the micro-targeting task to ad platforms. Indeed, to deliver ads, Facebook is employing optimization algorithms that exploit user data to decide which users should see what ads; which basically means ad platforms are performing an algorithmic-driven micro-targeting. Hence, when setting restrictions on micro-targeting, legislators should take into account both the traditional advertiser-driven micro-targeting as well as algorithmic-driven micro-targeting performed by ad platforms.

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Memić, Belma, Alem Čolaković, Adisa Hasković Džubur, and Elma Avdagić-Golub. "Efficiency-complexity evaluation methods of routing algorithms in mobile ad hoc networks." Science, Engineering and Technology 1, no. 2 (October 30, 2021): 24–31. http://dx.doi.org/10.54327/set2021/v1.i2.12.

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Routing in multidomain and multilayer networks is the subject of constant theoretical research, with special emphasis on routing optimization algorithms based on several criteria. Such research results in new proposals. The basic task of the algorithm is to perform the given task in a finite and reasonable period of time and with reasonable resource requirements. When new solutions are compared with previous solutions, it is necessary to consider as much information as possible about the characteristics and differences between these algorithms, which ultimately determines the degree of success of the algorithm. Routing algorithms depend on the goals to be achieved and most often solve a certain group of problems with certain simplifications of the overall problem and to the detriment of performance that are not crucial for a given routing optimization problem. Therefore, it is necessary to have acceptable methods for efficiency-complexity evaluation methods of routing algorithms with certain, universally applicable, metrics. Several theoretical approaches, including graph theory, optimization theory, complexity theory, allow approaches to compare the algorithms and the results achieved with the help of these algorithms.

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Xiang, Ma. "Research of Ad Hoc Networks Access Algorithm." Physics Procedia 25 (2012): 2257–62. http://dx.doi.org/10.1016/j.phpro.2012.03.379.

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Rani, K. Emily Esther. "Machine Learning Approach for Automatic Detection of Alzheimers Disease using Resting State fMRI." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 30, 2021): 5399–409. http://dx.doi.org/10.22214/ijraset.2021.36170.

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Alzheimer’s Disease (AD) is a neurological disease that affects memory and the livelihood of the people that are diagnosed with it. Efficient automated techniques for early diagnosis of AD is very important because early diagnosis is used to prevent a patient from death. In this work, we present a novel computer-aided diagnosis (CAD) techniques using machine learning algorithms for the early diagnosis of AD. The input resting state fMRI(rsfMRI) images are taken from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The input image is pre-processed using Discrete Wavelet Transform(DWT). Automated thresholding algorithm is used to segment the image. Then, the segmented resting state fMRI images are used to extract useful and informative features. The best features are selected by Fisher’s code feature selection algorithm. Finally, an automated Image classification step is performed using machine learning algorithms Support Vector Machine(SVM), Decision Tree , Random Forest and Multi-Layer Perceptron algorithms to distinguish between normal patients and AD patients.

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Sharma, Ishu, and K. R. Ramkumar. "A survey on ACO based multipath routing algorithms for ad hoc networks." International Journal of Pervasive Computing and Communications 13, no. 4 (November 6, 2017): 370–85. http://dx.doi.org/10.1108/ijpcc-d-17-00015.

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Purpose The purpose of this paper is to review the existing routing algorithms for wireless ad hoc networks to evaluate the methods according to the current requirements. Wireless ad hoc network can provide communication in emergency situations. Active nodes in ad hoc network work as sender, receiver and router at the same time. Nodes are equipped with limited bandwidth and energy. Optimal routing method can help in “smart use” of available resources. Evolvement of routing method based on need is a continuous process. Numerous applications of ad hoc network motivates researcher for further development to fulfill the need of society. Design/methodology/approach In this paper, routing algorithms for ad hoc network based on ant colony optimization, multipath routing and with multiple input multiple output (MIMO) antenna support have been discussed. Findings Comparative analysis has been drawn among existing algorithms of different categories. Transmitting message packets parallel through multiple paths in the network can save overall resource usage in the network. Originality/value The authors have discussed the future area of development for optimal routing in ad hoc networks. Considering signal noise ratio and congestion status while selecting path can provide better energy use in the network. Rather than leaving less prioritized route paths, multiple paths can be used for sending data packets parallel.

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Shadra, Chhaya, James Lin Chen, Cheryl D. Cho-Phan, Aradhana Ghosh, and Jonathan Hirsch. "An algorithmic approach to deriving line of therapy in a real-world data set for non-small cell lung cancer (NSCLC)." Journal of Clinical Oncology 37, no. 15_suppl (May 20, 2019): e18099-e18099. http://dx.doi.org/10.1200/jco.2019.37.15_suppl.e18099.

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e18099 Background: Real World Data (RWD) is being used for outcomes research and regulatory submissions. A key variable needed to understand treatment outcomes is Line of Therapy (LoT). However, LoT is generally not captured in RWD sources such as electronic health records (EHR) or claims data, and is typically derived using manual abstraction. To determine whether an automated approach to LoT derivation is possible, we created an algorithm and applied it to patients (pts) in the Syapse Learning Health Network. Methods: We selected confirmed NSCLC pts from 4 health systems in the RWD set, verifying diagnosis using ICD-9/10/O3 topography and morphology codes. We analyzed the EHR-derived medication list using a regimen-independent algorithm that classified antineoplastic drugs (AD), as defined by ATC L01, into LoT. Within each LoT, we compared the top 80% of AD prescribed (by volume of pts) to the LoT as indicated on each drug’s FDA label. We then used descriptive statistical summaries to outline the alignment between automated algorithmic results and indicated usage within that LoT. Results: In a set of 10,842 NSCLC pts, a total of 106 unique AD were prescribed in the first line as identified by our algorithm, and 13 drugs were prescribed as first line for 80% of the pts. Of those, 9 (69%) of those are indicated for first line, 3 are not indicated for NSCLC, and 1 is indicated for a subsequent NSCLC line, per FDA labels. 82 unique AD were prescribed in the second line as identified by our algorithm, and 15 drugs were prescribed as second line for 80% of the pts. Of those, 11 (73%) are indicated for treatment/continuation therapy for recurrent, advanced or metastatic disease, 3 are not indicated for NSCLC, and 1 is indicated for first line NSCLC per FDA labels. 36 unique AD were prescribed in subsequent line as identified by our algorithm, and 18 drugs were prescribed as subsequent line for 80% of the pts. Of those, 12 (67%) are indicated for treatment of recurrent, advanced or metastatic disease or subsequent systemic therapy, 5 are not indicated for NSCLC and 1 is indicated for first line per FDA labels. Conclusions: An automated algorithmic approach for deriving lines of therapy may be a viable solution to scalably calculate LoT in RWD sets. A deeper analysis using statistical sensitivity and specificity assessment of such algorithms is needed to validate the potential of an algorithmic approach.

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GONTHIER, GEORGES, BETA ZILIANI, ALEKSANDAR NANEVSKI, and DEREK DREYER. "How to make ad hoc proof automation less ad hoc." Journal of Functional Programming 23, no. 4 (July 2013): 357–401. http://dx.doi.org/10.1017/s0956796813000051.

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AbstractMost interactive theorem provers provide support for some form of user-customizable proof automation. In a number of popular systems, such as Coq and Isabelle, this automation is achieved primarily through tactics, which are programmed in a separate language from that of the prover's base logic. While tactics are clearly useful in practice, they can be difficult to maintain and compose because, unlike lemmas, their behavior cannot be specified within the expressive type system of the prover itself.We propose a novel approach to proof automation in Coq that allows the user to specify the behavior of custom automated routines in terms of Coq's own type system. Our approach involves a sophisticated application of Coq's canonical structures, which generalize Haskell type classes and facilitate a flexible style of dependently-typed logic programming. Specifically, just as Haskell type classes are used to infer the canonical implementation of an overloaded term at a given type, canonical structures can be used to infer the canonical proof of an overloaded lemma for a given instantiation of its parameters. We present a series of design patterns for canonical structure programming that enable one to carefully and predictably coax Coq's type inference engine into triggering the execution of user-supplied algorithms during unification, and we illustrate these patterns through several realistic examples drawn from Hoare Type Theory. We assume no prior knowledge of Coq and describe the relevant aspects of Coq type inference from first principles.

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Battineni, Gopi, Mohmmad Amran Hossain, Nalini Chintalapudi, Enea Traini, Venkata Rao Dhulipalla, Mariappan Ramasamy, and Francesco Amenta. "Improved Alzheimer’s Disease Detection by MRI Using Multimodal Machine Learning Algorithms." Diagnostics 11, no. 11 (November 13, 2021): 2103. http://dx.doi.org/10.3390/diagnostics11112103.

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Adult-onset dementia disorders represent a challenge for modern medicine. Alzheimer’s disease (AD) represents the most diffused form of adult-onset dementias. For half a century, the diagnosis of AD was based on clinical and exclusion criteria, with an accuracy of 85%, which did not allow for a definitive diagnosis, which could only be confirmed by post-mortem evaluation. Machine learning research applied to Magnetic Resonance Imaging (MRI) techniques can contribute to a faster diagnosis of AD and may contribute to predicting the evolution of the disease. It was also possible to predict individual dementia of older adults with AD screening data and ML classifiers. To predict the AD subject status, the MRI demographic information and pre-existing conditions of the patient can help to enhance the classifier performance. In this work, we proposed a framework based on supervised learning classifiers in the dementia subject categorization as either AD or non-AD based on longitudinal brain MRI features. Six different supervised classifiers are incorporated for the classification of AD subjects and results mentioned that the gradient boosting algorithm outperforms other models with 97.58% of accuracy.

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Llach, Daniel Cardoso. "Algorithmic Tectonics: How Cold War Era Research Shaped Our Imagination of Design." Architectural Design 83, no. 2 (March 2013): 16–21. http://dx.doi.org/10.1002/ad.1546.

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Ralphs, T. K., L. Ládanyi, and M. J. Saltzman. "A Library Hierarchy for Implementing Scalable Parallel Search Algorithms." Journal of Supercomputing 28, no. 2 (May 2004): 215–34. http://dx.doi.org/10.1023/b:supe.0000020179.55383.ad.

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Genter, Katie, and Peter Stone. "AD HOC TEAMWORK BEHAVIORS FOR INFLUENCING A FLOCK." Acta Polytechnica 56, no. 1 (February 29, 2016): 18. http://dx.doi.org/10.14311/app.2016.56.0018.

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Ad hoc teamwork refers to the challenge of designing agents that can influence the behavior of a team, without prior coordination with its teammates. This paper considers influencing a flock of simple robotic agents to adopt a desired behavior within the context of ad hoc teamwork. Specifically, we examine how the ad hoc agents should behave in order to orient a flock towards a target heading as quickly as possible when given knowledge of, but no direct control over, the behavior of the flock. We introduce three algorithms which the ad hoc agents can use to influence the flock, and we examine the relative importance of coordinating the ad hoc agents versus planning farther ahead when given fixed computational resources. We present detailed experimental results for each of these algorithms, concluding that in this setting, inter-agent coordination and deeper lookahead planning are no more beneficial than short-term lookahead planning.

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A.A. Adas, A. A. Adas. "Simulation of Cryptographic Algorithms in IPSec on Ad-Hoc Networks." journal of King Abdulaziz University Engineering Sciences 28, no. 1 (January 3, 2017): 31–46. http://dx.doi.org/10.4197/eng.28-1.3.

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This paper focuses on secure data communication between nodes in Ad-Hoc networks by employing IPSec (Internet Protocol Security). In wireless communication, Ad-Hoc network is a new paradigm since, which is used for highly sensitive and emergency operations. Ad-Hoc network is considered a number of mobile nodes that are connected through wireless interfaces and moves arbitrarily. Ensuring security is one of the main issues due to its infrastructure less solutions. This research aims for IPSec protocol that provides security for an Ad-Hoc networking in a various applications. IPSec incorporates security model, i.e. AES (Advanced Encryption Standard) into its framework. In this work, we consider the problem of incorporating security mechanisms to securing data communication for Ad-Hoc networks. We look at AODV routing protocol (Ad-Hoc On-Demand Distance Vector) in detail and it is used for secure routing. Simulation of IPSec protocol is simulated using NS-3 simulator. Results from NS-3 simulator is compared with AH, ESP, and AES in terms of Quality of Service parameters throughput, average processing time and average end-to-end delay.

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Chen, Yishun, Weigang Wu, and Hui Cao. "Navigation Route based Stable Connected Dominating Set for Vehicular Ad Hoc Networks." International Journal of Web Services Research 12, no. 1 (January 2015): 12–26. http://dx.doi.org/10.4018/ijwsr.2015010102.

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Forming a virtual backbone has been always a significant technique for ad hoc networks to achieve high efficiency in various applications, including mobility management, broadcast based information dissemination, etc. This is especially true for Vehicular Ad Hoc Network (VANET), because most of VANET applications rely on broadcasting messages. Among others, constructing the Connected Dominating Set (CDS) is a popular approach to forming virtual backbone in ad hoc networks, including VANETs. Although quite a lot of work has been done on CDS algorithms for ad hoc networks, almost all existing algorithms focus on minimizing the size of CDS, i.e. the number of nodes in CDS. In this paper, the authors consider to improve the stability of CDS, i.e. to reduce the changes of CDS during the runtime of a VANET. By making use of vehicle navigation route, the authors propose a new metric to selecting CDS node with high stability. Then, based on the new metric the authors design a CDS algorithm, which can construct a CDS with higher stability than the CDS by existing algorithms. Such advantage is validated via extensive simulations.

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Manaseer, Saher, and Fatima Badwan. "History-Based Backoff Algorithms for Mobile Ad Hoc Networks." Journal of Computer and Communications 04, no. 11 (2016): 37–46. http://dx.doi.org/10.4236/jcc.2016.411004.

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Sharma, Sarvesh Kumar. "An Approach In Optimization Of Ad-Hoc Routing Algorithms." International Journal of Distributed and Parallel systems 3, no. 3 (May 31, 2012): 101–10. http://dx.doi.org/10.5121/ijdps.2012.3310.

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Bacsárdi, László, and Sándor Imre. "Information dissemination algorithms in space deployed ad hoc networks." Periodica Polytechnica Electrical Engineering 55, no. 1-2 (2011): 13. http://dx.doi.org/10.3311/pp.ee.2011-1-2.02.

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